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Ultrasonic biomedical technology; marketing versus clinical reality
Ultrasonics 42 (2004) 17–27 www.elsevier.com/locate/ultras
Ultrasonic biomedical technology; marketing versus clinical reality F. Forsberg
*
Department of Radiology, Division of Diagnostic Ultrasound, Thomas Jefferson University, 132 South 10th Street, Suite 763J, Main Building, Philadelphia, PA 19107, USA
Abstract Clinical ultrasound imaging is the most frequently used imaging modality in the world accounting for almost 25% of all imaging studies performed. Hence, manufacturers of commercial ultrasound equipment are committing significant engineering resources to advance the technology behind the modality and to improve the diagnostic capabilities of ultrasound imaging. Consequently, new imaging technologies are constantly being introduced to the market under a host of different trademarked names. Hence, this paper will review many of the recent advances in ultrasound imaging and try to differentiate between developments aimed at marketing and those providing real clinical improvements. In particular, we will describe the technologies behind concepts such as coded ultrasound imaging, real time spatial compounding, tissue harmonic imaging, extended field of view imaging as well as 3D and 4D (i.e., real time 3D) imaging. Some of the latest advances in blood flow imaging e.g., B-flow, advanced dynamic flow (ADF) and automatic optimization methods will also be described. 2003 Elsevier B.V. All rights reserved. Keywords: Coded excitation; Compound imaging; Harmonic imaging; Photopic imaging; XRES; B-flow; Advanced dynamic flow; Extended field of view imaging; 3D imaging
1. Introduction Clinical ultrasound imaging is the most frequently used imaging modality in the world accounting for almost 25% of all imaging studies performed [1]. Hence, manufacturers of commercial ultrasound equipment are committing significant engineering resources to advance the technology behind the modality and to improve the diagnostic capabilities of ultrasound imaging. Consequently, new imaging technologies are constantly being introduced to the market under a host of different trademarked names. As an example, extended field of view imaging is marketed under at least five different names (Siescape, LOGIQView, FreeStyle extended imaging, ApliClear and Panoramic imaging). Not surprisingly, this can on occasion lead to confusion among end-users. Moreover, manufacturers may be tempted to introduce existing technologies or variations thereof, under new trade-names as a marketing strategy to establish a competitive advantage. Hence, this paper will review many of the recent advances in ultrasound imaging and try to differentiate *
Tel.: +1-215-955-4870; fax: +1-215-955-8549. E-mail address: flemming.forsberg@jefferson.edu (F. Forsberg).
0041-624X/$ - see front matter 2003 Elsevier B.V. All rights reserved. doi:10.1016/j.ultras.2003.12.027
between developments aimed at marketing and those providing real clinical improvements. As a rule of thumb, the more a technology is reported upon in the scientific literature and adopted by multiple manufacturers the more likely it represents a genuine attempt to address clinical needs. This assessment is obviously difficult to make in the early days following the introduction of a new ultrasound imaging technology. More specifically, this paper will describe the technologies behind concepts such as coded ultrasound imaging, real time spatial compounding, tissue harmonic imaging, extended field of view imaging as well as 3D and 4D (i.e., real time 3D) imaging. Some of the latest commercial advances in blood flow imaging e.g., B-flow, advanced dynamic flow (ADF) and automatic optimization methods will also be described.
2. Advances in grayscale imaging techniques 2.1. Transducer technologies The trend for many years has been towards broader bandwidth transducers with more elements, since these will provide superior resolution at multiple depths by
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20 volumes per second; see also Section 5). Clearly, developments such as these that have obvious clinical benefits and are driven by sustained engineering progress over prolonged periods of time will become part of the future clinical reality (no matter what marketing names they may be assigned). 2.2. Coded ultrasound imaging
Fig. 1. Spectra associated with two ultra-broad bandwidth transducers. Center frequencies of 10 MHz (peak 6 MHz; black line) and 15 MHz (peak 7 MHz; red line) with bandwidths of 150% and 173%, respectively, are shown. (Courtesy of Peter Lewin, PhD.)
allowing the best possible compromise between penetration/resolution and attenuation to be made. Examples of the spectra associated with two ultra-broad bandwidth transducers [2] are shown in Fig. 1. Both have bandwidths in excess of 150% with center frequencies around 10–15 MHz. Similar capabilities have been reported in capacitive microfabricated ultrasound transducer (cMUT) probes, which appear poised to become commercially available [3]. For more than a decade one major goal in transducer development has been the construction of a fully electronic 2D array, which would allow for complete beamsteering in three dimensional space (axial, lateral and azimuthal) [4]. Standard linear array transducers contain 192 vertically aligned elements with dynamic phased focusing on receive. By sub-dicing the elements in the horizontal direction a full 2D array would be constructed. Such arrays would be able to acquire full volume data sets with limited angle dependency and focusing in all three dimensions. Moreover, 2D arrays would permit true vector velocity flow imaging, since a volume data set allows flow to be interrogated at multiple angles, which could be combined to determine the true 3D flow vector [4]. The challenge in construction of 2D arrays has always been the large number of elements (192 · 192 ¼ 36,864 elements) and, thus, wires required for operation. While sparse array techniques can be used to reduce the number of elements required, they often suffer from reduced sensitivity as well as marked sidelobe levels. Nonetheless, a full 2D phased array with approximately 2800 elements (the exact configuration is proprietary) was recently introduced (in 2002) by Philips Medical Systems for use in echocardiography. This matrix array has multiplexers built into the probe handle reducing the number of wires needed within the cable and permitting a standard flexible cable configuration to be employed. It can produce real time 3D imaging (over
Another approach to solving the inherent compromise between penetration and resolution required in ultrasound imaging, is the use of coded waveforms. By transmitting more energy in longer coded pulses, improvements in signal-to-noise ratio (SNR) and/or penetration can be achieved. The SNR increase is proportional to the pulse length. However, range side-lobes (i.e., temporal side-lobes due to the autocorrelation function of the coded waveform [5]) are also introduced, which reduces spatial resolution (as does the increased pulse length). Overcoming these drawbacks requires properly designed codes for compressing (decoding) the received signal RðtÞ with a specific matched filter F ðtÞ cross-correlating the received signal to the transmitted signal to regain spatial resolution: X xðtÞ ¼ Rðt þ t0 ÞF ðt0 Þ; ð1Þ t0
where xðtÞ is the final output signal. The matched filter can be shown to be the convolution of the received signal by the time-reversed transmitted pulse [5]. Improvements in SNR in excess of 10 dB have been reported [6,7]. Different types of codes (e.g., Golay or chirps [8]) may be used to reduce side-lobes. Commercial systems that employ coded excitation schemes (based on binary phase or chirp encoding) are now available from two manufacturers (GE Medical Systems and Siemens/Acuson) [5]. An in vitro example obtained in a tissue-mimicking phantom demonstrates the advantages of coded excitation imaging (Fig. 2) compared to conventional ultrasound pulses. The increase in penetration and sensitivity with coded imaging allows two small pseudo-cysts (invisible on conventional sonography) to be seen. A recent evaluation of coded excitation in nine normal volunteers found a statistically significant increase in penetration on the order of 2 cm compared to conventional sonography (p < 0:001) [5]. Image quality was also scored as significantly better over the entire field of view when using codes (p < 0:05). 2.3. Compound imaging Ultrasound imaging is based on transmitting coherent pulses. Hence, sonography suffers from artifacts caused by coherent wave interference known as speckle. Speckle limits low resolution image contrast and may
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Table 1 Speckle reduction methods Image filters (adaptive) Frequency diversity (split-spectrum) Frequency compounding Local recognition and correction (local frequency diversity; zero adjustment procedure) Spatial compounding
lesions shows marked improvement mainly due to the decorrelation between p individual images, which can reffiffiffiffi duce speckle by up to N [10]. Spatial compounding has been adopted most rapidly in breast imaging, where by now it can be considered state-of-the-art ultrasound imaging [11,12]. Piccoli et al. reported on 144 breast patients studied with spatial compounding and conventional sonography and compared speckle, tumor margin and architecture, near field detail, microcalcifications, attenuation and general noise [11]. The images were evaluated randomly and independently by experienced readers. All features, except attenuation, were assessed as significantly better with spatial compounding than with regular ultrasound imaging (p < 0:003). An example of a breast tumor imaged in conventional mode and in spatial compounding mode is presented in Fig. 3. The tumor margins, as well as a central cluster of microcalcifications, are better delineated with compound imaging. Notice also the marked edge enhancement seen in the muscle and tissue layers both anterior and posterior to the tumor. However, the edge shadowing is better visualized with conventional sonography, which is similar to the results reported by Huber et al. [12]. Fig. 2. Tissue-mimicking phantom imaged with (A) conventional ultrasound pulses and (B) coded excitation pulses. Notice, the increase in penetration and sensitivity in (B) compared to (A) allowing two small cysts (arrows) to be seen in the latter case. (Courtesy of GE Medical Systems.)
