Realignment of myocardial first-pass MR perfusion images using an automatic detection of the heart-lung interface

Magnetic Resonance Imaging 22 (2004) 1001–1009

Realignment of myocardial first-pass MR perfusion images using an automatic detection of the heart-lung interface Alexandre Comtea,*, Alain Lalandea, Serge Ahob, Paul M. Walkera, Franc¸ois Brunottea a

Laboratoire de Biophysique, Faculte´ de Me´decine, Universite´ de Bourgogne, Dijon, France b Service d’Epide´miologie, CHU Dijon, Dijon Cedex 21034, France Received 30 December 2003; accepted 30 January 2004

Abstract Magnetic resonance first-pass imaging of a bolus of contrast agent is well adapted to distinguish normal and hypoperfused areas of the myocardium. In most cases, the signal intensity-time curves in user defined regions of nterest (ROI) are studied. As image acquisition is ECG-gated, the images are acquired at the same moment in the cardiac cycle, and the basic shape of the heart is similar from one view to the next. However, superficial respiratory motion can displace the heart in the short-axis plane. The aim of this study is to correct for the respiratory motion of the heart by performing an automatic realignment of the myocardial ROI based on a method tracking the movement of the lung-myocardium interface. Visual and quantitative analyses performed on 120 curves show a very good concordance between two automatic methods and the manual one. © 2004 Elsevier Inc. All rights reserved. Keywords: Contrast-enhanced first-pass; Image registration; Magnetic resonance imaging; Myocardial perfusion

1. Introduction Magnetic resonance imaging (MRI) of the myocardial first pass of a bolus of contrast agent allows one to distinguish normal from hypoperfused left ventricular (LV) myocardium. In most studies, signal intensity-time curves are generated from short-axis slices after delineation of myocardial regions of interests (ROIs) [1–17]. These ROIs are usually manually drawn. In order to acquire curves over a sufficient time, each imaging plane has to be imaged up to a hundred times. Unfortunately, it is often difficult to avoid respiratory movements of the heart and it is necessary to realign the ROIs on each image of the whole series. As manual realignment of the ROIs is time consuming and somewhat subjective, several automatic methods have been developed in the past to perform the ROI realignment. The aim of the present study is to propose a novel approach of myocardial ROIs realignment based on the tracking of the movement of a well-defined anatomic landmark, i.e., the heart-lung interface. The subsequent ROI realignment is

* Corresponding author. Tel.: ⫹33-3-80-39-32-94; fax: ⫹33-3-80-3932-93. E-mail address: [email protected] (A. Comte). 0730-725X/04/$ – see front matter © 2004 Elsevier Inc. All rights reserved. doi:10.1016/j.mri.2004.01.058

based on the hypothesis of an inplane rigid displacement of the heart.

2. Material and methods 2.1. Study population Six patients were included in this study (two men, four women) with a recent myocardial infarction. The mean age of the patients was 48.2 ⫾ 13.8 years (range 40 –75 years). The study was conducted in accordance with the recommendations of the local ethics committee. 2.2. Image acquisition technique Magnetic resonance imaging was performed on a 1.5 T magnetic resonance whole body imager (Siemens Magnetom Vision, Siemens GmbH, Erlangen, Germany) using a phased-array body coil. The magnetic resonance data were acquired using an electrocardiogram (ECG)-gated gradientecho sequence (turboFLASH: (repetition time) TR ⫽ 3.5 ms / (echo time) TE ⫽ 1.7 ms) after the injection of a bolus of gadolinium-DTPA (Magnevist, Schering A.G., Berlin, Germany) in a brachial vein at 0.2 mL/kg. The sequence

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into 512*512. The heart was imaged in the short-axis plane from the base to the apex with three to four slices of 12 mm, with an interslice gap of 3 mm. The cardiac gating allowed imaging of each plane at the same time within the cardiac cycle over the whole acquisition. Sixty frames were obtained from each imaged plane. 2.3. Reference frame ROI definition

Fig. 1. Short-axis MR image during first-pass perfusion study. ROI allows the evaluation of tissue signal enhancement in eight different myocardial regions.

