Enhanced Laws textures: A potential MRI surrogate marker of hepatic fibrosis in a murine model

    Enhanced Laws textures: A potential MRI surrogate marker of hepatic fibrosis in a murine model Baojun Li PhD, Hernan Jara PhD, Heishun Yu MD, Michael O’Brien MD, Jorge Soto MD, Stephan W. Anderson MD PII: DOI: Reference:

S0730-725X(16)30218-1 doi: 10.1016/j.mri.2016.11.008 MRI 8663

To appear in:

Magnetic Resonance Imaging

Received date: Revised date: Accepted date:

19 July 2016 31 October 2016 12 November 2016

Please cite this article as: Li Baojun, Jara Hernan, Yu Heishun, O’Brien Michael, Soto Jorge, Anderson Stephan W., Enhanced Laws textures: A potential MRI surrogate marker of hepatic fibrosis in a murine model, Magnetic Resonance Imaging (2016), doi: 10.1016/j.mri.2016.11.008

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Enhanced laws textures: A potential MRI surrogate marker of hepatic fibrosis in a murine model

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Baojun Li, PhD1, Hernan Jara1, PhD, Heishun Yu2, MD, Michael O’Brien3, MD, Jorge Soto1, MD, Stephan W. Anderson1, MD Department of Radiology, Boston University School of Medicine, Boston, MA 02118, USA

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Department of Radiology, Masschusetts General Hospital, Boston, MA 02118, USA

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Department of Pathology and Laboratory Medicine, Boston University School of Medicine, Boston, MA 02118, USA

Corresponding Author:

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Baojun Li, Ph.D. Department of Radiology Boston University School of Medicine Boston, MA 02118 T 617.638.7186 F 617.638.7509 [email protected]

Acknowledgement We would like to thank Dr. Karen Buch for her inspiring discussions during this study. We would also like to express our gratitude to the anonymous reviewers for their precious time and expert critics that have made the manuscript a much better one. The authors have no relevant conflicts of interest to disclose.

Running Title Enhanced Laws textures as MRI marker of hepatic fibrosis

ACCEPTED MANUSCRIPT 2 ABSTRACT

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Purpose: To compare enhanced Laws textures derived from parametric proton density (PD) maps to other MRI surrogate markers (T2, PD, apparent diffusion coefficient (ADC)) in assessing degrees of liver fibrosis in an ex vivo murine model of hepatic fibrosis imaged using 11.7T MRI.

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Methods: This animal study was IACUC approved. Fourteen male, C57BL/6 mice were divided into control and experimental groups. The latter were fed a 3,5-dicarbethoxy-1, 4dihydrocollidine (DDC) supplemented diet to induce hepatic fibrosis. Ex vivo liver specimens were imaged using an 11.7T scanner, from which the parametric PD, T2, and ADC maps were generated from spin-echo pulsed field gradient and multi-echo spin-echo acquisitions. A sequential enhanced Laws texture analysis was applied to the PD maps: automated dualclustering algorithm, optimal thresholding algorithm, global grayscale correction, and Laws texture features extraction. Degrees of fibrosis were independently assessed by digital image analysis (a.k.a. %Area Fibrosis). Scatterplot graphs comparing enhanced Laws texture features, T2, PD, and ADC values to degrees of fibrosis were generated and correlation coefficients were calculated.

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Results: Hepatic fibrosis and the enhanced Laws texture features were strongly correlated with higher %Area Fibrosis associated with higher Laws textures (r = 0.89). Without the proposed enhancements, only a moderate correlation was detected between %Area Fibrosis and unenhanced Laws texture features (r = 0.70). Correlation also existed between %Area Fibrosis and ADC (r = 0.86), PD (r = 0.65), and T2 (r = 0.66). Conclusions: Higher degrees of hepatic fibrosis are associated with increased Laws textures. The proposed enhancements could improve the accuracy of Laws texture features significantly. Keywords Laws texture features; T2; ADC; liver; fibrosis Abbreviations used ADC, apparent diffusion coefficient; CT, computed tomography; DDC, dihydrocollidine; DIA, digital image analysis; DWI, diffusion-weighted imaging; DW-TSE, diffusion-weighted turbo spin-echo; IACUC, institutional animal care and use committee; MR-E, MR-elastography; MRI, magnetic resonance imaging; NAFLD, nonalcoholic fatty liver disease; PBS, phosphate buffered saline; PD, proton density; RF, radio-frequency; RMS, root-meansquare; ROI, region of interest;