even obscure true structures in high contrast regions, due to the intensity variations induced by constructive and destructive interference from the tissue echoes. Many different techniques for speckle reduction have been proposed (Table 1). The technique that has received most attention is real time spatial compounding, which by now has been implemented by several manufacturers under a plethora of trademarked names (SonoCT, SieClear, FreeStyle and ApliPure). Compound imaging acquires N different scans (up to 9 in practice) of an object at different angles of insonation across the aperture of an electronic array. By averaging these independent scans a real time multi-angle compound image is assembled [9]. The detectability of low-contrast
2.4. Tissue harmonic imaging Second harmonic imaging was originally developed for ultrasound contrast agent imaging utilizing the ability of microbubbles to oscillate nonlinearly in the ultrasound field to form ‘‘microbubble only’’ images [13]. Further improvements are possible with the use of pulse inversion harmonic imaging (PI-HI) [14,15]. This technique cancels first (and other odd) harmonic signals by transmitting a pulse sequence where each pulse is an inverted copy of the previous pulse, and then summing the echoes from subsequent pulses (resulting in zero under linear scattering conditions). Hence, echoes from stationary tissue will be suppressed. However, nonlinear echoes will not cancel out and, thus, can be preferentially detected and displayed. While tissue is much less nonlinear than contrast microbubbles it still generates sufficient nonlinear signal components to form images. These signals arise because the speed of sound c can no longer be considered a
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Fig. 4. The generation of nonlinear signal components in tissue as a function of depth (rows represent consecutively deeper locations). The distortion of the ultrasound pulse (left column) and the corresponding increase in higher harmonic spectral components (right column) are shown.
these originate in superficial tissue structures where the harmonic signals have not yet developed. The improved resolution (finer speckle pattern) and depiction of vessel margins is demonstrated in Fig. 5, which shows a normal liver.
Fig. 3. Breast carcinoma (arrowheads) imaged (A) in conventional mode and (B) in spatial compounding mode. The tumor margins as well as the central cluster of microcalcifications (arrows) are better delineated with compounding (unlike the edge shadowing).
constant [16]. Rather it is a function of the particle velocity u and the coefficient of nonlinearity b according to c ¼ c0 þ bu:
ð2Þ
As the acoustic pressure wave travels deeper and deeper into the tissue the high pressure regions travel faster through the tissue (and conversely for the low pressure areas) resulting in the generation of nonlinear frequency components (Fig. 4). This imaging mode is known as tissue harmonic imaging (THI) or native tissue harmonics or simply harmonics (different from contrast harmonics mainly in its filter settings, which are optimized for the much weaker tissue signal components). Every manufacturer by now has THI on most if not all probes and systems. The advantages of THI include a narrower main lobe (i.e., improved axial and lateral resolution) as well as better side-lobe suppression [17]. In essence, THI is a sophisticated form of beam forming. Moreover, the depth-dependency of the harmonic signals in tissue results in reduced clutter and reverberation artifacts, since
Fig. 5. Normal liver imaged (A) in conventional mode and (B) in THI mode. A finer speckle pattern and improved depiction of the margins of the hepatic vein can be seen in (B).
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Table 2 Automatic optimization parameters Analyze
Optimize
Gray intensity distribution Gray pixel groups Color intensity distribution Spectrum and noise levels Vessel/color edge data
Image histogram or photopic XRES Color contrast and threshold PRF and display parameters PW Doppler angle
proved ability of the human eye to separate colors (compared to grayscale levels) by using an adaptive contrast optimization scheme. The algorithm not only expands the grayscale dynamic range, but also applies monochromatic color to stimulate photopic vision. Fig. 7 shows a muscoloskeletal example of a patient with calcific tendonitis of the rotator cuff imaged in both conventional grayscale and photopic modes. An area of thickening of the tendon with high level echoes from the calcifications can be observed in both images. There is also a hypoechoic area on the right representing anisotropy. A rigorous study of the usefulness of the photopic mode in muscoloskeletal imaging was
Fig. 6. Transverse grayscale imaging with spatial compounding of the lower pole of the right kidney (A) showing either a solid lesion or a complex cyst with low level echoes. When THI and spatial compounding are combined (B) the lesion turns out to be a simple renal cyst with septations.
The combination of THI with other methods e.g., spatial compounding can be synergistic. A renal scan of a patient found what appeared to be a solid lesion (Fig. 6A). Even with spatial compounding added there was still uncertainty as to whether the patient had a solid lesion or a complex cyst with low level echoes. However, when THI and spatial compounding were combined (Fig. 6B) the lesion turned out to be a simple renal cyst and, thus, of minimal clinical concern. 2.5. Photopic imaging Unlike the techniques described above, which operate either on the transmit side or directly upon reception, photopic imaging can be considered an automatic optimization method (Table 2) in the sense that it is an image processing technique applied immediately before the display. Histogram equalization which expands the grayscale levels of the received signals to fit the dynamic range available on the scanner have been available for a while. Photopic imaging (by Siemens Medical Systems) is an expansion of this concept, which relies on the im-
Fig. 7. Calcific tendonitis (arrow) of the rotator cuff imaged in conventional grayscale (A) demonstrating as an area of thickening of the tendon. The same image in photopic mode with a monochromatic color scale applied (B). There is anisotropy on the right demonstrating as a hypoechoic area.
