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Purpose To develop a fast volumetric T1 mapping technique. Materials and methods A stack-of-stars (SOS) Look Locker technique based on the acquisition of undersampled radial data (>30× relative to Nyquist) and an efficient multi-slab excitation scheme is presented. A principal-component based reconstruction is used to reconstruct T1 maps. Computer simulations were performed to determine the best choice of partitions per slab and degree of undersampling. The technique was validated in phantoms against reference T1 values measured with a 2D Cartesian inversion-recovery spin-echo technique. The SOS Look Locker technique was tested in brain (n = 4) and prostate (n = 5). Brain T1 mapping was carried out with and without kz acceleration and results between the two approaches were compared. Prostate T1 mapping was compared to standard techniques. A reproducibility study was conducted in brain and prostate. Statistical analyses were performed using linear regression and Bland Altman analysis. Results Phantom T1 values showed excellent correlations between SOS Look Locker and the inversion-recovery spin-echo reference (r2 = 0.9965; p Conclusion A rapid volumetric T1 mapping technique was developed. The technique enables high-resolution T1 mapping with adequate anatomical coverage in a clinically acceptable time.

A deep learning MR parameter mapping framework which combines accelerated radial data acquisition with a multi-scale residual network (MS-ResNet) for image reconstruction is proposed. The proposed supervised learning strategy uses input image patches from multi-contrast images with radial undersampling artifacts and target image patches from artifact-free multi-contrast images. Subspace filtering is used during pre-processing to denoise input patches. For each anatomy and relaxation parameter, an individual network is trained. in vivo T(1) mapping results are obtained on brain and abdomen datasets and in vivo T(2) mapping results are obtained on brain and knee datasets. Quantitative results for the T(2) mapping of the knee show that MS-ResNet trained using either fully sampled or undersampled data outperforms conventional model-based compressed sensing methods. This is significant because obtaining fully sampled training data is not possible in many applications. in vivo brain and abdomen results for T(1) mapping and in vivo brain results for T(2) mapping demonstrate that MS-ResNet yields contrast-weighted images and parameter maps that are comparable to those achieved by model-based iterative methods while offering two orders of magnitude reduction in reconstruction times. The proposed approach enables recovery of high-quality contrast-weighted images and parameter maps from highly accelerated radial data acquisitions. The rapid image reconstructions enabled by the proposed approach makes it a good candidate for routine clinical use.

BACKGROUND AND PURPOSE: Fast and accurate quantification of globe volumes in the event of an ocular trauma can provide clinicians with valuable diagnostic information. In this work, an automated workflow using a deep learning-based convolutional neural network is proposed for prediction of globe contours and their subsequent volume quantification in CT images of the orbits. MATERIALS AND METHODS: An automated workflow using a deep learning -based convolutional neural network is proposed for prediction of globe contours in CT images of the orbits. The network, 2D Modified Residual UNET (MRes-UNET2D), was trained on axial CT images from 80 subjects with no imaging or clinical findings of globe injuries. The predicted globe contours and volume estimates were compared with manual annotations by experienced observers on 2 different test cohorts. RESULTS: On the first test cohort (n = 18), the average Dice, precision, and recall scores were 0.95, 96%, and 95%, respectively. The average 95% Hausdorff distance was only 1.5 mm, with a 5.3% error in globe volume estimates. No statistically significant differences (P = .72) were observed in the median globe volume estimates from our model and the ground truth. On the second test cohort (n = 9) in which a neuroradiologist and 2 residents independently marked the globe contours, MRes-UNET2D (Dice = 0.95) approached human interobserver variability (Dice = 0.94). We also demonstrated the utility of inter-globe volume difference as a quantitative marker for trauma in 3 subjects with known globe injuries. CONCLUSIONS: We showed that with fast prediction times, we can reliably detect and quantify globe volumes in CT images of the orbits across a variety of acquisition parameters.