ACCEPTED MANUSCRIPT 3 1. INTRODUCTION Chronic liver diseases have become a worldwide health and economic concern with their

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widespread and high occurrence [1] [2] [3] [4] [5]. Hepatic fibrosis occurs as a non-specific

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response to a number of chronic liver insults, including viral hepatitis [5] [6], alcohol consumption [7], and NAFLD [8]. The reactive process of hepatic fibrosis involves the

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activation of hepatic stellate cells and subsequent accumulation of extracellular matrix proteins

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such as collagen [9]. Since hepatic fibrosis may lead to cirrhosis, liver failure, and hepatocellular carcinoma, early diagnosis of its presence and severity of is essential to halt disease progression

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[10].

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Percutaneous liver biopsy is the current gold standard for assessing liver fibrosis, but it is non-

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targeted, invasive, and expensive – disadvantages that limit its accuracy and reproducibility [11]

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[12] [13]. Due to these limitations of liver biopsy, and the potential complications (bleeding, infection, and patient discomfort), it is necessary to develop noninvasive methods to accurately diagnose hepatic fibrosis. To address this unmet need, various imaging techniques have emerged as noninvasive alternatives in detection and assessment of hepatic fibrosis. Conventional ultrasound, CT, and MRI are effective means to evaluate liver morphology and confirm cirrhosis in patients with advanced liver diseases, but such assessment is not effective to assess early stages of fibrosis [10] [14] [15] [16].

Various advanced MRI techniques have been explored. Among others, MR-E and DWI are both promising techniques for the diagnosis and staging of hepatic fibrosis. MR-E has been shown to provide an accurate diagnosis and staging of liver fibrosis [17] [18]. However, its availability has

ACCEPTED MANUSCRIPT 4 been severely limited because of the need for a special hardware. DWI has demonstrated some success in detecting hepatic fibrosis, as the aggregation of inflammatory cell infiltrate and

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scarring has reduced the translational mobility of water molecules [19] [20] [21] [22] [23] [24].

An alternative imaging-based approach to evaluate hepatic fibrosis is texture analysis. Texture

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analysis studies the complex visual pattern within the image of liver parenchyma that contains

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characteristic subpatterns (or texture). Such techniques have been applied successfully to brain, bone, and cartilage [25] [26] [27]. Studies have shown that various degrees of hepatic fibrosis

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lead to significant alterations in MRI texture of hepatic parenchyma [10] [28] [29]. One such study suggested that texture analysis can accurately diagnose and stage liver fibrosis [10].

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However, among other inherent limitations of this study, is the need for double contrastenhanced MRI, which is not a standard of clinical practice, limiting the availability and

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applicability of this technique.

Utilizing a powerful, state-of-the-art 11.7-Tesla pre-clinical MRI scanner, our group has recently demonstrated, on a murine model, that moderate correlations exist between MRI textures and hepatic fibrosis [28] [29]. With the absence of inter-subject variability, and the highly reproducible gold standards, this experimental setting offers unique insights into the relationship between texture-based features and hepatic fibrosis. This study is an extension of our previous efforts. We propose an enhanced texture analysis of a particular group of texture-based features, called Laws textures, aiming to improve the feature extraction and overall quantification. With the hope to find an accurate MRI surrogate marker of hepatic fibrosis, the enhanced Laws texture features will be compared with other MRI-based surrogate markers (T2, PD, and ADC) with

ACCEPTED MANUSCRIPT 5 respect to their utility in assessing degrees of liver fibrosis. Parametric PD maps are of particular interest in this study because they represent the MR images with highest signal-to-noise ratio, in

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which tissue is represented by the pure base structural information without additional weighting

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from relaxation or diffusion.