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performed by Nallamshetty et al. [18]. Grayscale and photopic images were obtained of 68 patients with muscoloskeletal complications. The grayscale images were also imported into Photoshop (Adobe Systems, San Jose, CA) and colorized using a palette similar to photopic imaging (but without adaptive contrast optimization). The images (204 in total) were evaluated in random order by three independent and blinded reviewers for detail resolution, noise levels, definition of tissue planes, diagnostic confidence and overall image impression (scale: 1–10). An ANOVA analysis showed no significant difference between the three types of ultrasound images. However, a recent study from Germany looking at the value of photopic imaging as an adjunct to THI in 58 patients (abdominal or otolaryngological studies) was more encouraging [19]. The combination of THI and photopic was found to improve image quality compared to conventional ultrasound imaging (p < 0:05). No colorized grayscale images were evaluated in this study. 2.6. XRES Adaptive image filtering for artifact suppression and tissue structure enhancement has been pursued for decades in medical ultrasound. Stetson et al. developed an adaptive grayscale mapping algorithm for tissue contrast enhancement, which uses pre-selected images of similar targets for optimization [20]. Several manufacturers have installed similar algorithms on their units. Recently, Philips Medical Systems introduced XRES, which is a real-time adaptive grayscale image enhancement algorithm [21]. As such, it is an automatic optimization method (Table 2) and it is based on earlier work in speckle reduction and computer vision [20,22]. Details on the actual processing performed with XRES are sparse, but it is known to be a nonlinear, adaptive 1D filter, which is applied locally in all directions. XRES estimates the magnitude of the local gradient (in a number of directions) and uses the status of this to determine the processing employed. For example, smoothing is applied parallel to interfaces (to improve continuity) while edge enhancement is employed in the perpendicular direction [21]. Moreover, XRES works as a multi-resolution algorithm in the sense that spatial frequency sub-bands are generated from down-sampled images and processed as representing different scales. XRES is being marketed as an adjunct technique to spatial compounding (i.e., SonoCT for this particular manufacturer) and indeed the only clinical evaluation published to date on XRES used the two techniques in conjunction [23]. Improvements in image quality were reported. As an example conventional grayscale imaging of the left lobe of the thyroid and the same image with XRES
applied are presented in Fig. 8A and B. Some edge enhancement of the muscle layers above the thyroid can be seen when XRES is turned on. For comparison, thyroid images with spatial compounding (Fig. 8C) and with both spatial compounding and XRES employed (Fig. 8D) are also shown. Speckle patterns have changed markedly from Fig. 8C and D relative to Fig. 8A and B.
3. Advances in flow imaging techniques As all diagnostic imaging modalities move from anatomical depiction (as in grayscale mode) towards functional imaging the importance of ultrasonic blood flow imaging increases. 3.1. Automatic optimization techniques Manufacturers have recognized that Doppler flow measurements are among the most complicated studies performed with ultrasound requiring extensive user optimization to produce diagnostic data. Hence, a number of automatic optimization techniques for flow imaging have been introduced (Table 2). By analyzing spectral contents and noise it is possible, with a single button push, to adjust spectral Doppler waveforms to optimize gain, baseline, dynamic range and pulse-repetition frequency (PRF). It is even possible to automatically adjust the beam-to-vessel angle. This is achieved by searching from the sample volume and out in both directions for bright interfaces and the edge of the color data. These are assumed to locally represent the morphology of the blood vessel wall and, thus, the angle of insonation can be estimated. Likewise, internal color contrast and threshold parameters can be modified to maximize the dynamic range displayed. While these concepts are intriguing all of the automatic optimization methods work best with no or low level noise, which are not circumstances one encounters often in clinical practice. 3.2. B-flow imaging It is possible to directly visualize groups of moving red blood cells within the blood using grayscale sonography by extending the coded excitation schemes developed by GE Medical Systems. This method is known as B-flow and it provides higher frame rates than conventional color Doppler and high dynamic range flow imaging at markedly improved spatial and temporal resolution [24]. Moreover, irrespective of the velocity, flow in both arteries and veins can be visualized with B-flow (unlike conventional scanners that only depict red blood cells in veins with slow flow). An equalization filter is used to subtract a fraction of the received signal from the previous A-line to suppress clutter and slow
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Fig. 8. Left lobe of the thyroid (T) and the carotid artery (CA) imaged in conventional grayscale mode (A) and with XRES applied (B). Notice the edge enhancement in the muscle layers above the thyroid. Thyroid imaged with spatial compounding (C) and with both spatial compounding and XRES employed (D). Speckle patterns have changed markedly from (C) and (D) relative to (A) and (B).
moving signal components, while retaining the faster moving blood echoes. More importantly, no operator optimization is required and the velocity and angle independence eliminates Doppler artifacts. However, no quantitative data can currently be derived from B-flow data (although B-flow, like color Doppler imaging, can obviously be used to guide placement of a spectral Doppler sample volume). An example of color Doppler and B-flow imaging of the carotid artery is presented in Fig. 9. The region of elevated velocities on the left in the vessel appears as yellow/blue patterns in color Doppler mode and as brighter grayscale echoes in B-flow mode. Notice, the fine depiction of the vessel lumen achievable with Bflow. Mixed results have been reported with B-flow to date. In a recent study by Garra et al. duplex Doppler and
B-flow assessment of carotid stenoses were performed in 35 patients (40 vessels) and compared to angiography or MRA [25]. In 18 carotid arteries agreement was within ±5% and in the remaining 22 within ±10%. The conclusion was that B-flow was better than duplex Doppler for identifying low grade stenoses (<40%). However, such low grade stenoses are not clinically significant. Somewhat disappointing results were also reported by Bucek et al. in their evaluation of 28 patients with carotid stenoses [26]. They found no correlation between Bflow, color Doppler and angiographic parameters for assessing internal carotid stenoses (p > 0:05). Conversely, Umemura and Yamada found B-flow to be highly effective in visualizing flow and detecting stenosis in 60 patients with ischemic cerebrovascular disease [27]. They described excellent correlation between digital subtraction angiography (DSA) and B-flow assessments
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Fig. 9. Flow in the carotid artery depicted in (A) color Doppler mode and (B) B-flow. The region of elevated velocities on the left in the vessel shows as yellow/blue patterns in (A) and as brighter grayscale echoes in (B).
of degree of stenosis (p < 0:0001) in a sub-set of 15 patients. 3.3. Advanced dynamic flow The velocity estimation technique used in most color Doppler systems is based on calculating the complex phase of the autocorrelation function [28]. Alternative estimators employing wideband techniques have also been developed, which assumes that each group of scatterers produces a unique echo-pattern that can be tracked over time and distance (i.e., a form of speckle tracking to locate the movements of a specific group of red blood cells) [28]. One particular implementation is advanced dynamic flow (ADF by Toshiba Medical Systems), which relies on wide bandwidth transmit pulses (i.e., short pulses similar to B-mode ultrasound) for improved spatial resolution and reduced estimator variance [29]. To regain the loss in sensitivity, associated with the reduced acoustic energy in a shorter pulse, ADF uses a receive frequency swept according to depth followed by waveform shaping using a filter. This method is typically employed for grayscale processing, but can be used in this context because ADF is
Fig. 10. The left internal carotid artery in a patient with a low grade stenosis depicted in (A) color Doppler mode and (B) ADF mode. Notice the improved visualization of the plaque surface/functional lumen interface seen with ADF. (Courtesy of Toshiba America Medical Systems.)
a power Doppler technique (i.e., velocity data is not required). The left internal carotid artery in a patient with a low grade stenosis is depicted in Fig. 10 in color Doppler and in ADF modes (albeit from slightly different scan planes). ADF is a variation of directional power Doppler as can be seen by the color bar (on the left in Fig. 10B). Also notice the improved visualization of the plaque surface/functional lumen interface seen with ADF, due to the improvement in sensitivity and vessel wall depiction obtained with this technique. ADF has been reported to be particularly useful in fetal vascular imaging [30].
