Read-out Segmented Echo Planar Imaging with Two-Dimensional Navigator Correction (RESOLVE): An Alternative Sequence to Improve Image Quality on Diffusion-Weighted Imaging of Prostate.

OBJECTIVE: We aimed to investigate if the use of read-out segmented echoplanar imaging with additional two-dimensional navigator correction (Readout Segmentation of Long Variable Echo, RESOLVE) for acquiring prostate diffusion-weighted imaging (DWI) improves image quality, compared to single-shot echoplanar imaging (ss-EPI). METHODS: This single-center prospective study cohort included 162 males with suspected prostate cancer, who underwent 3 Tesla multiparametric MRI (3T-mpMRI). Two abdominal radiologists, blinded to the clinical information, separately reviewed each 3T-mpMRI study to rank geometrical distortion, degree of rectal distention, lesion conspicuity, and anatomic details delineation first on ss-EPI-DWI and later on RESOLVE-DWI using 5-point scales (1 = excellent, 5 = poor). The average of the ranking scores given by two readers was generated and used as the final score. RESULTS: There was good-to-excellent interreader agreement for scoring image quality parameters on both ss-EPI and RESOLVE. Geometrical distortion scores > 3 was seen in 12.3% (20/162) of ss-EPI images, with all having geometrical distortion score <3 on RESOLVE (p < .001). The mean image distortion score was significantly less on RESOLVE than ss-EPI (1.16 vs 1.61, p < .01 regardless of rectal gas, p< .05 when stratified by the degree of rectal distention ). RESOLVE was superior to ss-EPI for lesion conspicuity (mean 1.35 vs 1.53, p< .002) and anatomic delineation (2.60 vs 2.68, p< .001) of prostate on DWI. CONCLUSION: Compared to conventional ss-EPI, the use of RESOLVE for acquisition of prostate DWI resulted in significantly enhanced image quality and reduced geometrical distortion. ADVANCES IN KNOWLEDGE: RESOLVE could be an alternative or replacement of ss-EPI for acquiring prostate DWI with significantly less geometrical distortion and significantly improved lesion conspicuity and anatomic delineation.

Three Tesla Multiparametric Magnetic Resonance Imaging: Comparison of Performance with and without Endorectal Coil for Prostate Cancer Detection, PI-RADS™ version 2 Category and Staging with Whole Mount Histopathology Correlation.

PURPOSE: We investigated the performance of 3 Tesla multiparametric magnetic resonance imaging with and without an endorectal coil to detect prostate cancer with a whole mount histopathology reference. MATERIALS AND METHODS: This retrospective HIPAA (Health Insurance Portability and Accountability Act) compliant, institutional review board approved, case-control study included patients who underwent 3 Tesla multiparametric magnetic resonance imaging with and without an endorectal coil from July 2009 to December 2016 prior to prostatectomy. The tumor detection rate was calculated for total and index lesions. Lesion magnetic resonance imaging and histopathology features were compared between the 2 groups. Using SPSS®, version 24 p <0.05 was considered significant. RESULTS: A total of 871 whole mount histopathology lesions in 429 patients with a mean ± SD age of 61.8 ± 7 years were included in analysis. The subcohorts with and without an endorectal coil comprised 260 and 169 patients with a total of 529 and 342 lesions, respectively. The overall tumor detection rates in all patients, and in the endorectal coil and nonendorectal coil subcohorts were 49.6% (432 of 871 patients), 50.5% (267 of 529) and 48.2% (165 of 342), respectively. The index tumor detection rates overall, and in the endorectal coil and nonendorectal coil subcohorts were 77.6% (333 of 429 patients), 78.5% (204 of 260) and 76.3% (129 of 169), respectively. In the endorectal coil and nonendorectal coil subcohorts we detected 35.9% (66 of 184) and 48.4% (76 of 157) of anterior lesions (p = 0.019), 58% (200 of 345) and 48.1% (89 of 185) of posterior lesions (p = 0.025), 37.3% (41 of 110) and 54.4% (62 of 114) of transition zone lesions (p = 0.010), and 53.7% (225 of 419) and 45.2% (103 of 228) of peripheral lesions (p = 0.033), respectively. After adjusting for clinical and pathological factors the endorectal coil group only showed higher detection of peripheral and posterior prostate cancer. CONCLUSIONS: We found that 3 Tesla multiparametric magnetic resonance imaging with and without an endorectal coil had similar detection of overall and index prostate cancer. However, the endorectal coil subcohort had significantly higher detection of posterior and peripheral prostate cancer, and lower detection of anterior and transition zone prostate cancer.