ACCEPTED MANUSCRIPT 6 2. MATERIALS AND METHODS 2.1. Study Design

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The IACUC of our institution approved this study. A sample size analysis estimated that seven

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(7) samples would have enough power to detect any correlation among the data (α = 0.05, β = 0.8, r = 0.89) [30]. Fourteen male, 6-week-old C57BL/6 mice (Charles River Laboratories,

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Wilmington, MA) were divided into a control group which were fed normal chow, and an

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experimental group, which were fed a 0.1% (w/w) 3,5-diethoxycarbonyl-1,4-DDC-supplemented diet (TestDiet, Richmond, IN) for the induction of hepatic fibrosis. DDC-supplemented diets

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lead to sclerosing cholangitis and marked biliary fibrosis [31]. The experimental diet was continued for a total duration of 13 weeks and mice were sacrificed intermittently

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(approximately one mouse per week) throughout this period for subsequent liver excision and ex vivo MR imaging. The control mouse was killed immediately before the initiation of the

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experimental diet.

2.2. Image Experiments

Immediately after excision, each liver was fixed in formalin for 18 hours and subsequently stored in PBS (pH=7.4) before imaging. A 20-mm transmit/receive quadrature coil was used to image the liver specimens, which were placed in 15-mm glass vials and secured inside the coil to minimize vibration. A single smaller vial (6 mm diameter) containing both PBS and olive oil was placed within the larger 15-mm glass vial, adjacent to the liver samples, thus providing absolute aqueous and lipid ADC and T2 references. These reference substances were used in all scans to serve as inter-animal reference. The samples were temperature-controlled and all imaging was performed at 22.5 ± 1oC, measured with a MRI compatible temperature sensor.

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Image acquisitions were performed using a 11.7T MRI scanner (Bruker Biospin, Billerica, MA)

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with the following geometric parameters: voxel dimensions = 150 x 150 x 700 μm, matrix size =

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128 x 128, number of slices per specimen = 16. For purposes of deriving parametric ADC maps, a multiple-slice spin-echo pulsed field gradient pulse sequence with the following acquisition

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parameters was used: TE = 14.5 ms, TR = 2000 ms, b-values = 0, 270, and 560 s/mm2. Due to

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the limitation of shorter T2 values of the liver samples (~40 ms) after formalin fixation, the need of a shorter TE became necessary in this study [32]. These b-values were chosen to

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accommodate the shorter TE, while agreeing with the b-value used clinically [33] [34]. The diffusion sensitization gradients were applied along the frequency encoding direction. Fat

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suppression was not used. For the purpose of deriving parametric T2 and PD maps, a multi-echo spin-echo sequence with the following parameters was used: TE1 = 6.4 ms, echo spacing = 6.4

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ms, echoes = 32, TR = 4000 ms. The significant longer TR (than T2) ensured an unsaturated regime where true PD values can be found.

Since the detailed procedure to create ADC and T2 values (and associated maps) has been described in our previous studies [23] [24], we will focus mainly on the procedure to generate PD maps here. As shown in Figure 1, directly acquired signal intensity can be modeled as a single exponential function of echo time, i.e., , where

represents the pixel value at a given image voxel (i,j,k) and

related to the proton density of the sample.

(1) is a value

ACCEPTED MANUSCRIPT 8 Equation (1) represents a linear regression problem for the pixel value logarithms versus TE. The PD value for a given voxel is obtained from the semi-logarithmic linear square fits: (2)

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Based on signal decay curves for the various tissues and reference materials (olive oil and PBS), only the echoes with appropriate signal were used as input for the algorithm to avoid spurious

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PD calculations (number of echoes used as input: liver specimens (T2: ~40 ms), n = 14; olive oil, PBS (T2: ~140 ms), n = 32). A single ROI was placed in the internal reference vial to obtain

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reference PD value of PBS. The calculated PD values were expressed in relative units where

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water would be 1000 pu.