4. Advances in volume imaging techniques A major drawback in the clinical utility of conventional ultrasound imaging has been the limited field of view defined by the aperture of the transducer. While the free hand movement of the probe provides much flexibility, it is often difficult to acquire major anatomical landmarks as well as the target structure on a single image. Overcoming this limitation to obtain true volume
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data has long been a goal for engineers and clinicians alike [31]. 4.1. Extended field of view imaging One recent development is called extended field of view imaging (often abbreviated XFOV) and provides a first step towards volume data acquisition [32]. This method uses a 2D tracking algorithm applied to image features across multiple images (in grayscale or power Doppler mode) obtained in a steady sweep. By matching the same features across images, an amalgamated image of a complete tissue structure can be produced to provide a more global perspective. Extended field of view imaging is marketed by the major manufacturers under at least five different names (Siescape, LOGIQView, FreeStyle extended imaging, ApliClear and Panoramic imaging), which indicates that it fulfills a real clinical need––albeit with a lot name confusion added for users. To date extended field of view imaging has been utilized for evaluating both lobes of the thyroid simultaneously as well as for muscoloskeletal applications [33]. Fig. 11 is an example of a patient who presented with a marked superficial swelling of unknown origin. Con-
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ventional sonography (Fig. 11A and B) was inconclusive, but the extended field of view image revealed a very large lipoma almost 35 · 65 mm in diameter (Fig. 11C). This finding was confirmed by CT scan (Fig. 11D). 4.2. Three dimensional imaging Due to the real time (or at least near real time) requirements of ultrasound imaging the time available for acquisition and processing of a 3D volume of data is very limited. This is particularly true when blood flow data is acquired with Doppler ultrasound using multiple firing techniques. Producing an accurate 3D ultrasound image relies critically on the data acquisition technique, and on exact definition of the 3D volume. In a static image collection modality with fixed slice thickness, such as CT or MRI, the geometry of the 3D volume is given, but in ultrasound a different approach has to be taken. To date, most manufacturers have relied on complete free-hand acquisition due to the relative ease of implementation and the use of standard 2D probes. This is akin to XFOV, but requires feature tracking in three directions. The 3D volume can also be specified by placing sensors on a conventional transducer and then
Fig. 11. Large superficial area of swelling shown with conventional sonography (A) and (B). Some vague outline of a border (arrowheads) can be seen in (B). Extended field of view image (C) revealed a very large lipoma greater then 6.0 cm in diameter (see measurements). This finding (arrows) was confirmed by CT (D).
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tracking its location in space via remote localizers or by mounting the transducer in a mechanical arm to move the probe using stepping motors under automatic control. A more elegant approach is afforded by 2D electronic arrays, which permit focusing in the elevation plane as well as true angle-independent flow measurements [4,31]. Commercial 2D arrays are finally emerging and probably represent the ultimate technique for real time 3D (sometimes referred to as 4D) ultrasound data acquisition. At the June, 2003 meeting of the American Society of Echocardiography Philips Medical Systems presented the first commercial 2800 element 2D phased array with real time 3D color Doppler imaging capabilities. Three-dimensional grayscale ultrasound imaging has already become a clinically useful tool in obstetrics; in particular for diagnosing facial abnormalities. An example of surface rendering of a fetal face is presented in Fig. 12, where a normal pregnancy at 31 weeks gestation was sent for 3D imaging to rule out cleft lip (suspected in an initial 2D examination). The upper lip was well visualized and appeared completely normal, thus excluding the possibility of a cleft lip. Furthermore, some structures (e.g., the fetal spine) can be difficult to visualize in completeness with conventional 2D ultrasound. Here the ability of 3D imaging to display planes otherwise not seen with 2D imaging (e.g., orthogonal to the transducer) is proving advantageous. The clinical applications of vascular 3D ultrasound imaging include the assessment of blood flow in the kidney, placenta, prostate and carotid artery [34,35]. The benefit is being able to localize a functional abnormality relative to the underlying anatomy. However, vascular 3D imaging is not yet used routinely in the clinic (although this may well change in the future given the arrival of 2D arrays with flow imaging capabilities).
5. Conclusions A review of many of the recent advances in grayscale ultrasound imaging (2D arrays, coded excitation imaging, spatial compounding, tissue harmonic imaging, photopic imaging, XRES and XFOV), flow imaging (optimization methods, B-flow and ADF) and 3D volume imaging has been given. In order to differentiate between developments aimed at marketing and those providing real clinical improvements it is important to recall that the more a technology is reported upon in the scientific literature and adopted by multiple manufacturers the more likely it represents a genuine attempt to address clinical needs. In the early days following the introduction of a unique new ultrasound imaging technology, such collaborative information may well be unavailable. Under such circumstances, users should adopt the common sense axiom that the more a technology is based on acoustical physics the more likely it is to work. Having made this determination, operators should strive for maximum exposure to the new technology (preferably during scanning of patients). The recent history of technical developments in ultrasound is characterized by the extraordinary number and diversity of new technologies being offered by manufacturers. All new ultrasound imaging tools will have strengths and weaknesses, but technologies that provide real clinical benefits (alone or in combination with other methods) will ultimately be successful.
Acknowledgements The advice and assistance of G. Bega, MD, B.B. Goldberg, MD, E.J. Halpern, MD, P. Lewin, PhD, J.B. Liu, MD, C.R.B. Merritt, MD, D.A. Merton, BS, RDMS, L. Needleman, MD, P. O’Kane, MD, N.M. Rawool, MD, W.T. Shi, PhD, L.D. Waldroup, BS, RDMS is gratefully acknowledged.
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Fig. 12. 3D grayscale imaging of a 31 week fetus referred for suspected cleft lip after a conventional 2D scan. Clearly, no cleft lip is present.