For each series of images corresponding to a given imaging plane, endocardial and epicardial borders are manually drawn by a trained operator on a frame chosen for its optimal contrast between myocardium and the blood in the left ventricular cavity. This frame is defined as the reference image. After identification of the insertion of the right ventricle free wall, an automatic program divides the myocardium in eight segments (Fig. 1). The ROI drawn on the reference image is then realigned manually on all the frames of the series in order to generate a reference perfusion curve on each myocardial segment. The realignment of the ROI will allow one to match the correct curve shape (Fig. 2). 2.4. Automatic realignment of MR images

was T1-weighted with an inversion time of 400 ms. A time delay buffer at the end of the sequence was adjusted in order to acquire one image every two cardiac cycles. The field of view was 400 mm and the matrix size was 96*128 resized

The aim of the automatic processing is to realign the ROI drawn on the reference image on all the images of the series. From the initial frame, the program moves backward for the

Fig. 2. Utility of the ROI realignment on the myocardium. (a) Poor position of the ROI due to respiratory motion. (b) Correct position of the ROI through realignment. Curves obtained (c) without or (d) with realignment.

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Fig. 3. Flow diagram summarizing the different steps of the heart-lung interface detection. From (a) the initial image, the lung is obtained (b) after a region growing method and then (c) mathematical morphological operations. With a laplacian filter, (d) the lung contour is extracted and then (e) converted into polar coordinates. From the previous step, (f) the heart-lung interface in polar coordinates is found, and reconverted (g) ino cartesian coordinates.

images acquired before the chosen image and forward for the images acquired later. As the acquisition is ECG-gated, the images of a given plane are acquired at the same moment within the cardiac cycle. Thus, it is hypothesised that the shape of the heart is not modified and that registration problems due to the systolic shortening of the long-axis of the heart can be neglected. It is also hypothesised that movements through the imaging plane due to respiratory motion are negligible. As a consequence, a rigid realignment of the ROI is performed without affine distortion. In the same manner as in manual processing, a perfusion curve is obtained for each studied myocardial segment. The realignment technique is based on the precise location and tracking of a particular anatomical feature in the image. We have chosen the interface between the left lung and the heart as an anatomical landmark, because from one image to the next, the deformation of this interface is only slight. It was hypothesised that the displacement of the heart follows exactly that of this interface. The realignment process can be decomposed into three steps: the lung detection, the detection of the whole interface between the lung and the heart, and the shifting of this landmark from one image to another. All the steps of the detection process are illustrated in the flow chart (Fig. 3). 2.4.1. Lung detection The lung has a low signal that varies only slightly during the first pass imaging. The heart is surrounded by the peri-

cardium and by fat. Pleura being very thin, there is a striking contrast between the lung and the heart that may facilitate automatic detection of the heart-lung interface. Thus, the detection of this interface is performed via the detection of the lung itself. For early image frames of the perfusion sequence, before the bolus injection, the heart is dark and the detection of the lung will encounter problems. In that case, the ROI remains at the same position as on the reference image without lung detection. A simple edge localisation process is not feasible because the images are too heterogeneous. In fact, the presence on the images of bright vessels in the lung makes detection poor. The first step consists in manually selecting a small region (A) within the left lung on the reference image. The coordinates of this area are conserved in order to be used on the following images. From A, we calculate the mean grey Gl共 p兲 level (ap) of the pixels included in A: ap ⫽ p␧A , with Card共 A兲 G1(p) the grey level of the pixel p and Card(A) the cardinal of A. A dynamic region growing method is then used to detect the lung. It consists in finding all connected pixels verifying a given criterion. Our criterion (C) is defined such as: (p verifies C) N (G1(p) ⱕ T), with T ⫽ ap ⫹ 10. The margin of 10 has been determined from the results of a study performed on 10 patients, concerning the distribution of the