INTRODUCTION: The ability to assess skeletal muscle adipose tissue is important given the negative clinical implications associated with greater fat infiltration of the muscle. Computed tomography and magnetic resonance imaging (MRI) are highly accurate for measuring appendicular soft tissue and muscle composition, but have limitations. Peripheral quantitative computed tomography (pQCT) is an alternative that investigators find valuable because of its low radiation, fast scan time, and comparatively lower costs. The present investigation sought to assess the accuracy of pQCT-derived estimates of total-, subcutaneous-, skeletal muscle-, intermuscular-, and calculated intramuscular-adipose tissue areas, and muscle density in the mid-thigh of young girls using the gold standard, 3 T MRI, as the criterion. METHODOLOGY: Cross-sectional data were analyzed for 26 healthy girls aged 9 to 12 years. Mid-thigh soft tissue composition was assessed by both pQCT and 3 T MRI. Mean tissue area for corresponding adipose compartments by pQCT and MRI was compared using t-tests, regression analysis, and Bland-Altman plots. Muscle density was regressed on MRI skeletal muscle adipose tissue, intermuscular adipose tissue, and intra-muscular adipose tissue, each expressed as a percentage of total muscle area. RESULTS: Correlations were high between MRI and pQCT for total adipose tissue (r(2)=0.98), subcutaneous adipose tissue (r(2)=0.95), skeletal muscle adipose tissue (r(2)=0.83), and intermuscular adipose tissue (r(2)=0.82), and pQCT muscle density correlated well with both MRI skeletal muscle adipose tissue (r(2)=0.70) and MRI intermuscular adipose tissue (r(2)=0.70). There was a slight, but statistically significant underestimation by pQCT for total and subcutaneous adipose tissue, while no significant difference was observed for skeletal muscle adipose tissue. Both pQCT-estimated intramuscular adipose tissue and muscle density were weakly correlated with MRI-intramuscular adipose tissue. CONCLUSIONS: We conclude that pQCT is a valid measurement technique for estimating all adipose sub-compartments, except for intramuscular adipose tissue, for the mid-thigh region in young/adolescent girls.

BACKGROUND AND PURPOSE: Accurate and reliable detection of white matter hyperintensities and their volume quantification can provide valuable clinical information to assess neurologic disease progression. In this work, a stacked generalization ensemble of orthogonal 3D convolutional neural networks, StackGen-Net, is explored for improving automated detection of white matter hyperintensities in 3D T2-FLAIR images. MATERIALS AND METHODS: Individual convolutional neural networks in StackGen-Net were trained on 2.5D patches from orthogonal reformatting of 3D-FLAIR (n = 21) to yield white matter hyperintensity posteriors. A meta convolutional neural network was trained to learn the functional mapping from orthogonal white matter hyperintensity posteriors to the final white matter hyperintensity prediction. The impact of training data and architecture choices on white matter hyperintensity segmentation performance was systematically evaluated on a test cohort (n = 9). The segmentation performance of StackGen-Net was compared with state-of-the-art convolutional neural network techniques on an independent test cohort from the Alzheimer’s Disease Neuroimaging Initiative-3 (n = 20). RESULTS: StackGen-Net outperformed individual convolutional neural networks in the ensemble and their combination using averaging or majority voting. In a comparison with state-of-the-art white matter hyperintensity segmentation techniques, StackGen-Net achieved a significantly higher Dice score (0.76 [SD, 0.08], F1-lesion (0.74 [SD, 0.13]), and area under precision-recall curve (0.84 [SD, 0.09]), and the lowest absolute volume difference (13.3% [SD, 9.1%]). StackGen-Net performance in Dice scores (median = 0.74) did not significantly differ (P = .22) from interobserver (median = 0.73) variability between 2 experienced neuroradiologists. We found no significant difference (P = .15) in white matter hyperintensity lesion volumes from StackGen-Net predictions and ground truth annotations. CONCLUSIONS: A stacked generalization of convolutional neural networks, utilizing multiplanar lesion information using 2.5D spatial context, greatly improved the segmentation performance of StackGen-Net compared with traditional ensemble techniques and some state-of-the-art deep learning models for 3D-FLAIR.