2.3. Enhanced Laws Texture Analysis

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2.3.1. Image Corrections/Pre-processing In the proposed texture analysis scheme, PD maps were pre-processed (or corrected) prior to the texture analysis, which consists of the following three steps: 1) elimination of hepatic blood vessels, 2) partial volume artifact correction, and 3) global grayscale normalization.

First, an automated dual-clustering algorithm was applied to the PD maps to segment the nonliver parenchyma including hepatic blood vessels, liver margins, and prominent artifacts (Figure 3). The dual-clustering algorithms were developed in our laboratory using MATHCAD (PTC, Needham, MA) [35]. The algorithm scans through each voxel in the data set to see whether it is contained in an user-predefined range of proton densities and as to whether it has a certain user prescribed level of connectivity, or clustering, with similar voxels in a geometric segment. There

ACCEPTED MANUSCRIPT 9 are two control parameters to be prescribed a priori by the operator, specifically the spatial cluster area expressed in units of pixels and the similarly clustering condition percentage

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required for accepting a given voxel to the desired segment. The similarly clustering condition

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percentage is defined as the minimum percentage of neighboring voxels that have similar proton densities. For this work, we used spatial cluster area of 169 pixels and similarly clustering

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condition percentage of 90%. Provisions were also added to the dual clustering segmentation

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algorithm for morphologically closing small islands of unselected voxels (holes) and for dilating the outer contour of the segment. Generated segments were inspected visually for accuracy and

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this accuracy check constitutes the remaining human effort required for segmentation.

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Secondly, an optimal thresholding algorithm was applied to reduce partial volume artifact arisen from image acquisition and reconstruction (highlighted in Figure 4(a)). The effect of these low-

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intensity pixels can be seen in the image histogram (circled in Figure 4(b)). This artifact can be sufficiently suppressed using an iterative optimal thresholding algorithm [36]. The method assumes all the image pixels are from two probability distributions (e.g., the liver tissues and the dark background) and attempts to find the graylevel threshold corresponding to the minimum probability between the maxima of the two distributions, which results in minimal segmentation error. To find the optimal threshold, this algorithm was applied iteratively (usually 4 to 10 iterations were sufficient), updating the threshold in each iteration from the weighted sum of the two distributions. The resulting image and image histogram are shown in Figure 4(c) and 4(d), respectively. Clearly the partial volume artifact is suppressed with minimal impact on the liver tissues themselves.

Finally, the PD maps were corrected by mean and standard deviation to minimize the overall

ACCEPTED MANUSCRIPT 10 grayscale variation across images. This step was critical in our study because the overall grayscale variation presented in the PD maps was unrelated to local image texture [37]. The

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intensity variation in the PD maps was attributed to poor RF coil uniformity and gradient-driven

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eddy currents. Although these intensity variations have little impact on visual diagnosis, the performance of Laws texture analysis, which assumes homogeneity of intensity within each

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texture subregion, can be significantly degraded. The correction was applied to the entire 128 x

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128 image. The mean gray value of each corrected image was set to 500 and the standard

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deviation to 250.

2.3.2. Laws Texture Analysis

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The texture energy measures developed by Laws [38] have been widely used in a variety of clinical applications [10] [25] [27] [28] [29] [39]. In this study, nine 5 x 5 operators were

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employed, which were formed by combining four elemental texture vectors: level (L), edge (E), spot (S), and ripple (R). Each pair of 5-pixel vectors was convolved to create a 5 x 5 operator and certain symmetric pairs (e.g., L5E5 and E5L5) were combined to produce the final nine operators (Table 1). For example, L5E5/E5L5 performs edge detection in vertical or horizontal directions, and L5S5/S5L5 detects lines in the orthogonal directions.

