Accelerated T2-Weighted TSE Imaging of the Prostate Using Deep Learning Image Reconstruction: A Prospective Comparison with Standard T2-Weighted TSE Imaging

Abstract

Multiparametric MRI (mpMRI) of the prostate has become the standard of care in prostate cancer evaluation. Recently, deep learning image reconstruction (DLR) methods have been introduced with promising results regarding scan acceleration. Therefore, the aim of this study was to investigate the impact of deep learning image reconstruction (DLR) in a shortened acquisition process of T2-weighted TSE imaging, regarding the image quality and diagnostic confidence, as well as PI-RADS and T2 scoring, as compared to standard T2 TSE imaging. Sixty patients undergoing 3T mpMRI for the evaluation of prostate cancer were prospectively enrolled in this institutional review board-approved study between October 2020 and March 2021. After the acquisition of standard T2 TSE imaging (T2S), the novel T2 TSE sequence with DLR (T2DLR) was applied in three planes. Overall, the acquisition time for T2S resulted in 10:21 min versus 3:50 min for T2DLR. The image evaluation was performed by two radiologists independently using a Likert scale ranging from 1–4 (4 best) applying the following criteria: noise levels, artifacts, overall image quality, diagnostic confidence, and lesion conspicuity. Additionally, T2 and PI-RADS scoring were performed. The mean patient age was 69 ± 9 years (range, 49–85 years). The noise levels and the extent of the artifacts were evaluated to be significantly improved in T2DLR versus T2S by both readers (p < 0.05). Overall image quality was also evaluated to be superior in T2DLR versus T2S in all three acquisition planes (p = 0.005–<0.001). Both readers evaluated the item lesion conspicuity to be superior in T2DLR with a median of 4 versus a median of 3 in T2S (p = 0.001 and <0.001, respectively). T2-weighted TSE imaging of the prostate in three planes with an acquisition time reduction of more than 60% including DLR is feasible with a significant improvement of image quality.

Analysis of a Deep Learning-Based Superresolution Algorithm Tailored to Partial Fourier Gradient Echo Sequences of the Abdomen at 1.5 T

“…It was shown that DL image reconstructions are possible with high image quality in musculoskeletal imaging, abdominal imaging, and in prostate MRI including drastic reduction of acquisition time. 29,30 However, in GRE imaging most approaches were related to improvement of image quality and resolution (superresolution) and not primarily to acquisition time due to the fast imaging process. 19 The successful application of superresolution was previously demonstrated in head and neck and knee imaging as well as in precontrast and postcontrast abdominal MRI.…”

Deep Learning Applications in Magnetic Resonance Imaging: Has the Future Become Present?

“…However, a severe limitation of these sequences is based on its long acquisition times and reduced scanner availability. In recent studies, it was shown that DL image reconstruction is able to achieve an acquisition time reduction of up to 65% in prostate TSE imaging, without any loss of image quality ( Figure 2 ) [ 11 , 38 ].…”

Reduction in Acquisition Time and Improvement in Image Quality in T2-Weighted MR Imaging of Musculoskeletal Tumors of the Extremities Using a Novel Deep Learning-Based Reconstruction Technique in a Turbo Spin Echo (TSE) Sequence

“…In radiology, trained algorithms based on larger datasets have primarily been introduced in, e.g., classification, segmentation, pattern recognition, and artificial intelligence-based diagnosis [ 13 , 14 ]. In the meantime, the inclusion of these components into the reconstruction process has enabled great improvements in image quality, sharpness, and signal-to-noise ratio (SNR) in MRI and has consequently also accelerated acquisitions [ 15 , 16 , 17 , 18 , 19 ].…”