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grey level intensity in the lung. We obtained a mean grey level of 14.5 and standard deviation (SD) of 4.7 (10 corresponds to the rounding off of 2 ⫻ SD). The starting pixel is the isobarycentre of A, if and only if it verifies C, otherwise the first nearest point in the neighbourhood verifying C is chosen. From the starting pixel, the eight neighbours are tested to see if they verify C. Those that do not verify C are excluded. The others are labelled with checked flags and are stocked so that each pixel is only tested once. Every thousand (empirically determined) new detected pixels, a new mean grey level of pixels (ap') is calculated and T is updated, with T ⫽ ap' ⫹ 10. The result of this detection by a region growing method is a lung punctuated with “holes” (Fig. 3b). A new image (I) is created with the previously detected pixels, which appear in white and the rest of the image in black. Mathematical morphological tools are then used to improve the lung detection. The mathematical morphology is a complete and coherent theory [18]. The principle is to compare objects in an image with a reference user-defined object, called structuring element. The most frequently used operations are: dilatation, erosion, opening, closing. In our case, to fill in the holes in I, successive closings with different structuring elements are applied to I (Fig. 3c). The shapes used for the structuring element are the square and the diamond. These two shapes are used alternatively and allow one to avoid favouring one particular direction. Moreover, the size of the structuring element increases during the processing for each closing step, from a width of 3 to 13 pixels (only odd sizes). The number of closings is constant and the sizes of the different structuring elements on each closing are the same whatever the patient data used. The last operation involves smoothing with a median filter to obtain a less irregular lung contour. 2.4.2. Detection of the interface between the lung and the heart Initially, a laplacien filter is applied on I to extract the contour of the detected pulmonary area (Fig. 3d). The second step determines the required anatomical landmark included in this contour. Then, the centre of the LV is indicated manually on the reference image, stored and used again on subsequent images. The lung contour is then converted into polar coordinates (Fig. 3e). A closing (morphological mathematical tool) is used to obtain a closed contour. For each row, the point whose abscissa is the minimum, if it exists, is retained. All the others are excluded. The average (a0) and standard deviation (e0) of the abscissa of the selected points are calculated, and among them, we excluded those whose abscissa is greater than 10 ⫽ a0 ⫹ e0 ⫹ ␧ខ with ␧ខ fixed empirically. The resulting contour (Fig. 3f) is converted back into cartesian coordinates, giving an approximation of the interface extracted from the lung contour. The approximation is due to rounding off during conversions (cartesian to polar coordinates and polar to cartesian coordinates). From this interface ap-

Fig. 4. Displacement vector obtained with the barycentric method. G1 ⫽ isobarycentre of the reference interface. G2 ⫽ isobarycentre of the current interface.

proximation, the points included on the original interface obtained with the laplacien filter are extracted in order to re-construct an interface belonging entirely to the lung contour (Fig. 3g). 2.4.3. Automatic realignment of MR images Once the interface between the myocardium and the lung is detected on the current image of the series, the displacement between the interface on this image and the interface on the reference image is calculated. Two different calculation methods have been developed: one is based on the interface isobarycentre and the second on the least squares’ method. Whatever the method used, this displacement is calculated for each image. The manually traced ROI on the reference image is realigned on each image according to this displacement. ROI size and shape were kept constant throughout the processing of each slice. 2.4.3.1. The barycentric method The isobarycentre of the interface calculated on the reference image is considered as the reference isobarycentre. The displacement vector corresponds to the misalignment between the isobarycentre of the interface on the current image and the reference isobarycentre (Fig. 4). One of the advantages of this method is that it is rapid. However, it does not take into account a rotation. 2.4.3.2. Use of the least squares’ method The method using the least squares’ method is based on the comparison between the position of the reference interface and the interface detected on the current image. The norm of the vectors defined by the extremities of the interface on the current and reference images are calculated. The fixed interface is associated with the vector with the larger of the two norms. The vector with the smaller norm (vជ ) is displaced on the fixed interface to give vជ i (i僆 {1, .., np}) such as both vជ i extremities are on this fixed interface, with np the finite number of possible positions for this vector. The extremities of the interface corresponding to vជ are ¡ named A and B such as vជ ⫽ AB (Fig. 5a). For each position (Pi, i僆 {1, .., np}) of the vector vជ i, the new coordinates of the points of the interface associated with this “moving” vector are calculated (Fig. 5b). Let n be the number of points of this interface.

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Fig. 5. Realignment performed with the method using the least squares (a) Interface of which associated vector (AB) norm is the smaller. (b) Positioning of this interface on the second interface with the least squares’ method. A and B must be placed on this second interface. (c) calculation of ␪˜ .

For each of these points (Mi,j, i僆 {1, .., np}, j僆 {1, .., n}), the minimum distance (dMi,j, i僆 {1, .., np}, j僆 {1, .., n}) to the fixed interface is calculated. Then we define Si as the sum of the squares of these distances (Si ⫽

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n

2 dM , i僆{1, .., np}). For each Pi, a corresponding i,j

i⫽1

Si is obtained. Let S ⫽ min{Si, i僆 {1, .., np}}, then, ?i0 僆 {1, .., np}/S ⫽ Si0, and Pi0 is the new position retained for the “moving” vector. Let vជ i0 be the vector corresponding to Pi0, and Ai0 and Bi0 the extremities of the interface in the Pi0 position (Fig. 5b). The next step is the determination of the displacement D, which transforms [AB] into [Ai0 Bi0]: D is a displacement in the plane therefore ? C僆ℜ2, ␪僆[0;2␲],R僆ᏹ2(⺢) and Tជ 僆 ⺢2 such as [Ai0Bi0] ⫽ ᏾([AB]) ⫹ Tជ with R cos␪ ⫺sin␪ . ᏾ is the affine rotation associated ⫽ sin␪ cos␪ with R of centre C and angle ␪. In our context, C ⫽ A, and ␪ is such as R vជ ⫽ vជ i0, (Fig. 5c) with R cos␪ ⫺sin␪ ⫽ . Tជ is the translation of vector sin␪ cos␪ ¡ ¡ AAi0(Tជ ⫽ AAi0). To realign the ROI there are two cases: (a) If vជ is associated with the reference contour, each point P of the ROI undergoes the previous displacement. Its image is P' ⫽ ᏾(P) ⫹ Tជ and P' is a point of the realigned ROI. (b) If vជ is associated with the current contour, P' is the image of P such as P⬘ ⫽ ᏾'(P ⫺ Tជ ) with ᏾' the affine rotation of centre A, and angle ⫺␪, i.e., R' ⫽ tR.