Background T1 mapping is often used in some clinical protocols. Existing techniques are limited in slice coverage, and/or spatial-temporal resolution, or require long acquisitions. Here we present a multi-slice inversion-recovery (IR) radial steady-state free precession (radSSFP) pulse sequence combined with a principal component (PC) based reconstruction that overcomes these limitations. Purpose To develop a fast technique for multi-slice high-resolution T1 mapping. Study type Technical efficacy study done prospectively. Phantom/subjects IR-radSSFP was tested in phantoms, five healthy volunteers, and four patients with abdominal lesions. Field strength/sequence IR-radSSFP was implemented at 3T. Assessment Computer simulations were performed to optimize the flip angle for T1 estimation; testing was done in phantoms using as reference an IR spin-echo pulse sequence. T1 mapping with IR-radSSFP was also assessed in vivo (brain and abdomen) and T1 values were compared with literature. T1 maps were also compared with a radial IR-FLASH technique. Statistical tests A two-tailed t-test was used to compare T1 values in phantoms. A repeatability study was carried out in vivo using Bland-Altman analysis. Results Simulations and phantom experiments showed that a flip angle of 20˚ was optimal for T1 mapping. When comparing single to multi-slice experiments in phantoms there were no significant differences between the means T1 values (P = 0.0475). In vivo results show that T1 maps with spatial resolution as high as 0.69 mm × 0.69 mm × 2.00 mm (brain) and 0.83 mm × 0.83 mm × 3.00 mm (abdomen) can be generated for 84 brain slices in 3 min and 10 abdominal slices in a breath-hold; T1 values were comparable to those reported in literature. The coefficients of variation from the repeatability study were 1.7% for brain and 2.5-2.7% in the abdomen. Data conclusion A multi-slice IR-radSSFP technique combined with a PC-based reconstruction was demonstrated for higher resolution T1 mapping. This technique is fast, motion-insensitive and yields repeatable T1 values comparable to those in literature. Level of evidence 2 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2019;49:239-252.

Technology and Research Initiative Fund (TRIF) - Improving Health and Arizona Biomedical Research Commission (ABRC) [ADHS14-082996]

BACKGROUND Increased breast density is a significant independent risk factor for breast cancer, and recent studies show that this risk is modifiable. Hence, breast density measures sensitive to small changes are desired. PURPOSE Utilizing fat-water decomposition MRI, we propose an automated, reproducible breast density measurement, which is nonionizing and directly comparable to mammographic density (MD). STUDY TYPE Retrospective study. POPULATION The study included two sample sets of breast cancer patients enrolled in a clinical trial, for concordance analysis with MD (40 patients) and reproducibility analysis (10 patients). FIELD STRENGTH/SEQUENCE The majority of MRI scans (59 scans) were performed with a 1.5T GE Signa scanner using radial IDEAL-GRASE sequence, while the remaining (seven scans) were performed with a 3T Siemens Skyra using 3D Cartesian 6-echo GRE sequence with a similar fat-water separation technique. ASSESSMENT After automated breast segmentation, breast density was calculated using FraGW, a new measure developed to reliably reflect the amount of fibroglandular tissue and total water content in the entire breast. Based on its concordance with MD, FraGW was calibrated to MR-based breast density (MRD) to be comparable to MD. A previous breast density measurement, Fra80-the ratio of breast voxels with

Purpose To develop a novel multiresolution MRI methodology for accurate estimation of glomerular filtration rate (GFR) in vivo. Materials and methods A three-dimensional golden-angle radial stack-of-stars (SoS) trajectory was used for data acquisition on a 3 Tesla MRI scanner. Multiresolution reconstruction and analysis was performed using arterial input function reconstructed at 1-s. temporal resolution and renal dynamic data reconstructed using compressed sensing (CS) with 4-s temporal resolution. The method was first validated using simulations and the clinical utility of the technique was evaluated by comparing the GFR estimates from the proposed method to the estimated GFR (eGFR) obtained from serum creatinine for 10 subjects. Results The 4-s temporal resolution CS images minimized streaking artifacts and noise while the 1-s temporal resolution AIF minimized errors in GFR estimates. A paired t-test showed that there was no statistically significant difference between MRI based total GFR values and serum creatinine based eGFR estimates (P = 0.92). Conclusion We have demonstrated the feasibility of multiresolution MRI using a golden angle radial stack-of-stars scheme to accurately estimate GFR as well as produce diagnostic quality dynamic images in vivo. Level of evidence 1 Technical Efficacy: Stage 3 J. MAGN. RESON. IMAGING 2017;46:303-311.