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[20] P. Stetson, F. Sommer, A. Macovski, Lesion contrast enhancement in medical ultrasound imaging, IEEE Trans. Med. Imag. 16 (1997) 416. [21] J. Jago, A. Collet-Billon, C. Chenal, J.-M. Jong, S. Makram-Ebid, XRES: adaptive enhancement of ultrasound images, MedicaMundi 46 (2002) 36. [22] G. Granlund, H. Knutsson, Signal Processing for Computer Vision, Kluwer Academic Publishers, Boston, MA, 1994. [23] J.Y. Meuwly, J.P. Thiran, F. Gudinchet, Application of adaptive image processing technique to real-time spatial compound ultrasound imaging improves image quality, Invest. Radiol. 38 (2003) 257. [24] J. Furuse, Y. Maru, K. Mera, et al., Visualization of blood flow in hepatic vessels and hepatocellular carcinoma using B-flow sonography, J. Clin. Ultrasound 29 (2001) 1. [25] B.S. Garra, G. Alsofrom, P. Kelly, M. Pinet, S. Knight, K. Cawley, B-flow ultrasound: can it replace carotid Doppler? J. Ultrasound Med. 21 (2002) S46. [26] R.A. Bucek, M. Reiter, I. Koppensteiner, R. Ahmadi, E. Minar, J. Lammer, B-flow evaluation of carotid arterial stenosis: initial experience, Radiology 225 (2002) 295. [27] A. Umemura, K. Yamada, B-mode flow imaging of the carotid artery, Stroke 32 (2001) 2055. [28] K. Ferrara, G. DeAngelis, Color flow mapping, Ultrasound Med. Biol. 23 (1997) 321. [29] T. Sato, Advanced dynamic flow, Igaku Butsuri 21 (2001) 142. [30] S. Miyashita, Efficacy of dynamic flow ultrasonography in fetal vascular imaging, Med. Rev. 27 (2003) 1. [31] J.T. Yen, S.W. Smith, Real-time rectilinear volumetric imaging, IEEE Trans. Ultrason. Ferroelectr. Freq. Control 49 (2002) 114. [32] L. Weng, A.P. Tirumalai, C.M. Lowery, et al., US extendedfield-of-view imaging technology, Radiology 203 (1997) 877. [33] C.M. Sofka, R.S. Adler, Sonographic evaluation of shoulder arthroplasty, Am. J. Roentgenol. 180 (2003) 1117. [34] A. Fenster, D.B. Downey, 3-D ultrasound imaging: a review, IEEE Eng. Med. Biol. Mag. 15 (1996) 41. [35] T.R. Nelson, D.H. Pretorius, Three-dimensional ultrasound imaging, Ultrasound Med. Biol. 24 (1998) 1243.
Ultrasonic biomedical technology; marketing versus clinical reality F. Forsberg
*
Department of Radiology, Division of Diagnostic Ultrasound, Thomas Jefferson University, 132 South 10th Street, Suite 763J, Main Building, Philadelphia, PA 19107, USA
Abstract Clinical ultrasound imaging is the most frequently used imaging modality in the world accounting for almost 25% of all imaging studies performed. Hence, manufacturers of commercial ultrasound equipment are committing significant engineering resources to advance the technology behind the modality and to improve the diagnostic capabilities of ultrasound imaging. Consequently, new imaging technologies are constantly being introduced to the market under a host of different trademarked names. Hence, this paper will review many of the recent advances in ultrasound imaging and try to differentiate between developments aimed at marketing and those providing real clinical improvements. In particular, we will describe the technologies behind concepts such as coded ultrasound imaging, real time spatial compounding, tissue harmonic imaging, extended field of view imaging as well as 3D and 4D (i.e., real time 3D) imaging. Some of the latest advances in blood flow imaging e.g., B-flow, advanced dynamic flow (ADF) and automatic optimization methods will also be described. 2003 Elsevier B.V. All rights reserved. Keywords: Coded excitation; Compound imaging; Harmonic imaging; Photopic imaging; XRES; B-flow; Advanced dynamic flow; Extended field of view imaging; 3D imaging
1. Introduction Clinical ultrasound imaging is the most frequently used imaging modality in the world accounting for almost 25% of all imaging studies performed [1]. Hence, manufacturers of commercial ultrasound equipment are committing significant engineering resources to advance the technology behind the modality and to improve the diagnostic capabilities of ultrasound imaging. Consequently, new imaging technologies are constantly being introduced to the market under a host of different trademarked names. As an example, extended field of view imaging is marketed under at least five different names (Siescape, LOGIQView, FreeStyle extended imaging, ApliClear and Panoramic imaging). Not surprisingly, this can on occasion lead to confusion among end-users. Moreover, manufacturers may be tempted to introduce existing technologies or variations thereof, under new trade-names as a marketing strategy to establish a competitive advantage. Hence, this paper will review many of the recent advances in ultrasound imaging and try to differentiate *
Tel.: +1-215-955-4870; fax: +1-215-955-8549. E-mail address: flemming.forsberg@jefferson.edu (F. Forsberg).
0041-624X/$ - see front matter 2003 Elsevier B.V. All rights reserved. doi:10.1016/j.ultras.2003.12.027
between developments aimed at marketing and those providing real clinical improvements. As a rule of thumb, the more a technology is reported upon in the scientific literature and adopted by multiple manufacturers the more likely it represents a genuine attempt to address clinical needs. This assessment is obviously difficult to make in the early days following the introduction of a new ultrasound imaging technology. More specifically, this paper will describe the technologies behind concepts such as coded ultrasound imaging, real time spatial compounding, tissue harmonic imaging, extended field of view imaging as well as 3D and 4D (i.e., real time 3D) imaging. Some of the latest commercial advances in blood flow imaging e.g., B-flow, advanced dynamic flow (ADF) and automatic optimization methods will also be described.
2. Advances in grayscale imaging techniques 2.1. Transducer technologies The trend for many years has been towards broader bandwidth transducers with more elements, since these will provide superior resolution at multiple depths by
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20 volumes per second; see also Section 5). Clearly, developments such as these that have obvious clinical benefits and are driven by sustained engineering progress over prolonged periods of time will become part of the future clinical reality (no matter what marketing names they may be assigned). 2.2. Coded ultrasound imaging
Fig. 1. Spectra associated with two ultra-broad bandwidth transducers. Center frequencies of 10 MHz (peak 6 MHz; black line) and 15 MHz (peak 7 MHz; red line) with bandwidths of 150% and 173%, respectively, are shown. (Courtesy of Peter Lewin, PhD.)
