Faculty Bibliography

BACKGROUND:Diffusion-weighted imaging (DWI) provides insight into the pathophysiology underlying renal dysfunction. Variants of DWI include intravoxel incoherent motion (IVIM), which differentiates between microstructural diffusion and vascular or tubular flow, and diffusion tensor imaging (DTI), which quantifies diffusion directionality. PURPOSE/OBJECTIVE:To investigate the reproducibility of joint IVIM-DTI and compare controls to presurgical renal mass patients. STUDY TYPE/METHODS:Prospective cross-sectional. SUBJECTS/METHODS:Thirteen healthy controls and ten presurgical renal mass patients were scanned. Ten controls were scanned twice to investigate reproducibility. FIELD STRENGTH/SEQUENCE/UNASSIGNED:Subjects were scanned on a 3T system using 10 b-values and 20 diffusion directions for IVIM-DTI in a study approved by the local Institutional Review Board. ASSESSMENT/RESULTS:Retrospective coregistration and measurement of joint IVIM-DTI parameters were performed. STATISTICAL ANALYSIS/METHODS:Parameter reproducibility was defined as intraclass correlation coefficient (ICC) >0.7 and coefficient of variation (CV) <30%. Patient data were stratified by lesion side (contralateral/ipsilateral) for comparison with controls. Corticomedullary differentiation was evaluated. RESULTS:In controls, the reproducible subset of REnal Flow and Microstructure AnisotroPy (REFMAP) parameters had average ICC = 0.82 and CV = 7.5%. In renal mass patients, medullary fractional anisotropy (FA) was significantly lower than in controls (0.227 ± 0.072 vs. 0.291 ± 0.044, P = 0.016 for the kidney contralateral to the mass and 0.228 ± 0.070 vs. 0.291 ± 0.044, P = 0.018 for the kidney ipsilateral). In the kidney ipsilateral to the mass, cortical Dp,radial was significantly higher than in controls (P = 0.012). Conversely, medullary Dp,axial was significantly lower in contralateral than ipsilateral kidneys (P = 0.027) and normal controls (P = 0.044). DATA CONCLUSION/UNASSIGNED:REFMAP-MRI parameters provide unique information regarding renal dysfunction. In presurgical renal mass patients, directional flow changes were noted that were not identified with IVIM analysis alone. Both contralateral and ipsilateral kidneys in patients show reductions in structural diffusivities and anisotropy, while flow metrics showed opposing changes in contralateral vs. ipsilateral kidneys. LEVEL OF EVIDENCE/METHODS:2 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018.

PURPOSE/OBJECTIVE:F-THK5351. The conventional standardized uptake value ratio (SUVR) method relies on the average uptake from an unaffected tissue sample, and therefore is susceptible to biases from off-target binding as well as variability among individuals in the reference region. We propose a new method, standardized uptake value peak-alignment (SUVP), to reduce the bias of the SUVR, and improve the quantitative assessment of tau deposition. METHODS:F-AV-1451 on two independent cohorts (N = 18 and 32, respectively), each with cognitively normal (NL) subjects and Alzheimer's disease (AD) subjects. RESULTS:F-AV-1451). In the cerebellar cortex, an AD-NL group difference with effect size (Cohen's d) of 0.62 was observed for AV-1451, confirming the limitation of the SUVR approach using this region as a reference. A smaller cerebellar effect size (0.09) was observed for THK5351. CONCLUSION/CONCLUSIONS:The SUVP method reduces the bias of the reference region and improves the NL-AD classification compared to the SUVR approach.