The corrected PD maps were entered into an in-house Matlab (Mathworks, Natick, MA) texture analysis program [25] [28] [29] [39] in order to calculate enhanced Laws texture features. A visual check was performed by an experienced MRI scientist (HJ) to confirm the absence of inclusion of hepatic blood vessels and liver margins. Finally, the value of each Law’s texture operator (Table 1) was calculated for each PD map and averaged across all maps of the

ACCEPTED MANUSCRIPT 11 specimen. This procedure yields nine Law texture features, one per operator, for each specimen. To ease the comparison with the fibrosis “gold standards” (discussed in the next section), the

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nine texture features were summarized by the RMS value.

In order to assess the effectiveness of the proposed enhancements described previously in

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Section C.1, the original PD maps were also entered into the texture analysis program to

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compute the set of nine unenhanced Laws texture features. A single investigator (BL) manually segmented and removed the reference materials (i.e., olive oil and PBS) from these PD maps

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prior to the computation of Laws texture features. Similar to the enhanced Laws texture features,

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2.4. Histopathologic Analysis

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these nine unenhanced Laws texture features were then summarized into the RMS value.

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Following the ex vivo imaging experiments, the liver specimens were embedded in paraffin and serial sections of 5 μm was cut. Sections were stained with hematoxylin and eosin as well as with Masson’s trichrome stains. A board certified pathologist (MO) with over 20 years’ experience in pathology of chronic liver disease reviewed the specimens to determine the extent of fibrosis and inflammation using commonly used grading schemes as a reference standard for subsequent comparison to texture analysis (Figure 5).

Subsequently, whole trichrome stained slides were digitized using a digital slide scanner (ScanScope CS, Aperio Technologies, Inc., Vista, CA) to generate an additional, digital image analysis (DIA) derived reference standard for subsequent comparison to texture analysis. Using automated software (Image-Pro+, Media Cybernetics, Inc., Bethesda, MD), a color-based

ACCEPTED MANUSCRIPT 12 segmentation was used to convert the histology image into a binary mask of the image (Figure 6). Colorimetric criteria were used to segment the blue staining fibrosis, the total area of which

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was expressed as a percentage of the total area of liver tissue on the slide (a.k.a. %Area Fibrosis).

2.5. Statistical Analysis

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Enhanced Laws texture features, unenhanced Laws texture features, T2, PD, and ADC values of

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the liver specimens were plotted against DIA-derived percentage area of fibrosis (%Area Fibrosis). Pearson or Spearman correlation coefficients were calculated. Correlation coefficients

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were classified by absolute value as follows: 0.0-0.2, very weak to negligible correlation; 0.20.4, weak correlation; 0.4-0.7, moderate correlation; 0.7-0.9, strong correlation; 0.9-1.0, very

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strong correlation.

ACCEPTED MANUSCRIPT 13 3. RESULTS Table 2 summarizes the numeric results from this study. The DDC-supplemented diet introduced

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significant fibrosis in the experimental mice. Subjective fibrosis score ranged from 0 to 4, while

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the digital image analyses determined the %Area Fibrosis ranged from 3% to almost 34%. The control mouse demonstrated a %Area Fibrosis of 3% because of the fact that collagen is present

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in normal liver. Inflammation ranged from mild (2) to severe (4) in all but the control mouse.

Scatterplot graphs comparing Laws textures, T2, PD, ADC values, and inflammation scores to

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degrees of fibrosis are displayed in Figure 7. Hepatic fibrosis and enhanced Laws texture features were strongly correlated with higher %Area Fibrosis associated with higher Laws

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textures (r = 0.89). Without the proposed enhancements, only a moderate correlation was detected between %Area Fibrosis and unenhanced Laws texture features (r = 0.70). There was a

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statistical significant difference between enhanced and unenhanced Laws texture features (p < 0.001). Strong, but negative correlation also existed between ADC and %Area Fibrosis (r = 0.86). Moderate correlations were seen between %Area Fibrosis and PD (r = -0.65) and T2 (r = 0.66).