Subspace-constrained reconstruction methods restrict the relaxation signals (of size M) in the scene to a pre-determined subspace (of size K≪M) and allow multi-contrast imaging and parameter mapping from accelerated acquisitions. However, these constraints yield poor image quality at some imaging contrasts, which can impact the parameter mapping performance. Additional regularization such as the use of joint-sparse (JS) or locally-low-rank (LLR) constraints can help improve the recovery of these images but are not sufficient when operating at high acceleration rates. We propose a method, non-local rank 3D (NLR3D), that is built on block matching and transform domain low rank constraints to allow high quality recovery of subspace-coefficient images (SCI) and subsequent multi-contrast imaging and parameter mapping. The performance of NLR3D was evaluated using Monte-Carlo (MC) simulations and compared against the JS and LLR methods. In vivo T 2 mapping results are presented on brain and knee datasets. MC results demonstrate improved bias, variance, and MSE behavior in both the multi-contrast images and parameter maps when compared to the JS and LLR methods. In vivo brain and knee results at moderate and high acceleration rates demonstrate improved recovery of high SNR early TE images as well as parameter maps. No significant difference was found in the T2 values measured in ROIs between the NLR3D reconstructions and the reference images (Wilcoxon signed rank test). The proposed method, NLR3D, enables recovery of high-quality SCI and, consequently, the associated multi-contrast images and parameter maps.

PURPOSE: To design a pulse sequence for efficient 3D T2-weighted imaging and T2 mapping. METHODS: A stack-of-stars turbo spin echo pulse sequence with variable refocusing flip angles and a flexible pseudorandom view ordering is proposed for simultaneous T2-weighted imaging and T2 mapping. An analytical framework is introduced for the selection of refocusing flip angles to maximize relative tissue contrast while minimizing T2 estimation errors and maintaining low specific absorption rate. Images at different echo times are generated using a subspace constrained iterative reconstruction algorithm. T2 maps are obtained by modeling the signal evolution using the extended phase graph model. The technique is evaluated using phantoms and demonstrated in vivo for brain, knee, and carotid imaging. RESULTS: Numerical simulations demonstrate an improved point spread function with the proposed pseudorandom view ordering compared to golden angle view ordering. Phantom experiments show that T2 values estimated from the stack-of-stars turbo spin echo pulse sequence with variable refocusing flip angles have good concordance with spin echo reference values. In vivo results show the proposed pulse sequence can generate qualitatively comparable T2-weighted images as conventional Cartesian 3D SPACE in addition to simultaneously generating 3D T2 maps. CONCLUSION: The proposed stack-of-stars turbo spin echo pulse sequence with pseudorandom view ordering and variable refocusing flip angles allows high resolution isotropic T2 mapping in clinically acceptable scan times. The optimization framework for the selection of refocusing flip angles improves T2 estimation accuracy while generating T2-weighted contrast comparable to conventional Cartesian imaging.

Background Double inversion recovery (DIR) fast spin-echo (FSE) cardiovascular magnetic resonance (CMR) sequences are used clinically for black-blood T2-weighted imaging. However, these sequences suffer from slice inefficiency due to the non-selective inversion pulses. We propose a multi-band (MB) encoded DIR radial FSE (MB-DIR-RADFSE) technique to simultaneously excite two slices. This sequence has improved signal-to-noise ratio per unit time compared to a single slice excitation. It is also motion robust and enables the reconstruction of high-resolution black-blood T2-weighted images and T2 maps for the excited slices. Methods Hadamard encoded MB pulses were used in MB-DIR-RADFSE to simultaneously excite two slices. A principal component based iterative reconstruction was used to jointly reconstruct black-blood T2-weighted images and T2 maps. Phantom and in vivo experiments were performed to evaluate T2 mapping performance and results were compared to a T2-prepared balanced steady state free precession (bSSFP) method. The inter-segment variability of the T2 maps were assessed using data acquired on healthy subjects. A reproducibility study was performed to evaluate reproducibility of the proposed technique. Results Phantom experiments show that the T2 values estimated from MB-DIR-RADFSE are comparable to the spin-echo based reference, while T2-prepared bSSFP over-estimated T2 values. The relative contrast of the black-blood images from the multi-band scheme was comparable to those from a single slice acquisition. The myocardial segment analysis on 8 healthy subjects indicated a significant difference (p-value