In-Bore 3-T MR-guided Transrectal Targeted Prostate Biopsy: Prostate Imaging Reporting and Data System Version 2-based Diagnostic Performance for Detection of Prostate Cancer.

Purpose To determine the diagnostic yield of in-bore 3-T magnetic resonance (MR) imaging-guided prostate biopsy and stratify performance according to Prostate Imaging Reporting and Data System (PI-RADS) versions 1 and 2. Materials and Methods This study was HIPAA compliant and institution review board approved. In-bore 3-T MR-guided prostate biopsy was performed in 134 targets in 106 men who (a) had not previously undergone prostate biopsy, (b) had prior negative biopsy findings with increased prostate-specific antigen (PSA) level, or (c) had a prior history of prostate cancer with increasing PSA level. Clinical, diagnostic 3-T MR imaging was performed with in-bore guided prostate biopsy, and pathology data were collected. The diagnostic yields of MR-guided biopsy per patient and target were analyzed, and differences between biopsy targets with negative and positive findings were determined. Results of logistic regression and areas under the curve were compared between PI-RADS versions 1 and 2. Results Prostate cancer was detected in 63 of 106 patients (59.4%) and in 72 of 134 targets (53.7%) with 3-T MR imaging. Forty-nine of 72 targets (68.0%) had clinically significant cancer (Gleason score ≥ 7). One complication occurred (urosepsis, 0.9%). Patients who had positive target findings had lower apparent diffusion coefficient values (875 × 10-6 mm2/sec vs 1111 × 10-6 mm2/sec, respectively; P < .01), smaller prostate volume (47.2 cm3 vs 75.4 cm3, respectively; P < .01), higher PSA density (0.16 vs 0.10, respectively; P < .01), and higher proportion of PI-RADS version 2 category 3-5 scores when compared with patients with negative target findings. MR targets with PI-RADS version 2 category 2, 3, 4, and 5 scores had a positive diagnostic yield of three of 23 (13.0%), six of 31 (19.4%), 39 of 50 (78.0%), and 24 of 29 (82.8%) targets, respectively. No differences were detected in areas under the curve for PI-RADS version 2 versus 1. Conclusion In-bore 3-T MR-guided biopsy is safe and effective for prostate cancer diagnosis when stratified according to PI-RADS versions 1 and 2. ©RSNA, 2016.

Renal compartment segmentation in DCE-MRI images.

Renal compartment segmentation from Dynamic Contrast-Enhanced MRI (DCE-MRI) images is an important task for functional kidney evaluation. Despite advancement in segmentation methods, most of them focus on segmenting an entire kidney on CT images, there still lacks effective and automatic solutions for accurate segmentation of internal renal structures (i.e. cortex, medulla and renal pelvis) from DCE-MRI images. In this paper, we introduce a method for renal compartment segmentation which can robustly achieve high segmentation accuracy for a wide range of DCE-MRI data, and meanwhile requires little manual operations and parameter settings. The proposed method consists of five main steps. First, we pre-process the image time series to reduce the motion artifacts caused by the movement of the patients during the scans and enhance the kidney regions. Second, the kidney is segmented as a whole based on the concept of Maximally Stable Temporal Volume (MSTV). The proposed MSTV detects anatomical structures that are homogeneous in the spatial domain and stable in terms of temporal dynamics. MSTV-based kidney segmentation is robust to noises and does not require a training phase. It can well adapt to kidney shape variations caused by renal dysfunction. Third, voxels in the segmented kidney are described by principal components (PCs) to remove temporal redundancy and noises. And then k-means clustering of PCs is applied to separate voxels into multiple clusters. Fourth, the clusters are automatically labeled as cortex, medulla and pelvis based on voxels' geometric locations and intensity distribution. Finally, an iterative refinement method is introduced to further remove noises in each segmented compartment. Experiments on 14 real clinical kidney datasets and 12 synthetic dataset demonstrate that results produced by our method match very well with those segmented manually and the performance of our method is superior to the other five existing methods.