ACCEPTED MANUSCRIPT 14 4. DISCUSSIONS This study demonstrated that the proposed enhancements to the conventional Laws texture

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analysis reduced scattering of the calculated texture results (p<0.001). Hepatic blood vessels carrying sharp edges can mimic extracellular collagen networks and bias those Laws texture

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operators emphasizing edges (e.g., L5E5, E5S5). As Laws texture operators essentially operate

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on grayscale image pixel values, global grayscale intensity variations across the specimens can

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artificially enhance or reduce the magnitude of texture features. Partial volume effect further increases the grayscale intensity variations. The dual-cluster algorithm has previously been

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applied in segmenting brain structures from diffusion-weighted, echo-planar imaging and proven to be highly accurate and immune to magnetic field inhomogeneities [35]. In this study, the

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algorithm worked well to exclude the blood vessels from subsequent texture analysis, as the

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proton density of these vessels typically differs from the rest of liver parenchyma. The optimal

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thresholding algorithm is a very popular algorithm that finds many medical applications, mainly due to its robustness against a large variety of image contrasts and histogram conditions [36].

It has been established that the presence of hepatic fibrosis and co-existence of hepatic steatosis have reduced the molecular diffusion and capillary perfusion, which result in decreased T2 and ADC values [23] [24]. As observed in this study, the underlying alteration in regional capillary perfusion is accompanied by increased Laws texture features, which reflect thickening of cellular walls and proliferation of external cellular collagen network on a regional basis. The finding that enhanced Laws texture and ADC value correlate much stronger with degrees of hepatic fibrosis, than T2 and PD, observed in this study suggests that MRI surrogate markers on a regional basis may be better suited, than those at a cellular scale, for monitoring underlying hepatic alterations

ACCEPTED MANUSCRIPT 15 and fibrosis progression. The benefit of evaluating cirrhosis on a global and regional basis has previously been reported in a cohort with advanced liver fibrosis (stage ≥ 3) [40]. Our finding

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was similar but the subjects involved all fibrosis stages.

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There are several challenges remaining in order to translate this technique into routine clinical

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practices. First, the high field gradient and the relative small field-of-view have resulted in a

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spatial resolution in the order of 150 μm. These conditions do not represent the typical scanners encountered in a clinical setting. Therefore, the results presented in this study cannot be directly

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generalized to an in vivo clinical setting. While the ex vivo environment employed in this study is inherently artificial, the methodology was highly controlled with all liver tissue specimens

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undergoing identical fixation procedures and specimens were imaged using an identical protocol

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using a fixed, temperature controlled environment. Therefore, the effects of disease progression

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on the proton density maps and their subsequently derived texture features, reported in this setting are postulated to predominately reflect differences in the tissue specimens related to varying degrees of fibrosis. Second, motion artifacts present in in vivo MRI scans can compromise the image quality especially for scans of the abdomen, which may lead to inaccurate quantification of PD maps. However, improvements in MRI technology, especially in the acceleration of image acquisition, and advance in DW-TSE might bring the possibility of PD maps into routine clinical usage. Third, B1 field inhomogeneity can pose a significant problem for in vivo MRI scans. While the proposed global grayscale normalization scheme was effective to reduce the non-uniformity across the liver specimen, its effectiveness still needs to be proven in the clinical field-of-view. Finally, characterization of in vivo diffusion is challenging because perfusion will affect intravoxel incoherent flow motion. To obtain the correct diffusion

ACCEPTED MANUSCRIPT 16 coefficients, the diffusion analysis will have to take into consideration of perfusion fraction, f,

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and pseudo diffusion, D*.

In conclusion, this study demonstrated that increased liver parenchymal textures derived from

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parametric proton density maps are associated with higher degrees of hepatic fibrosis. It was also

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demonstrated the proposed enhancements could significantly improve the accuracy of Laws

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texture features. Enhanced Laws texture features may be more accurate than PD and T2 in

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diagnosing and staging hepatic fibrosis.

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assessing degrees of fibrosis, and are potentially accurate imaging-based surrogate marker for

ACCEPTED MANUSCRIPT 17 REFERENCES

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Table 1. The following are the Laws texture operators used in this study.