allowing the best possible compromise between penetration/resolution and attenuation to be made. Examples of the spectra associated with two ultra-broad bandwidth transducers [2] are shown in Fig. 1. Both have bandwidths in excess of 150% with center frequencies around 10–15 MHz. Similar capabilities have been reported in capacitive microfabricated ultrasound transducer (cMUT) probes, which appear poised to become commercially available [3]. For more than a decade one major goal in transducer development has been the construction of a fully electronic 2D array, which would allow for complete beamsteering in three dimensional space (axial, lateral and azimuthal) [4]. Standard linear array transducers contain 192 vertically aligned elements with dynamic phased focusing on receive. By sub-dicing the elements in the horizontal direction a full 2D array would be constructed. Such arrays would be able to acquire full volume data sets with limited angle dependency and focusing in all three dimensions. Moreover, 2D arrays would permit true vector velocity flow imaging, since a volume data set allows flow to be interrogated at multiple angles, which could be combined to determine the true 3D flow vector [4]. The challenge in construction of 2D arrays has always been the large number of elements (192 · 192 ¼ 36,864 elements) and, thus, wires required for operation. While sparse array techniques can be used to reduce the number of elements required, they often suffer from reduced sensitivity as well as marked sidelobe levels. Nonetheless, a full 2D phased array with approximately 2800 elements (the exact configuration is proprietary) was recently introduced (in 2002) by Philips Medical Systems for use in echocardiography. This matrix array has multiplexers built into the probe handle reducing the number of wires needed within the cable and permitting a standard flexible cable configuration to be employed. It can produce real time 3D imaging (over
Another approach to solving the inherent compromise between penetration and resolution required in ultrasound imaging, is the use of coded waveforms. By transmitting more energy in longer coded pulses, improvements in signal-to-noise ratio (SNR) and/or penetration can be achieved. The SNR increase is proportional to the pulse length. However, range side-lobes (i.e., temporal side-lobes due to the autocorrelation function of the coded waveform [5]) are also introduced, which reduces spatial resolution (as does the increased pulse length). Overcoming these drawbacks requires properly designed codes for compressing (decoding) the received signal RðtÞ with a specific matched filter F ðtÞ cross-correlating the received signal to the transmitted signal to regain spatial resolution: X xðtÞ ¼ Rðt þ t0 ÞF ðt0 Þ; ð1Þ t0
where xðtÞ is the final output signal. The matched filter can be shown to be the convolution of the received signal by the time-reversed transmitted pulse [5]. Improvements in SNR in excess of 10 dB have been reported [6,7]. Different types of codes (e.g., Golay or chirps [8]) may be used to reduce side-lobes. Commercial systems that employ coded excitation schemes (based on binary phase or chirp encoding) are now available from two manufacturers (GE Medical Systems and Siemens/Acuson) [5]. An in vitro example obtained in a tissue-mimicking phantom demonstrates the advantages of coded excitation imaging (Fig. 2) compared to conventional ultrasound pulses. The increase in penetration and sensitivity with coded imaging allows two small pseudo-cysts (invisible on conventional sonography) to be seen. A recent evaluation of coded excitation in nine normal volunteers found a statistically significant increase in penetration on the order of 2 cm compared to conventional sonography (p < 0:001) [5]. Image quality was also scored as significantly better over the entire field of view when using codes (p < 0:05). 2.3. Compound imaging Ultrasound imaging is based on transmitting coherent pulses. Hence, sonography suffers from artifacts caused by coherent wave interference known as speckle. Speckle limits low resolution image contrast and may
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Table 1 Speckle reduction methods Image filters (adaptive) Frequency diversity (split-spectrum) Frequency compounding Local recognition and correction (local frequency diversity; zero adjustment procedure) Spatial compounding
lesions shows marked improvement mainly due to the decorrelation between p individual images, which can reffiffiffiffi duce speckle by up to N [10]. Spatial compounding has been adopted most rapidly in breast imaging, where by now it can be considered state-of-the-art ultrasound imaging [11,12]. Piccoli et al. reported on 144 breast patients studied with spatial compounding and conventional sonography and compared speckle, tumor margin and architecture, near field detail, microcalcifications, attenuation and general noise [11]. The images were evaluated randomly and independently by experienced readers. All features, except attenuation, were assessed as significantly better with spatial compounding than with regular ultrasound imaging (p < 0:003). An example of a breast tumor imaged in conventional mode and in spatial compounding mode is presented in Fig. 3. The tumor margins, as well as a central cluster of microcalcifications, are better delineated with compound imaging. Notice also the marked edge enhancement seen in the muscle and tissue layers both anterior and posterior to the tumor. However, the edge shadowing is better visualized with conventional sonography, which is similar to the results reported by Huber et al. [12]. Fig. 2. Tissue-mimicking phantom imaged with (A) conventional ultrasound pulses and (B) coded excitation pulses. Notice, the increase in penetration and sensitivity in (B) compared to (A) allowing two small cysts (arrows) to be seen in the latter case. (Courtesy of GE Medical Systems.)
even obscure true structures in high contrast regions, due to the intensity variations induced by constructive and destructive interference from the tissue echoes. Many different techniques for speckle reduction have been proposed (Table 1). The technique that has received most attention is real time spatial compounding, which by now has been implemented by several manufacturers under a plethora of trademarked names (SonoCT, SieClear, FreeStyle and ApliPure). Compound imaging acquires N different scans (up to 9 in practice) of an object at different angles of insonation across the aperture of an electronic array. By averaging these independent scans a real time multi-angle compound image is assembled [9]. The detectability of low-contrast
2.4. Tissue harmonic imaging Second harmonic imaging was originally developed for ultrasound contrast agent imaging utilizing the ability of microbubbles to oscillate nonlinearly in the ultrasound field to form ‘‘microbubble only’’ images [13]. Further improvements are possible with the use of pulse inversion harmonic imaging (PI-HI) [14,15]. This technique cancels first (and other odd) harmonic signals by transmitting a pulse sequence where each pulse is an inverted copy of the previous pulse, and then summing the echoes from subsequent pulses (resulting in zero under linear scattering conditions). Hence, echoes from stationary tissue will be suppressed. However, nonlinear echoes will not cancel out and, thus, can be preferentially detected and displayed. While tissue is much less nonlinear than contrast microbubbles it still generates sufficient nonlinear signal components to form images. These signals arise because the speed of sound c can no longer be considered a
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Fig. 4. The generation of nonlinear signal components in tissue as a function of depth (rows represent consecutively deeper locations). The distortion of the ultrasound pulse (left column) and the corresponding increase in higher harmonic spectral components (right column) are shown.
these originate in superficial tissue structures where the harmonic signals have not yet developed. The improved resolution (finer speckle pattern) and depiction of vessel margins is demonstrated in Fig. 5, which shows a normal liver.
Fig. 3. Breast carcinoma (arrowheads) imaged (A) in conventional mode and (B) in spatial compounding mode. The tumor margins as well as the central cluster of microcalcifications (arrows) are better delineated with compounding (unlike the edge shadowing).
constant [16]. Rather it is a function of the particle velocity u and the coefficient of nonlinearity b according to c ¼ c0 þ bu:
ð2Þ
As the acoustic pressure wave travels deeper and deeper into the tissue the high pressure regions travel faster through the tissue (and conversely for the low pressure areas) resulting in the generation of nonlinear frequency components (Fig. 4). This imaging mode is known as tissue harmonic imaging (THI) or native tissue harmonics or simply harmonics (different from contrast harmonics mainly in its filter settings, which are optimized for the much weaker tissue signal components). Every manufacturer by now has THI on most if not all probes and systems. The advantages of THI include a narrower main lobe (i.e., improved axial and lateral resolution) as well as better side-lobe suppression [17]. In essence, THI is a sophisticated form of beam forming. Moreover, the depth-dependency of the harmonic signals in tissue results in reduced clutter and reverberation artifacts, since
Fig. 5. Normal liver imaged (A) in conventional mode and (B) in THI mode. A finer speckle pattern and improved depiction of the margins of the hepatic vein can be seen in (B).
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Table 2 Automatic optimization parameters Analyze
Optimize
Gray intensity distribution Gray pixel groups Color intensity distribution Spectrum and noise levels Vessel/color edge data
Image histogram or photopic XRES Color contrast and threshold PRF and display parameters PW Doppler angle
proved ability of the human eye to separate colors (compared to grayscale levels) by using an adaptive contrast optimization scheme. The algorithm not only expands the grayscale dynamic range, but also applies monochromatic color to stimulate photopic vision. Fig. 7 shows a muscoloskeletal example of a patient with calcific tendonitis of the rotator cuff imaged in both conventional grayscale and photopic modes. An area of thickening of the tendon with high level echoes from the calcifications can be observed in both images. There is also a hypoechoic area on the right representing anisotropy. A rigorous study of the usefulness of the photopic mode in muscoloskeletal imaging was
Fig. 6. Transverse grayscale imaging with spatial compounding of the lower pole of the right kidney (A) showing either a solid lesion or a complex cyst with low level echoes. When THI and spatial compounding are combined (B) the lesion turns out to be a simple renal cyst with septations.
