Medizinische Universität Graz Austria/Österreich - Forschungsportal - Medical University of Graz
Gewählte Publikation:
SHR Neuro Krebs Kardio Lipid Stoffw Microb
Cochran, RL; Bradley, WR; Dhami, RS; Milshteyn, E; Pohl, M; Ghosh, S; Nakrour, N; Lan, P; Wang, XZ; Guidon, A; Harisinghani, MG.
Comparing prostate diffusion weighted images reconstructed with a commercial deep-learning product to a deep learning phase corrected model at 1.5 T
CLIN IMAG. 2026; 130: 110681
Doi: 10.1016/j.clinimag.2025.110681
Web of Science
PubMed
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- Co-Autor*innen der Med Uni Graz
- Pohl Maximilian
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- Abstract:
- Purpose: To determine whether a new deep learning (DL) based phase corrected (DLPC) reconstruction model can enhance image quality of diffusion weighted images of the prostate acquired at 1.5 T compared to a commercially available DL based product. Methods and materials: A retrospective study of 30 consecutive patients undergoing conventional multiparametric MRI (mpMRI) of the prostate on a single 1.5 T scanner was performed. Diffusion image datasets reconstructed with a commercially available DL product and a new DLPC model were assessed. Qualitative image assessment was performed by three board certified radiologists using a 5-point Likert scale across four features and inter-rater agreement was estimated using Gwet's AC2 statistic. Quantitative image comparison was performed by assessing SNR of acquired intermediate b-value (b = 1000 s/mm(2)) diffusion images. The Wilcoxon matched-pairs signed rank test was used to assess differences between techniques. Image noise was assessed using the edge function. Results: Median patient age was 70 years (interquartile range: 66.0-75.3). All radiologists perceived less noise and better image quality for all DLPC image sets compared to commercial DL images (p < 0.05). Significantly higher SNR was observed for the acquired intermediate b-value diffusion images reconstructed with DLPC (median SNR: 49.4 vs 27.5; p < 0.001), and mean ADC values did not significantly differ between DLPC and DL images (p = 0.63). Edge analyses demonstrated significantly reduced noise for DLPC images (p < 0.001). Conclusions: DLPC image reconstruction of diffusion weighted prostate image datasets reduces image noise and improves SNR over a commercial DL product at 1.5 T.
- Find related publications in this database (Keywords)
- Magnetic resonance imaging (MRI)
- Deep learning (DL)
- Artificial intelligence (AI)
- Prostate cancer
- Diffusion weighted imaging (DWI)