PURPOSE: A new method for streak artifact reduction in radial MRI based on phased array filtering. THEORY: Radial imaging in applications that require large fields-of-view can be susceptible to streaking artifacts due to gradient nonlinearities. Coil removal methods prune the coils contributing the most to streaking artifacts at the expense of signal loss. Phased array beamforming is a form of spatial filtering used to suppress unwanted signals. The proposed method uses interference covariance generated from the streaking artifact samples which are manually extracted with phased array beamforming to suppress streaking in the images. METHODS: The performance of the proposed method was evaluated on abdomen radial fast spin echo images acquired on a 1.5T Siemens scanner and compared with previously proposed methods. RESULTS: Our results demonstrate that the proposed method can effectively suppress streaking artifacts without any noticeable loss in signal levels. Coil removal methods can suppress streaks as well but they may incur significant signal loss due to coil pruning. Quantitative metrics also demonstrate the superiority of the proposed method over earlier methods. CONCLUSION: The use of interference covariance with phased array beamforming can help reduce streaking artifacts.

Purpose Lung function is typically characterized by spirometer measurements, which do not offer spatially specific information. Imaging during exhalation provides spatial information but is challenging due to large movement over a short time. The purpose of this work is to provide a solution to lung imaging during forced expiration using accelerated magnetic resonance imaging. The method uses radial golden angle stack-of-stars gradient echo acquisition and compressed sensing reconstruction. Methods A technique for dynamic three-dimensional imaging of the lungs from highly undersampled data is developed and tested on six subjects. This method takes advantage of image sparsity, both spatially and temporally, including the use of reference frames called bookends. Sparsity, with respect to total variation, and residual from the bookends, enables reconstruction from an extremely limited amount of data. Results Dynamic three-dimensional images can be captured at sub-150 ms temporal resolution, using only three (or less) acquired radial lines per slice per timepoint. The images have a spatial resolution of mm. Lung volume calculations based on image segmentation are compared to those from simultaneously acquired spirometer measurements. Conclusion Dynamic lung imaging during forced expiration is made possible by compressed sensing accelerated dynamic three-dimensional radial magnetic resonance imaging. Magn Reson Med, 2015. © 2015 Wiley Periodicals, Inc.

BACKGROUND AND PURPOSE: TSE-based T2-weighted imaging of the spine has long scan times. This work proposes a fast imaging protocol using variable refocusing flip angles, optimized for blurring and specific absorption rate. MATERIALS AND METHODS: A variable refocusing flip angle echo-train was optimized for the spine to improve the point spread function and minimize the specific absorption rate, yielding images with improved spatial resolution and SNR compared with the constant flip angle sequence. Data were acquired from 51 patients (35 lumbar, 16 whole-spine) using conventional TSE and the proposed sequence, with a single-shot variant for whole-spine. Noninferiority analysis was performed to evaluate the efficiency of the proposed technique. RESULTS: The proposed multishot sequence resulted in a 2× shorter scan time with a >1.5× lower specific absorption rate. The variable flip angle sequence was noninferior to the conventional TSE (P < .025) for all image-quality and clinical criteria except signal-to-noise ratio for the lumbar spine protocol. However, mean image scores for the TSE-variable refocusing flip angle were ≥4.3 for all criteria, and concordance analysis showed high agreement (>90%) with the TSE, indicating clinical equivalence. The single-shot sequence resulted in 4× shorter whole-spine scans, and image scores were ≥4.4 for all criteria, attesting to its clinical utility. CONCLUSIONS: We present a fast T2-weighted spine protocol using variable refocusing flip angles, including a single-shot variant. The sequences have better point spread function behavior than their constant flip angle counterparts and, being faster, should be less sensitive to patient motion, often seen in the longer TSE scans.

