The purpose of this study was to determine if image distortion is less in prostate MR apparent diffusion coefficient (ADC) maps generated from a reduced-field-of-view (rFOV) diffusion-weighted-imaging (DWI) technique than from a conventional DWI sequence (CONV), and to determine if the rFOV ADC tumor contrast is as high as or better than that of the CONV sequence. Fifty patients underwent a 3T MRI exam. CONV and rFOV (utilizing a 2D, echo-planar, rectangularly-selective RF pulse) sequences were acquired using b=600, 0 s/mm2. Distortion was visually scored 0–4 by three independent observers and quantitatively measured using the difference in rectal wall curvature between the ADC maps and T2-weighted images. Distortion scores were lower with the rFOV sequence (p<0.012, Wilcoxon Signed-Rank Test, n=50), and difference in distortion scores did not differ significantly among observers (p=0.99, Kruskal-Wallis Rank Sum Test). The difference in rectal curvature was less with rFOV ADC maps (26±10%) than CONV ADC maps (34±13%) (p<0.011, student’s t-test). In seventeen patients with untreated, biopsy confirmed prostate cancer, the rFOV sequence afforded significantly higher ADC tumor contrast (44.0%) than the CONV sequence (35.9%), (p<0.0012, student’s t-test). The rFOV sequence yielded significantly decreased susceptibility artifact and significantly higher contrast between tumor and healthy tissue.

The purpose of this study was to characterize prostate cancer (PCa) based on multiparametric MR (mpMR) measures derived from MRI, diffusion, spectroscopy, and dynamic contrast-enhanced (DCE) MRI, and to validate mpMRI in detecting PCa and predicting PCa aggressiveness by correlating mpMRI findings with whole-mount histopathology. Seventy-eight men with untreated PCa received 3 T mpMR scans prior to radical prostatectomy. Cancerous regions were outlined, graded, and cancer amount estimated on whole-mount histology. Regions of interest were manually drawn on T -weighted images based on histopathology. Logistic regression was used to identify optimal combinations of parameters for the peripheral zone and transition zone to separate: (i) benign from malignant tissues; (ii) Gleason score (GS) ≤3 + 3 disease from ≥GS3 + 4; and (iii) ≤ GS3 + 4 from ≥GS4 + 3 cancers. The performance of the models was assessed using repeated fourfold cross-validation. Additionally, the performance of the logistic regression models created under the assumption that one or more modality has not been acquired was evaluated. Logistic regression models yielded areas under the curve (AUCs) of 1.0 and 0.99 when separating benign from malignant tissues in the peripheral zone and the transition zone, respectively. Within the peripheral zone, combining choline, maximal enhancement slope, apparent diffusion coefficient (ADC), and citrate measures for separating ≤GS3 + 3 from ≥GS3 + 4 PCa yielded AUC = 0.84. Combining creatine, choline, and washout slope yielded AUC = 0.81 for discriminating ≤GS3 + 4 from ≥GS4 + 3 disease. Within the transition zone, combining washout slope, ADC, and creatine yielded AUC = 0.93 for discriminating ≤GS3 + 3 and ≥GS3 + 4 cancers. When separating ≤GS3 + 4 from ≥GS4 + 3 PCa, combining choline and washout slope yielded AUC = 0.92. MpMRI provides excellent separation between benign tissues and PCa, and across PCa tissues of different aggressiveness. The final models prominently feature spectroscopy and DCE-derived metrics, underlining their value within a comprehensive mpMRI examination.

