Editorial for “Comparison of a Deep Learning‐Accelerated vs. Conventional T2‐Weighted Sequence in Biparametric MRI of the Prostate”

Editorial for “Comparison of a Deep Learning-Accelerated vs. Conventional T2Weighted Sequence in Biparametric MRI of the Prostate” Prostate magnetic resonance imaging (MRI) provides a noninvasive tool for prostate cancer diagnosis, local staging, and biopsy planning. Multiparametric MRI (mpMRI), proposed by the American College of Radiology in their guidelines for Prostate Imaging-Reporting and Data System (PI-RADS), has been widely used in the past decade as the standardized and most comprehensive MRI protocol for prostate cancer. However, mpMRI is limited by its long scanning time (30–45 minutes), high cost, and requirement for gadolinium-based contrast agents (GBCA). Bi-parametric MRI (bpMRI) has gained popularity as it includes only T2-weighted (T2w) and diffusion-weighted images (DWI), the primary sequences used to determine a lesion’s PI-RADS risk assessment category. The bpMRI has been demonstrated to have comparable diagnostic accuracy to the mpMRI at a reduced cost, with shorter scan times, and without requiring GBCA injection. With the increasing utilization of advanced diagnostic MRI techniques, there are associated challenges related to high costs, poor access, and long wait times in health systems worldwide. For prostate MRI, further acceleration of bpMRI will make prostate imaging even more affordable and efficient. To that end, parallel imaging techniques, represented by SENSE and GRAPPA, have been introduced to most clinical MRI product sequences (for the prostate and other anatomic regions) to accelerate the acquisition by downsampling k-space and using coil sensitivity information for the subsequent image reconstruction. Meanwhile, the recent development of deep learning (DL)-based reconstruction allows an even higher k-space down-sampling rate than traditional parallel imaging reconstruction while keeping a similar quality relative to fully sampled images. One of the most representative neural networks among all DL reconstruction approaches is the variational network (VN), which has been proven to effectively accelerate MRI protocols for imaging multiple body regions. Recent pilot experiments in prostate MRI demonstrated no significant reduction in image quality for both T2w and DWI with DL-reconstruction for up to 6x under-sampling. These pilot studies found no significant difference in PI-RADS scoring between conventional and DL-accelerated sequences. In this issue of JMRI, Tong et al present a larger-scale study (i.e. 80 subjects vs. 30–60 patients in previous similar studies) to investigate the validity of accelerated T2w imaging in bpMRI by using DL-based VN reconstruction. In order to achieve a generalized VN for a broader range DL-based reconstruction, the authors trained the network using >10,000 slices of fully sampled T2w Turbo Spin-Echo (TSE) data acquired from both 1.5 T and 3 T scanners over multiple anatomic regions (i.e. head, pelvis, and knee). With the proposed DL-based method, the acquisition time of T2w images in the bpMRI protocol was reduced from 6.8 minutes to 2.4 minutes. Subsequently, conventional T2w images (CLT2) and DL-accelerated T2w images (DL-T2) were, respectively, combined with conventional DWIs that produced two bpMRI datasets (CL-bpMRI and DL-bpMRI), and both were evaluated anonymously by three radiologists. Two radiologists marked no significant difference between CL-T2 and DL-T2 while one indicated a higher image quality for CL-T2. However, results from all three radiologists indicated no significant difference between CL-bpMRI and DL-bpMRI with respect to diagnostic quality. The authors also introduced a DL-based computer-aided detection (DL-CAD) system to further evaluate the CLand DL-bpMRI results, which demonstrated no difference in sensitivity between the two approaches but lower specificity for DL-bpMRI compared to CL-bpMRI. At present, there are only a few studies on DL-based prostate imaging, and all indicate no significant difference in PI-RADS rating or lesion detection by radiologists. While this presented study revealed a similar result, the DL-CAD provided additional evidence for this finding. To further develop the approaches presented in this study, several investigations can be performed, such as a protocol-matched image quality comparison between CL-T2 and DL-T2; a comparison under multiple VN training strategies; and an evaluation of differences in results compared to other studies.