The four chamber plane is currently underutilized in the right ventricular segmentation community. Four chamber information can be useful to determine ventricular short axis stacks and provide a rough estimate of the right ventricle in short axis stacks. In this study, we develop and test a semi-automated technique for segmenting the right ventricle in four chamber cine cardiac magnetic resonance images. The three techniques that use minimum cost path algorithms were used. The algorithms are: Dijkstra's shortest path algorithm (Dijkstra), an A* algorithm that uses length, curvature and torsion into an active contour model (ALCT), and a variation of polar dynamic programming (PDP). The techniques are evaluated against the expert traces using 175 cardiac images from 7 patients. The evaluation first looks at mutual overlap metrics and then focuses on clinical measures such as fractional area change (FAC). The mean mutual overlap between the physician's traces ranged from 0.85 to 0.88. Using as reference physician 1's landmarks and traces (i.e., comparing the traces from physician 1 to the semi-automated segmentation using physician 1's landmarks), the PDP algorithm has a mean mutual overlap of 0.8970 compared to 0.8912 for ALCT and 0.8879 for Dijkstra. The mean mutual overlap between the BP regions generated by physician 1 and physician 2 landmarks are 0.9674, 0.9605 and 0.9531 for PDP, ALCT and Dijkstra, respectively. The FAC correlation coefficient between the physician's traces ranged from 0.73 to 0.93.

Diindolylmethane (DIM), a bioactive metabolite of indole-3-carbinol found in cruciferous vegetables, has proposed cancer chemoprevention activity in the breast. There is limited evidence of clinically relevant activity of DIM or long-term safety data of its regular use. A randomized, double-blind, placebo-controlled trial was conducted to determine the activity and safety of combined use of BioResponse DIM® (BR-DIM) with tamoxifen. Women prescribed tamoxifen (n = 130) were randomly assigned oral BR-DIM at 150 mg twice daily or placebo, for 12 months. The primary study endpoint was change in urinary 2/16α-hydroxyestrone (2/16α-OHE1) ratio. Changes in 4-hydroxyestrone (4-OHE1), serum estrogens, sex hormone-binding globulin (SHBG), breast density, and tamoxifen metabolites were assessed. Ninety-eight women (51 placebo, 47 DIM) completed intervention; compliance with treatment was >91%. BR-DIM increased the 2/16α-OHE1 ratio (+3.2 [0.8, 8.4]) compared to placebo (−0.7 [−1.7, 0.8], P

Two-thirds of U.S. adult women are overweight or obese. High body mass index (BMI) and adult weight gain are risk factors for a number of chronic diseases, including postmenopausal breast cancer. The higher postmenopausal breast cancer risk in women with elevated BMI is likely to be attributable to related metabolic disturbances including altered circulating sex steroid hormones and adipokines, elevated pro-inflammatory cytokines, and insulin resistance. Metformin is a widely used antidiabetic drug that has demonstrated favorable effects on metabolic disturbances and as such may lead to lower breast cancer risk in obese women. Further, the anti-proliferative effects of metformin suggest it may decrease breast density, an accepted biomarker of breast cancer risk. This is a Phase II randomized, double-blind, placebo-controlled trial of metformin in overweight/obese premenopausal women who have elements of metabolic syndrome. Eligible participants will be randomized to receive metformin 850 mg BID (n = 75) or placebo (n = 75) for 12 months. The primary endpoint is change in breast density, based on magnetic resonance imaging (MRI) acquired fat-water features. Secondary outcomes include changes in serum insulin levels, serum insulin-like growth factor (IGF)-1 to insulin-like growth factor binding protein (IGFBP)-3 ratio, serum IGF-2 levels, serum testosterone levels, serum leptin to adiponectin ratio, body weight, and waist circumference. Exploratory outcomes include changes in metabolomic profiles in plasma and nipple aspirate fluid. Changes in tissue architecture as well as cellular and molecular targets in breast tissue collected in a subgroup of participants will also be explored. The study will evaluate whether metformin can result in favorable changes in breast density, select proteins and hormones, products of body metabolism, and body weight and composition. The study should help determine the potential breast cancer preventive activity of metformin in a growing population at risk for multiple diseases. ClinicalTrials.gov Identifier: NCT02028221 . Registered on January 2, 2014. Grant #: 1R01CA172444-01A1 awarded on Sept 11, 2013.