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3. Curve generation and statistical analysis In order to test the two methods, a software integrating these methods has been developed in our laboratory: the epicardial and endocardial contours are manually traced on

the myocardium by an experienced observer on the reference image. For the same set of images, two automatic realignments are done using barycentric and least squares’ methods. In order to validate both methods, an assessment of each method versus a manual realignment was performed by comparing the signal intensity-time curves, rather than basing the analysis on residual displacements, since this type of analysis is too user-dependent on very small displacements, and comparing the displacements with some identifiable landmark is difficult because of the too heterogeneous images. One hundred twenty signal intensity-time curves were generated for each method (both automatic methods and the manual one). Every curve consists of a set of 60 points. Firstly, a visual analysis has been carried out: three experienced users have classified the 360 (3 ⫻ 120) curves into four groups according to the classification proposed by Rogers et al. [19]. This classification is illustrated in Fig. 6. This classification was performed at one week’s interval for each method. Nonparametric Kappa-tests [20] were performed in order to compare the classification results. Intraand inter-observer kappa coefficients and inter-method kappa coefficients were calculated. To evaluate the results, a classification of agreement depending on the kappa coefficient value proposed by Landis and Koch [21] was used. Secondly, a quantitative analysis has been performed: to estimate the differences between the automatic and manual realignments, 120 BlandAltman plots [22] were generated for each automatic method by considering the curve obtained with the manual realignment as the independent variable and the corresponding curve obtained with an automatic method as the dependent variable. Figure 7 shows an example of a BlandAltman plot. The 120 averages of differences and the 120 standard deviations were retained from the Bland-Altman

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Fig. 6. Classification of different curves according to Rogers et al. [19].

plot results. Thus, rather than having a Bland-Altman plot for the entire set of data points, each signal intensity-time curve has its own Bland-Altman plot. It therefore allows us to appreciate the concordance between manual and automatic approaches for each curve. To complete this analysis, a comparison of performance between the two automatic methods has been performed: distribution plots of the means and the standard deviations have been made for each method, and an F test was performed on the standard deviations and a t test on the means (means follow a normal distribution).

4. Results With regards to the visual analysis, the kappa-test results are reported in Table 1. All kappa coefficients are greater

than 0.66 with p ⬍ 0.0001 (statistical significance was accepted for p ⬍ 0.05) and half of them are greater than 0.80, thus suggesting good results: our results of intra- and inter-observer kappa-tests, and inter-method kappa-tests are, according to the classification proposed by Landis and Koch [21], either ‘very good’ or ‘good’. Figure 8a shows the distribution histogram of the 120 means obtained from the 120 Bland-Altman plots considering the barycentric and manual methods. The mean of these 120 values is 0.55, and the standard deviation is 2.19. Figure 8b shows the equivalent histogram generated with the second automatic method results. The mean is 0.66, and the standard deviation is 2.94. Figure 8c and 8d represent the distribution of the standard deviations extracted from the Bland-Altman plots. Both F and t tests performed on these plots were nonsignificant, inferring that the two methods are equivalent.

Fig. 7. Example of Bland-Altman plot. The curve with a manual realignment is the independent variable and the corresponding curve obtained with an automatic method is the dependent variable. Mean ⫽ 0.53, ␴ ⫽ 2.36. The 60 points on the plot are obtained from the 60 points constituting both curves.