The combination of THI with other methods e.g., spatial compounding can be synergistic. A renal scan of a patient found what appeared to be a solid lesion (Fig. 6A). Even with spatial compounding added there was still uncertainty as to whether the patient had a solid lesion or a complex cyst with low level echoes. However, when THI and spatial compounding were combined (Fig. 6B) the lesion turned out to be a simple renal cyst and, thus, of minimal clinical concern. 2.5. Photopic imaging Unlike the techniques described above, which operate either on the transmit side or directly upon reception, photopic imaging can be considered an automatic optimization method (Table 2) in the sense that it is an image processing technique applied immediately before the display. Histogram equalization which expands the grayscale levels of the received signals to fit the dynamic range available on the scanner have been available for a while. Photopic imaging (by Siemens Medical Systems) is an expansion of this concept, which relies on the im-
Fig. 7. Calcific tendonitis (arrow) of the rotator cuff imaged in conventional grayscale (A) demonstrating as an area of thickening of the tendon. The same image in photopic mode with a monochromatic color scale applied (B). There is anisotropy on the right demonstrating as a hypoechoic area.
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performed by Nallamshetty et al. [18]. Grayscale and photopic images were obtained of 68 patients with muscoloskeletal complications. The grayscale images were also imported into Photoshop (Adobe Systems, San Jose, CA) and colorized using a palette similar to photopic imaging (but without adaptive contrast optimization). The images (204 in total) were evaluated in random order by three independent and blinded reviewers for detail resolution, noise levels, definition of tissue planes, diagnostic confidence and overall image impression (scale: 1–10). An ANOVA analysis showed no significant difference between the three types of ultrasound images. However, a recent study from Germany looking at the value of photopic imaging as an adjunct to THI in 58 patients (abdominal or otolaryngological studies) was more encouraging [19]. The combination of THI and photopic was found to improve image quality compared to conventional ultrasound imaging (p < 0:05). No colorized grayscale images were evaluated in this study. 2.6. XRES Adaptive image filtering for artifact suppression and tissue structure enhancement has been pursued for decades in medical ultrasound. Stetson et al. developed an adaptive grayscale mapping algorithm for tissue contrast enhancement, which uses pre-selected images of similar targets for optimization [20]. Several manufacturers have installed similar algorithms on their units. Recently, Philips Medical Systems introduced XRES, which is a real-time adaptive grayscale image enhancement algorithm [21]. As such, it is an automatic optimization method (Table 2) and it is based on earlier work in speckle reduction and computer vision [20,22]. Details on the actual processing performed with XRES are sparse, but it is known to be a nonlinear, adaptive 1D filter, which is applied locally in all directions. XRES estimates the magnitude of the local gradient (in a number of directions) and uses the status of this to determine the processing employed. For example, smoothing is applied parallel to interfaces (to improve continuity) while edge enhancement is employed in the perpendicular direction [21]. Moreover, XRES works as a multi-resolution algorithm in the sense that spatial frequency sub-bands are generated from down-sampled images and processed as representing different scales. XRES is being marketed as an adjunct technique to spatial compounding (i.e., SonoCT for this particular manufacturer) and indeed the only clinical evaluation published to date on XRES used the two techniques in conjunction [23]. Improvements in image quality were reported. As an example conventional grayscale imaging of the left lobe of the thyroid and the same image with XRES
applied are presented in Fig. 8A and B. Some edge enhancement of the muscle layers above the thyroid can be seen when XRES is turned on. For comparison, thyroid images with spatial compounding (Fig. 8C) and with both spatial compounding and XRES employed (Fig. 8D) are also shown. Speckle patterns have changed markedly from Fig. 8C and D relative to Fig. 8A and B.
3. Advances in flow imaging techniques As all diagnostic imaging modalities move from anatomical depiction (as in grayscale mode) towards functional imaging the importance of ultrasonic blood flow imaging increases. 3.1. Automatic optimization techniques Manufacturers have recognized that Doppler flow measurements are among the most complicated studies performed with ultrasound requiring extensive user optimization to produce diagnostic data. Hence, a number of automatic optimization techniques for flow imaging have been introduced (Table 2). By analyzing spectral contents and noise it is possible, with a single button push, to adjust spectral Doppler waveforms to optimize gain, baseline, dynamic range and pulse-repetition frequency (PRF). It is even possible to automatically adjust the beam-to-vessel angle. This is achieved by searching from the sample volume and out in both directions for bright interfaces and the edge of the color data. These are assumed to locally represent the morphology of the blood vessel wall and, thus, the angle of insonation can be estimated. Likewise, internal color contrast and threshold parameters can be modified to maximize the dynamic range displayed. While these concepts are intriguing all of the automatic optimization methods work best with no or low level noise, which are not circumstances one encounters often in clinical practice. 3.2. B-flow imaging It is possible to directly visualize groups of moving red blood cells within the blood using grayscale sonography by extending the coded excitation schemes developed by GE Medical Systems. This method is known as B-flow and it provides higher frame rates than conventional color Doppler and high dynamic range flow imaging at markedly improved spatial and temporal resolution [24]. Moreover, irrespective of the velocity, flow in both arteries and veins can be visualized with B-flow (unlike conventional scanners that only depict red blood cells in veins with slow flow). An equalization filter is used to subtract a fraction of the received signal from the previous A-line to suppress clutter and slow
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Fig. 8. Left lobe of the thyroid (T) and the carotid artery (CA) imaged in conventional grayscale mode (A) and with XRES applied (B). Notice the edge enhancement in the muscle layers above the thyroid. Thyroid imaged with spatial compounding (C) and with both spatial compounding and XRES employed (D). Speckle patterns have changed markedly from (C) and (D) relative to (A) and (B).
moving signal components, while retaining the faster moving blood echoes. More importantly, no operator optimization is required and the velocity and angle independence eliminates Doppler artifacts. However, no quantitative data can currently be derived from B-flow data (although B-flow, like color Doppler imaging, can obviously be used to guide placement of a spectral Doppler sample volume). An example of color Doppler and B-flow imaging of the carotid artery is presented in Fig. 9. The region of elevated velocities on the left in the vessel appears as yellow/blue patterns in color Doppler mode and as brighter grayscale echoes in B-flow mode. Notice, the fine depiction of the vessel lumen achievable with Bflow. Mixed results have been reported with B-flow to date. In a recent study by Garra et al. duplex Doppler and
B-flow assessment of carotid stenoses were performed in 35 patients (40 vessels) and compared to angiography or MRA [25]. In 18 carotid arteries agreement was within ±5% and in the remaining 22 within ±10%. The conclusion was that B-flow was better than duplex Doppler for identifying low grade stenoses (<40%). However, such low grade stenoses are not clinically significant. Somewhat disappointing results were also reported by Bucek et al. in their evaluation of 28 patients with carotid stenoses [26]. They found no correlation between Bflow, color Doppler and angiographic parameters for assessing internal carotid stenoses (p > 0:05). Conversely, Umemura and Yamada found B-flow to be highly effective in visualizing flow and detecting stenosis in 60 patients with ischemic cerebrovascular disease [27]. They described excellent correlation between digital subtraction angiography (DSA) and B-flow assessments
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Fig. 9. Flow in the carotid artery depicted in (A) color Doppler mode and (B) B-flow. The region of elevated velocities on the left in the vessel shows as yellow/blue patterns in (A) and as brighter grayscale echoes in (B).
