Optimizing Image Quality with High-Resolution, Deep-Learning-Based Diffusion-Weighted Imaging in Breast Cancer Patients at 1.5 T

Overview

Journal Diagnostics (Basel)

Specialty Radiology

Date 2024 Aug 29

PMID 39202230

Authors

Susann-Cathrin Olthof

Elisabeth Weiland

Thomas Benkert

Daniel Wessling

Daniel Leyhr

Saif Afat

Konstantin Nikolaou

Heike Preibsch

Affiliations

Soon will be listed here.

Abstract

The objective of this study was to evaluate a high-resolution deep-learning (DL)-based diffusion-weighted imaging (DWI) sequence for breast magnetic resonance imaging (MRI) in comparison to a standard DWI sequence (DWI) at 1.5 T. It is a prospective study of 38 breast cancer patients, who were scanned with DWI and DWI. Both DWI sequences were scored for image quality, sharpness, artifacts, contrast, noise, and diagnostic confidence with a Likert-scale from 1 (non-diagnostic) to 5 (excellent). The lesion diameter was evaluated on b 800 DWI, apparent diffusion coefficient (ADC), and the second subtraction (SUB) of the contrast-enhanced T1 VIBE. SNR was also calculated. Statistics included correlation analyses and paired -tests. High-resolution DWI offered significantly superior image quality, sharpness, noise, contrast, and diagnostic confidence (each < 0.02)). Artifacts were significantly higher in DWI by one reader (M = 4.62 vs. 4.36 Likert scale, < 0.01) without affecting the diagnostic confidence. SNR was higher in DWI for b 50 and ADC maps (each = 0.07). Acquisition time was reduced by 22% in DWI. The lesion diameters in DWI b 800 and and ADC and were respectively 6% lower compared to the 2nd SUB. A DL-based diffusion sequence at 1.5 T in breast MRI offers a higher resolution and a faster acquisition, including only minimally more artefacts without affecting the diagnostic confidence.

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