A. Comte et al. / Magnetic Resonance Imaging 22 (2004) 1001–1009 Table 1 Kappa test results

Intra-observer Obs 1/Obs 2 Obs 1/Obs 3 Obs 2/Obs 3 Inter-observer Obs 1 Obs 2 Obs 3 Inter-method Obs 1 Ref/M1 Ref/M2 Obs 2 Ref/M1 Ref/M2 Obs 3 Ref/M1 Ref/M2

Kappa

Standard Error

p

0.90 0.88 0.80

0.07 0.08 0.08

⬍0.0001 ⬍0.0001 ⬍0.0001

0.88 0.66 0.72

0.08 0.08 0.08

⬍0.0001 ⬍0.0001 ⬍0.0001

0.74 0.77

0.07 0.07

⬍0.0001 ⬍0.0001

0.75 0.72

0.08 0.08

⬍0.0001 ⬍0.0001

0.91 0.87

0.08 0.09

⬍0.0001 ⬍0.0001

Statistical significance was accepted for p ⬍0.05. Ref ⫽ the manual method; M1 ⫽ the barycentric method; M2 ⫽ the method using the least squares.

5. Discussion Previous publications [2,3,5,8 –10,15–17,19,23–29] have shown that myocardial perfusion can be studied successfully using MRI first pass imaging. However, image acquisition provides a large amount of data and post-processing

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can be particularly time consuming and user-dependent. The present study has shown that intensity-time curves generated after automatic realignment do not differ from those obtained after manual realignment. Manual tracing of a ROI on a reference image, followed by an automatic realignment of this ROI on all the images of the series, can render the post-processing less time-consuming and less user-dependent. Other methods have been proposed for realignment of ROI in myocardial perfusion studies. A method proposed by Yang et al. [30] consists of detecting the translational motion in the k-space and positioning the ROI on the myocardium with deformable contours (snakes). In the image domain it has been proposed by Delzescaux et al. [31] to match contours extracted from the image with an adaptative model (including the left ventricle, the right ventricle and the pericardium), combined with an approach based on the potential map. The method presented by Bidaut and Valle´ e [32] consists of displacing an area englobing the heart on the current image, according to two translations and one rotation and comparing this area with the corresponding area on a reference image by calculating the mean of the pixel-based squared difference between these two areas. By minimizing this value, the optimal position is found. One of the advantages of the present method over the previously described techniques is that the results are obtained from clinical data with a wide set of curves and not with phantoms. The present study hypothesised that the main motion of

Fig. 8. Repartition of the means obtained with the 120 Bland-Altman plots for (a) the barycentric method (m ⫽ 0.55 ⫾ 2.19) and (b) the method using the least squares (m ⫽ 0.66 ⫾ 2.94). And, repartition of the standard deviations obtained with the 120 Bland-Altman plots for (c) the barycentric method and (d) the method using the least squares.

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the heart occurs in the studied plane. Through plane motion along the long axis of the heart has been considered as negligible. Motion along the long axis of the heart is mainly due to systolic contraction that produces a shift of the heart toward the apex. This phenomenon is minimized in the present study since ECG-gating allows imaging a given plane at the same moment in the cardiac cycle. Obviously, cardiac arrhythmia limits this approach, the heart being imaged at different levels of contraction. Therefore, arrhythmia induces through plane displacement and a modification of the shape of the left ventricle on the successively acquired images. Although optimal in case of breath-hold acquisitions, the present method can also be used while the patient is breathing quietly. The fact that the patient might be allowed to maintain breathing makes the examination more comfortable and physiological than a prolonged breath-hold. Moreover, the impulsive respiratory movement at the end of a poorly tolerated breath-hold might be much more difficult to correct than the slow amplitude movements due to slow and quiet respiration. The Bland-Altman plots allow one to compare quantitatively the curves generated by the different methods point by point, and the results show a good concordance between the tested curves. The inter-method differences are very close to zero, which is negligible in comparison with the amplitude of the recorded curves. The grey level value during the contrast transit varying between 5 and 85 (mean of 38 ⫾ 21), the concordance between two curves obtained automatically and manually is high. It should be recognised that the present method is limited in case of a poor detection of the lung. Most frequently this phenomenon occurs close to the apex, where the interface between the lung and the heart is either very short or nonexistant. In these cases, automatic methods might fail. In such a case, it is useful to manually realign the ROI on a few images. This user intervention is not time consuming and usually improves the quality of the curves without requiring a manual realignment of the ROI on all the images of the series. This manual correction has not been used in the present study. As a consequence, the current results may be further improved by correcting the ROI misalignment on a few images. Both methods are reliable and robust, and automatic processing of myocardium first-pass study with MRI is feasible by using a realignment based on a rigid method. Concerning the realignment method, user intervention is limited to the selection of a point close to the centre of the LV and to the selection of an area within the lung, on the reference image. These interventions are easy to do and take place only at the beginning of the processing. Besides, these methods are easy to implement and might be used for others applications requiring realignment of other structures than the left myocardium. Currently, this software is used in our routine clinical context.

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