Chronic liver disease is a worldwide health problem, and hepatic fibrosis (HF) is one of the hallmarks of the disease. The current reference standard for diagnosing HF is biopsy followed by pathologist examination, however this is limited by sampling error and carries risk of complications. Pathology diagnosis of HF is based on textural change in the liver as a lobular collagen network that develops within portal triads. The scale of collagen lobules is characteristically on order of 1-5 mm, which approximates the resolution limit of in vivo gadolinium-enhanced magnetic resonance imaging in the delayed phase. We have shown that MRI of formalin fixed human ex vivo liver samples mimic the textural contrast of in vivo Gd-MRI and can be used as MRI phantoms. We have developed local texture analysis that is applied to phantom images, and the results are used to train model observers. The performance of the observer is assessed with the area-under-the-receiveroperator- characteristic curve (AUROC) as the figure of merit. To optimize the MRI pulse sequence, phantoms are scanned with multiple times at a range of flip angles. The flip angle that associated with the highest AUROC is chosen as optimal based on the task of detecting HF.

Chronic liver disease is a worldwide health problem, and hepatic fibrosis (HF) is one of the hallmarks of the disease. The current reference standard for diagnosing HF is biopsy followed by pathologist examination; however, this is limited by sampling error and carries a risk of complications. Pathology diagnosis of HF is based on textural change in the liver as a lobular collagen network that develops within portal triads. The scale of collagen lobules is characteristically in the order of 1 to 5 mm, which approximates the resolution limit of in vivo gadolinium-enhanced magnetic resonance imaging in the delayed phase. We use MRI of formalin-fixed human ex vivo liver samples as phantoms that mimic the textural contrast of in vivo Gd-MRI. We have developed a local texture analysis that is applied to phantom images, and the results are used to train model observers to detect HF. The performance of the observer is assessed with the area-under-the-receiver–operator-characteristic curve (AUROC) as the figure-of-merit. To optimize the MRI pulse sequence, phantoms were scanned with multiple times at a range of flip angles. The flip angle that was associated with the highest AUROC was chosen as optimal for the task of detecting HF.

Rationale and Objectives To develop and test an algorithm that outlines the breast boundaries using information from fat and water magnetic resonance images. Materials and Methods Three algorithms were implemented and tested using registered fat and water magnetic resonance images. Two of the segmentation algorithms are simple extensions of the techniques used for contrast-enhanced images: one algorithm uses clustering and local gradient (CLG) analysis and the other algorithm uses a Hessian-based sheetness filter (HSF). The third segmentation algorithm uses k-means++ and dynamic programming (KDP) for finding the breast pixels. All three algorithms separate the left and right breasts using either a fixed region or a morphological method. The performance is quantified using a mutual overlap (Dice) metric and a pectoral muscle boundary error. The algorithms are evaluated against three manual tracers using 266 breast images from 14 female subjects. Results The KDP algorithm has a mean overlap percentage improvement that is statistically significant relative to the HSF and CLG algorithms. When using a fixed region to remove the tissue between breasts with tracer 1 as a reference, the KDP algorithm has a mean overlap of 0.922 compared to 0.864 (P

Summary A double inversion radial fast spin echo (DIR-RADFSE) has been developed to obtain T2 maps of the heart with high-temporal and spatial resolution from data acquired in as ingle breath hold. The method allows for the quantitative assessment of inflammation in the heart. Background While DE imaging is considered a gold standard in the evaluation of myocardial scar/viability in patients with old MI or cardiomyopathy, a few recent publications demonstrate a higher diagnostic sensitivity of T2weighted techniques in patients with Non-STEMI and myocarditis where changes in T2-weighting are due to inflammation in the myocardium leading to edema (Abdel-Aty H, JACC 53:1194, 2009; Tilak GS, Invest