The study purpose was to determine whether 5α-reductase inhibitors (5-ARIs) affect the discrimination between low-grade prostate cancer (PCa) and benign tissues on mpMRI. Twenty men with biopsy-proven Gleason 3+3 PCa and 3T mpMRI were studied. Ten patients (Tx) were receiving 5-ARIs for at least a year at scan time. Ten untreated patients (Un) were matched to the treated cohort. For each subject two regions of interest (ROI) representing cancerous and benign tissues were drawn within the peripheral zone of each prostate, MR measures evaluated, and cancer contrast versus benign [Contrast=(MRTumor-MRHealthy)/MRHealthy] calculated. Decreased cancer contrast was noted on T2-weighted images: 0.4 (Un) versus 0.3 (Tx). However, for functional MR measures, a better separation of cancerous and benign tissues was observed in the treated group. Cancer contrast on high-b diffusion weighted images (DWI) was 0.61 (Un) vs. 0.99 (Tx). Logistic regression analysis yielded higher AUC (area under the curve) values for distinguishing cancerous from benign regions in treated subjects on high-b DWI [0.71 (Un), 0.94 (Tx)], maximal enhancement slope [0.95 (Un), 1 (Tx)], peak enhancement [0.84 (Un), 0.93 (Tx)], washout slope [0.78 (Un), 0.99 (Tx)], Ktrans [0.9 (Un), 1 (Tx)], and combined measures [0.86 (Un), 0.99 (Tx)]. Coefficients of variation for MR measures were lower in benign and cancerous tissues in the treated group compared to the untreated group. This study's results suggest an increase in homogeneity of benign and malignant peripheral zone prostatic tissues with 5-ARI exposure, observed as reduced variability of MR measures after treatment. Cancer discrimination was lower with T2-weighted imaging, but was higher with functional MR measures in a 5-ARI-treated cohort compared to controls.

The use of multiparametric MRI scans for the evaluation of men with prostate cancer has increased dramatically and is likely to continue expanding as new developments come to practice. However, it has not yet gained the same level of acceptance of other imaging tests. Partly, this is because of the use of suboptimal protocols, lack of standardization, and inadequate patient preparation. In this manuscript, we describe several practical aspects of prostate MRI that may facilitate the implementation of new prostate imaging programs or the expansion of existing ones.

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Purpose To evaluate a semi-automatic software-based method of registering in vivo prostate magnetic resonance (MR) images to digital histopathology images using two approaches: 1) in which the prostates were molded to simulate distortion due to the endorectal imaging coil prior to fixation, and 2) in which the prostates were not molded. Materials and Methods T2-weighted MR images and digitized whole-mount histopathology images were acquired for twenty-six patients with biopsy-confirmed prostate cancer who underwent radical prostatectomy. Ten excised prostates were molded prior to fixation. A semi-automatic method was used to align MR images to histopathology. Percent overlap between MR and histopathology images, as well as distances between corresponding anatomical landmarks were calculated and used to evaluate the registration technique for molded and unmolded cases. Results The software successfully morphed histology-based prostate images into corresponding MR images. Percent overlap improved from 80.4±5.8% prior to morphing to 99.7±0.62% post morphing. Molded prostates had a smaller distance between landmarks (1.91±0.75mm) versus unmolded (2.34±0.68mm), p<0.08. Conclusion Molding a prostate prior to fixation provided a better alignment of internal structures within the prostate, but this did not reach statistical significance. Software-based morphing allowed for nearly complete overlap between the pathology slides and the MR images.

Background: Virtualization in radiology presents a growing opportunity to improve the efficiency and lower the cost of care delivery. Furthermore, it may provide a means to manage staffing shortages, reduce burnout, and encourage employee development. In radiology, the feasibility to perform several non-patientfacing imaging procedure tasks remotely is explored. Virtualization of magnetic resonance imaging (MRI) exams is of specific interest as it typically involves a range of complex tasks, and inefficiencies can increase patient wait times (WTs) and reduce overall utilization.Methods: To explore this, a computer simulation model is developed to approximate turn-around time (TAT) and patient WT while accounting for technologist expertise and workflow complexity. The model was validated against 1,618 magnetic resonance (MR) inpatient/ED exams. Using cognitive task analysis (CTA), complex MR functions are identified from a technologist perspective that may require a high-level of expertise and thus influence workflow durations.Results: Virtualizing complex, non-patient-facing functions may reduce MR workflow duration by up to 15% and patient in-exam WT by up to 75% when utilizing expert technologists. For example, scans involving the abdomen and spine and addressing unreported implants benefit the most from experts and may be supported remotely.Conclusions: Modifying the workflow of MRI exams by segmenting complex, non-patient facing functions to expert technologists within an organization or in a remote center is feasible as it improves efficiencies and results in a better patient experience. In addition, administration now has guidance on how to effectively deploy a highly-trained workforce in a virtual setting.