Accelerated Diffusion-Weighted Imaging in 3 T Breast MRI Using a Deep Learning Reconstruction Algorithm With Superresolution Processing

Abstract

Objectives Diffusion-weighted imaging (DWI) enhances specificity in multiparametric breast MRI but is associated with longer acquisition time. Deep learning (DL) reconstruction may significantly shorten acquisition time and improve spatial resolution. In this prospective study, we evaluated acquisition time and image quality of a DL-accelerated DWI sequence with superresolution processing (DWIDL) in comparison to standard imaging including analysis of lesion conspicuity and contrast of invasive breast cancers (IBCs), benign lesions (BEs), and cysts. Materials and Methods This institutional review board–approved prospective monocentric study enrolled participants who underwent 3 T breast MRI between August and December 2022. Standard DWI (DWISTD; single-shot echo-planar DWI combined with reduced field-of-view excitation; b-values: 50 and 800 s/mm2) was followed by DWIDL with similar acquisition parameters and reduced averages. Quantitative image quality was analyzed for region of interest–based signal-to-noise ratio (SNR) on breast tissue. Apparent diffusion coefficient (ADC), SNR, contrast-to-noise ratio, and contrast (C) values were calculated for biopsy-proven IBCs, BEs, and for cysts. Two radiologists independently assessed image quality, artifacts, and lesion conspicuity in a blinded independent manner. Univariate analysis was performed to test differences and interrater reliability. Results Among 65 participants (54 ± 13 years, 64 women) enrolled in the study, the prevalence of breast cancer was 23%. Average acquisition time was 5:02 minutes for DWISTD and 2:44 minutes for DWIDL (P < 0.001). Signal-to-noise ratio measured in breast tissue was higher for DWISTD (P < 0.001). The mean ADC values for IBC were 0.77 × 10−3 ± 0.13 mm2/s in DWISTD and 0.75 × 10−3 ± 0.12 mm2/s in DWIDL without significant difference when sequences were compared (P = 0.32). Benign lesions presented with mean ADC values of 1.32 × 10−3 ± 0.48 mm2/s in DWISTD and 1.39 × 10−3 ± 0.54 mm2/s in DWIDL (P = 0.12), and cysts presented with 2.18 × 10−3 ± 0.49 mm2/s in DWISTD and 2.31 × 10−3 ± 0.43 mm2/s in DWIDL. All lesions presented with significantly higher contrast in the DWIDL (P < 0.001), whereas SNR and contrast-to-noise ratio did not differ significantly between DWISTD and DWIDL regardless of lesion type. Both sequences demonstrated a high subjective image quality (29/65 for DWISTD vs 20/65 for DWIDL; P < 0.001). The highest lesion conspicuity score was observed more often for DWIDL (P < 0.001) for all lesion types. Artifacts were scored higher for DWIDL (P < 0.001). In general, no additional artifacts were noted in DWIDL. Interrater reliability was substantial to excellent (k = 0.68 to 1.0). Conclusions DWIDL in breast MRI significantly reduced scan time by nearly one half while improving lesion conspicuity and maintaining overall image quality in a prospective clinical cohort.

Optimized, Person-Centered Workflow Design for a High-Throughput Breast MRI Screening Facility—A Simulation Study

“…In the simulation, the implementation of an abMRI scanning protocol also turned out to be a big-time saver, reducing total appointment time, MRI room occupation, and time one needs to lie in prone position. Various studies have been investigating ideal abMRI and ultrafast protocols, in order to obtain optimal image quality while shortening the acquisition time 23,29–32 . Currently, abMRI protocols are clinically already in use.…”