Objective To evaluate integrated deep learning reconstruction (Integrated DLR), which combines phase correction, denoising, and super-resolution, for prostate diffusion-weighted imaging (DWI) by comparing it with compressed sensing (CS) and deep learning–based denoising reconstruction (Denoising DLR). Methods This single-center, retrospective study included 47 patients scanned on a single-vendor 3-T system. Three DWI datasets were reconstructed from identical raw data using CS, Denoising DLR, and Integrated DLR. Two radiologists rated overall image quality, noise, artifact, and prostate delineation. Apparent signal-to-noise ratio (SNR) was assessed in all patients. In 21 patients with pathologically confirmed prostate cancer, we assessed lesion conspicuity, lesion localization/anatomical relationship, Prostate Imaging Reporting and Data System (PI-RADS) version 2.1 DWI scores, apparent lesion contrast-to-noise ratio (CNR), and apparent diffusion coefficient (ADC). Comparisons were performed using Friedman tests followed by Bonferroni-corrected Wilcoxon signed-rank tests. Results Scores for overall image quality, noise, and prostate delineation were highest with Integrated DLR, followed by Denoising DLR and CS, for both readers (all p < 0.001); apparent SNR showed the same pattern (p < 0.001). Artifact scores did not differ among methods. Lesion conspicuity differed among methods for Reader 2 (p = 0.044) but not for Reader 1 (p = 0.199), whereas Integrated DLR yielded the highest lesion localization/anatomical relationship scores for both readers (both p < 0.001). Integrated DLR also yielded the highest apparent CNR and lesion ADC (both p < 0.001), while PI-RADS DWI scores were unchanged. Conclusion Integrated DLR improved prostate DWI image quality and lesion visibility compared with CS and Denoising DLR, potentially facilitating visual assessment of prostate DWI.
Intravoxel incoherent motion (IVIM) magnetic resonance imaging (MRI) technique evaluates the microstructure and microvascular blood perfusion of biological tissues. IVIM-derived pseudo-diffusion coefficient (Dfast) quantifies the perfusion-related motion of blood within the tissue microvascular and is related to the mean squared displacement of water molecules in time. Diffuse correlation spectroscopy (DCS) is a noninvasive optical technique to monitor the microvascular blood flow changes. DCS-derived blood flow index (BFIDCS) is proportional to mean squared displacement of the red blood cells in time, thereby enabling comparison to Dfast. To evaluate the correlation between Dfast and BFIDCS, controlled muscle-occlusion tests were performed on five healthy volunteers, with five leg and four forearm datasets. In response to muscle occlusion, the BFIDCS decreased, whereas the mean Dfast increased, leading to no positive correlation between these parameters (rrm: repeated measures correlation, rrm=−0.219,95%CI:−0.724to0.438 in leg occlusion and rrm=−0.567,95%CI:−0.894to0.155, in forearm occlusion). The IVIM data were of adequate quality (median voxelwise signal to noise ratio at b = 0: 25–57), however the voxelwise coefficient of variation CVvoxels of the Dfast (median within-subjects CVvoxels in the leg: 210.3% and in forearm: 160.9%) were imprecise. The disagreement between the IVIM- and DCS-derived parameter responses is therefore specific to the applied protocol, and its cause remains unresolved. Therefore, further studies with larger cohorts and different experimental protocols are required to better characterize the correlation between the IVIM- and DCS-derived parameters under varying perfusion conditions.