Purpose Our aim was to evaluate the accuracy of a new algorithm to automatically delineate the breast region from the chest on T1-weighted, non-fat-suppressed MR images. This process is also referred to as the chest wall detection. There is a general agreement that this step is very difficult to automate. At the same time it is crucially needed for clinically important processing workflows [1]. These workflows include 3D measurement of breast density and of the breast parenchymal enhancement. Both measures reveal patients at risk of breast cancer [2]. Manually traced chest wall was used as the ground truth when estimating the segmentation errors. Segmentation accuracy was evaluated using the Hausdorff distance and the volumetric error. We also estimated the inter-observer agreement in defining the chest wall surface. Methods The program starts by generating the mid-sagittal 2D section by averaging the signal across 20 mm thick mid-sagittal slab. We determine the chest wall boundary on this image by modeling the signal profiles along the antero-posterior direction as a sequence of three tissues: background air, skin and fat layer, muscle. Non-uniformity correction is then applied to the entire 3D volume. The mid-sagittal boundary, represented as a polyline P, is then propagated in two opposite (left and right) directions. At each sagittal section the algorithm adjusts the control points of the polyline received from an adjacent slice. The adjustment is estimated from the weighted sum of six measures that combine specific local and global signal statistics. These include: the local gradient, the signal uniformity, the gradient similarity, the contour-gradient consistency, the global contour uniformity and the normal vector consistency. At each iteration we form a candidate shift vector, we apply it to shift P to its new position, and then we smooth the resulting polyline. The process terminates when the magnitude of the shift becomes negligible or when the specified number of iterations is exceeded. Two metrics were used to estimate accuracy. The conventional volumetric error was obtained by dividing the volume DV of misclassified breast voxels over the true breast volume V. The Hausdorff distance, HD, is the distance between each voxel on the true breast/ chest wall border and the closest boundary voxel produced by the algorithm. HD is averaged over the entire chest wall surface. From a clinical database of screening breast MRIs acquired at our medical center we have randomly selected 16 test exams. The selection was constrained to enforce that there were four exams in each of the four breast density categories [3]. Bilateral breasts were imaged on Siemens 3T Magnetom Trio equipped with a 7-element surface breast coil. The parameters of the T1-weighted non-fat-suppressed sequence were: TR = 4.74 ms, TE = 1.79 ms, FOV = 320 mm2, matrix = 448 9 358 9*150, 0.7 9 0.7 9 1.1 mm voxels, TA = 2-3 min. Three experts in breast and chest anatomy drew contours to separate the chest wall from the breast (Fig. 1). The pectoralis fascia and pectoralis muscles were used as reference points for the anterolateral borders. The medial border of the axilla was the posterolateral boundary. The axillary tail was considered as the breast tissue. The ground truth references were constructed by a software designed to perform voxel-based ROI averaging [4]. (Figure Presented) Results The border distance error HD was 0.84 +/- 0.8 mm (average +/- standard deviation) and ranged from 0.57 to 2.45 mm. The volume error DV/V was 6.43 +/- 6.82%. There was no correlation between the HD and DV/V (R2 = 0.23, p = 0.12). The test cases covered a wide range 411-3439 ml of breast volumes. There was a significant positive correlation (R2 = 0.40, p = 0.02) between volumetric error and the true breast volume V, but there was no correlation between HD and V (R2 = 0.08, p = 0.44). The average execution time was under 1.5 min per case on a standard 8-core workstation. The inter-observer agreement measured in term of HD was 0.56 +/- 0.15 mm (average +/- standard deviation). The agreement expressed in terms of volumetric discrepancy (relative to breast volume) was 1.61% +/- 0.71%. Conclusion Breast density, defined as fraction of fibroglandular tissue, and postcontrast enhancement, are considered significant risk factors for breast cancer. These MRI measures are recommended for radiologic reports and are promising cancer biomarkers. Radiologists currently visually estimate these measure. Unfortunately, readers agreement for qualitative evaluation is only fair, requiring better standardization and reproducibility. Computer-assisted quantitative assessment is needed, but the task is challenging due to image nonuniformity (breast coils cause loss of MR signal in remote regions) and to the anatomical complexity of chest wall boundary (Fig. 2). (Figure Presented) Given its accuracy and speed, our breast segmentation method appears to be ready for clinical use as a part of larger workflow to generate routine diagnostic reports

BACKGROUND: Potential clinical implications of the level of background parenchymal enhancement (BPE) on breast MRI are increasing. Currently, BPE is typically evaluated subjectively. Tests of concordance between subjective BPE assessment and computer-assisted quantified BPE have not been reported. PURPOSE OR HYPOTHESIS: To compare subjective radiologist assessment of BPE with objective quantified parenchymal enhancement (QPE). STUDY TYPE: Cross-sectional observational study. POPULATION: Between 7/24/2015 and 11/27/2015, 104 sequential patients (ages 23 - 81 years, mean 49 years) without breast cancer underwent breast MRI and were included in this study. FIELD STRENGTH/SEQUENCE: 3T; fat suppressed axial T2, axial T1, and axial fat suppressed T1 before and after intravenous contrast. ASSESSMENT: Four breast imagers graded BPE at 90 and 180 s after contrast injection on a 4-point scale (a-d). Fibroglandular tissue masks were generated using a phantom-validated segmentation algorithm, and were co-registered to pre- and postcontrast fat suppressed images to define the region of interest. QPE was calculated. STATISTICAL TESTS: Receiver operating characteristic (ROC) analyses and kappa coefficients (k) were used to compare subjective BPE with QPE. RESULTS: ROC analyses indicated that subjective BPE at 90 s was best predicted by quantified QPE 50.0 = d, and at 180 s by quantified QPE 74.5 = d. Agreement between subjective BPE and QPE was slight to fair at 90 s (k = 0.20-0.36) and 180 s (k = 0.19-0.28). At higher levels of QPE, agreement between subjective BPE and QPE significantly decreased for all four radiologists at 90 s (P