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Level Edge Spot Ripple

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Edge in vertical/horizontal direction Repetitive lines in the orthogonal directions Ripple in vertical direction V-shaped structure and edge in diagonal directions Not well defined Not well defined Elongated spot in diagonal directions Large spot High-frequency spot

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5 x 5 Laws texture operator: L5E5/E5L5 L5S5/S5L5 L5R5/R5L5 E5S5/S5E5 E5R5/R5E5 S5R5/R5S5 E5E5 S5S5 R5R5

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Operator Definition Elemental 1-D Vector: L5 [1 4 6 4 1] E5 [-1 -2 0 2 1] S5 [-1 0 2 0 -1] R5 [1 -4 6 -4 1]

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Table 2. Comparison of histology, MRI, and Laws texture results in a Murine liver fibrosis model.

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38.4 30.4 26.7 30.1 32.1 31.6 24.4 18.0 21.6 30.1 24.7 23.7 21.7 28.4

PD Laws (p.u.) Texture (H2O=1000pu) (x105) 613.54 4.802 605.85 4.714 559.96 5.534 555.35 5.494 537.56 5.801 562.58 5.599 549.45 6.718 451.70 8.252 490.75 6.596 498.86 6.204 483.47 6.334 472.93 6.052 452.33 7.159 500.85 6.215

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Control Exp. 1 Exp. 2 Exp. 3 Exp. 4 Exp. 5 Exp. 6 Exp. 7 Exp. 8 Exp. 9 Exp. 10 Exp. 11 Exp. 12 Exp. 13

Weeks on Digital Image ADC DDC diet Analysis (x10-6 mm2/s) (% Area Fibrosis) 0 3.24 842 1 6.54 967 2 9.03 942 3 14.48 708 4 24.03 723 5 19.97 699 6 28.82 623 7 33.70 516 8 22.42 613 9 19.22 659 10 19.32 596 11 22.32 634 12 25.07 617 13 16.56 664

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Subjective Subjective Fibrosis Inflammation Score Score 0 0 1 3 2 4 3 4 4 4 2 4 4 4 4 2 3 2 2 3 1 2 3 2 3 3 3 3

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Figure 1. Directly acquired image pixel values can be modeled as a single exponential function of echo time. The PD value for a given voxel is obtained from the linear regression of the pixel values logarithms versus TE. To avoid spurious PD calculations, only the echoes with appropriate signal were used as input for the algorithm. In this particular example, the correct fitting is displayed as the white line, while the incorrect fitting is shown as the yellow line.

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Figure 2. Directly acquired spin-echo image with varying TE (3 of the 32 shown) used to generate parametric PD map (rightmost image) of murine liver at 11.7T.

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Figure 3. Exemplary parametric PD maps. (a) Original PD map without the dual-clustering algorithm, and (b) the same PD map after the dual-clustering algorithm, which has removed most of the non-liver parenchyma, including hepatic blood vessels, liver margins, and prominent artifacts.

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Figure 4. Comparison of the same hepatic PD map in this study. (a) Before iterative optimal thresholding. (b) Corresponding image histogram to (a). (c) After iterative optimal thresholding. (d) Corresponding image histogram to (c). Clearly the partial volume artifact is suppressed with minimal impact on the liver tissues themselves. For display purpose only, Figure 4(a) and 4(c) have been resized to 1000-by-1000 to better visualize the image details. Figure 5. Score keys for the subjective histopathologic grading system used in this study: (a) subjective fibrosis scores, and (b) subjective inflammation scores. Figure 6. Masson's trichrome stained murine liver specimen reveals marked fibrosis which is represented by the blue staining areas (a). Colorimetric segmentation is subsequently used to generate a binary mask of the image (b) to determine percentage areas of fibrosis. (Reproduced from [24] with permission). Figure 7. Correlations between degrees of hepatic fibrosis and (a) Laws textures WITH proposed corrections, (b) Laws textures WITHOUT corrections, (c) ADC, (d) T2, and (e) PD. The degree of hepatic fibrosis is measured by percentage area fibrosis based on automatic analysis of digitized whole trichrome stained liver specimens.

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