of degree of stenosis (p < 0:0001) in a sub-set of 15 patients. 3.3. Advanced dynamic flow The velocity estimation technique used in most color Doppler systems is based on calculating the complex phase of the autocorrelation function [28]. Alternative estimators employing wideband techniques have also been developed, which assumes that each group of scatterers produces a unique echo-pattern that can be tracked over time and distance (i.e., a form of speckle tracking to locate the movements of a specific group of red blood cells) [28]. One particular implementation is advanced dynamic flow (ADF by Toshiba Medical Systems), which relies on wide bandwidth transmit pulses (i.e., short pulses similar to B-mode ultrasound) for improved spatial resolution and reduced estimator variance [29]. To regain the loss in sensitivity, associated with the reduced acoustic energy in a shorter pulse, ADF uses a receive frequency swept according to depth followed by waveform shaping using a filter. This method is typically employed for grayscale processing, but can be used in this context because ADF is
Fig. 10. The left internal carotid artery in a patient with a low grade stenosis depicted in (A) color Doppler mode and (B) ADF mode. Notice the improved visualization of the plaque surface/functional lumen interface seen with ADF. (Courtesy of Toshiba America Medical Systems.)
a power Doppler technique (i.e., velocity data is not required). The left internal carotid artery in a patient with a low grade stenosis is depicted in Fig. 10 in color Doppler and in ADF modes (albeit from slightly different scan planes). ADF is a variation of directional power Doppler as can be seen by the color bar (on the left in Fig. 10B). Also notice the improved visualization of the plaque surface/functional lumen interface seen with ADF, due to the improvement in sensitivity and vessel wall depiction obtained with this technique. ADF has been reported to be particularly useful in fetal vascular imaging [30].
4. Advances in volume imaging techniques A major drawback in the clinical utility of conventional ultrasound imaging has been the limited field of view defined by the aperture of the transducer. While the free hand movement of the probe provides much flexibility, it is often difficult to acquire major anatomical landmarks as well as the target structure on a single image. Overcoming this limitation to obtain true volume
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data has long been a goal for engineers and clinicians alike [31]. 4.1. Extended field of view imaging One recent development is called extended field of view imaging (often abbreviated XFOV) and provides a first step towards volume data acquisition [32]. This method uses a 2D tracking algorithm applied to image features across multiple images (in grayscale or power Doppler mode) obtained in a steady sweep. By matching the same features across images, an amalgamated image of a complete tissue structure can be produced to provide a more global perspective. Extended field of view imaging is marketed by the major manufacturers under at least five different names (Siescape, LOGIQView, FreeStyle extended imaging, ApliClear and Panoramic imaging), which indicates that it fulfills a real clinical need––albeit with a lot name confusion added for users. To date extended field of view imaging has been utilized for evaluating both lobes of the thyroid simultaneously as well as for muscoloskeletal applications [33]. Fig. 11 is an example of a patient who presented with a marked superficial swelling of unknown origin. Con-
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ventional sonography (Fig. 11A and B) was inconclusive, but the extended field of view image revealed a very large lipoma almost 35 · 65 mm in diameter (Fig. 11C). This finding was confirmed by CT scan (Fig. 11D). 4.2. Three dimensional imaging Due to the real time (or at least near real time) requirements of ultrasound imaging the time available for acquisition and processing of a 3D volume of data is very limited. This is particularly true when blood flow data is acquired with Doppler ultrasound using multiple firing techniques. Producing an accurate 3D ultrasound image relies critically on the data acquisition technique, and on exact definition of the 3D volume. In a static image collection modality with fixed slice thickness, such as CT or MRI, the geometry of the 3D volume is given, but in ultrasound a different approach has to be taken. To date, most manufacturers have relied on complete free-hand acquisition due to the relative ease of implementation and the use of standard 2D probes. This is akin to XFOV, but requires feature tracking in three directions. The 3D volume can also be specified by placing sensors on a conventional transducer and then
Fig. 11. Large superficial area of swelling shown with conventional sonography (A) and (B). Some vague outline of a border (arrowheads) can be seen in (B). Extended field of view image (C) revealed a very large lipoma greater then 6.0 cm in diameter (see measurements). This finding (arrows) was confirmed by CT (D).
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tracking its location in space via remote localizers or by mounting the transducer in a mechanical arm to move the probe using stepping motors under automatic control. A more elegant approach is afforded by 2D electronic arrays, which permit focusing in the elevation plane as well as true angle-independent flow measurements [4,31]. Commercial 2D arrays are finally emerging and probably represent the ultimate technique for real time 3D (sometimes referred to as 4D) ultrasound data acquisition. At the June, 2003 meeting of the American Society of Echocardiography Philips Medical Systems presented the first commercial 2800 element 2D phased array with real time 3D color Doppler imaging capabilities. Three-dimensional grayscale ultrasound imaging has already become a clinically useful tool in obstetrics; in particular for diagnosing facial abnormalities. An example of surface rendering of a fetal face is presented in Fig. 12, where a normal pregnancy at 31 weeks gestation was sent for 3D imaging to rule out cleft lip (suspected in an initial 2D examination). The upper lip was well visualized and appeared completely normal, thus excluding the possibility of a cleft lip. Furthermore, some structures (e.g., the fetal spine) can be difficult to visualize in completeness with conventional 2D ultrasound. Here the ability of 3D imaging to display planes otherwise not seen with 2D imaging (e.g., orthogonal to the transducer) is proving advantageous. The clinical applications of vascular 3D ultrasound imaging include the assessment of blood flow in the kidney, placenta, prostate and carotid artery [34,35]. The benefit is being able to localize a functional abnormality relative to the underlying anatomy. However, vascular 3D imaging is not yet used routinely in the clinic (although this may well change in the future given the arrival of 2D arrays with flow imaging capabilities).
5. Conclusions A review of many of the recent advances in grayscale ultrasound imaging (2D arrays, coded excitation imaging, spatial compounding, tissue harmonic imaging, photopic imaging, XRES and XFOV), flow imaging (optimization methods, B-flow and ADF) and 3D volume imaging has been given. In order to differentiate between developments aimed at marketing and those providing real clinical improvements it is important to recall that the more a technology is reported upon in the scientific literature and adopted by multiple manufacturers the more likely it represents a genuine attempt to address clinical needs. In the early days following the introduction of a unique new ultrasound imaging technology, such collaborative information may well be unavailable. Under such circumstances, users should adopt the common sense axiom that the more a technology is based on acoustical physics the more likely it is to work. Having made this determination, operators should strive for maximum exposure to the new technology (preferably during scanning of patients). The recent history of technical developments in ultrasound is characterized by the extraordinary number and diversity of new technologies being offered by manufacturers. All new ultrasound imaging tools will have strengths and weaknesses, but technologies that provide real clinical benefits (alone or in combination with other methods) will ultimately be successful.
Acknowledgements The advice and assistance of G. Bega, MD, B.B. Goldberg, MD, E.J. Halpern, MD, P. Lewin, PhD, J.B. Liu, MD, C.R.B. Merritt, MD, D.A. Merton, BS, RDMS, L. Needleman, MD, P. O’Kane, MD, N.M. Rawool, MD, W.T. Shi, PhD, L.D. Waldroup, BS, RDMS is gratefully acknowledged.
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Fig. 12. 3D grayscale imaging of a 31 week fetus referred for suspected cleft lip after a conventional 2D scan. Clearly, no cleft lip is present.
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