Comparing prostate diffusion weighted images reconstructed with a commercial deep-learning product to a deep learning phase corrected model at 1.5 T.
Authors
Affiliations (11)
Affiliations (11)
- Department of Radiology, Massachusetts General Hospital, Boston, MA, United States; Harvard Medical School, Cambridge, MA, United States. Electronic address: [email protected].
- Department of Radiology, Massachusetts General Hospital, Boston, MA, United States; Harvard Medical School, Cambridge, MA, United States. Electronic address: [email protected].
- Department of Radiology, Massachusetts General Hospital, Boston, MA, United States; Harvard Medical School, Cambridge, MA, United States. Electronic address: [email protected].
- GE HealthCare, Boston, MA, United States. Electronic address: [email protected].
- Department of Radiology, Massachusetts General Hospital, Boston, MA, United States; Harvard Medical School, Cambridge, MA, United States; Department of Radiology, Medical University of Graz, Graz, Austria. Electronic address: [email protected].
- Department of Radiology, Massachusetts General Hospital, Boston, MA, United States; Harvard Medical School, Cambridge, MA, United States. Electronic address: [email protected].
- Department of Radiology, Massachusetts General Hospital, Boston, MA, United States; Harvard Medical School, Cambridge, MA, United States. Electronic address: [email protected].
- GE HealthCare, Menlo Park, CA, United States. Electronic address: [email protected].
- GE HealthCare, Houston, TX, United States. Electronic address: [email protected].
- GE HealthCare, Boston, MA, United States. Electronic address: [email protected].
- Department of Radiology, Massachusetts General Hospital, Boston, MA, United States; Harvard Medical School, Cambridge, MA, United States. Electronic address: [email protected].
Abstract
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. 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<sup>2</sup>) diffusion images. The Wilcoxon matched-pairs signed rank test was used to assess differences between techniques. Image noise was assessed using the edge function. 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). DLPC image reconstruction of diffusion weighted prostate image datasets reduces image noise and improves SNR over a commercial DL product at 1.5 T.