Evaluation of a Deep Learning Reconstruction for High-Quality T2-Weighted Breast Magnetic Resonance Imaging

Author:

Allen Timothy J.1ORCID,Henze Bancroft Leah C.2ORCID,Unal Orhan12,Estkowski Lloyd D.3,Cashen Ty A.3ORCID,Korosec Frank2,Strigel Roberta M.124,Kelcz Frederick2,Fowler Amy M.124,Gegios Alison2,Thai Janice2,Lebel R. Marc3ORCID,Holmes James H.567ORCID

Affiliation:

1. Department of Medical Physics, University of Wisconsin-Madison, 1111 Highland Avenue, Madison, WI 53705, USA

2. Department of Radiology, University of Wisconsin-Madison, 600 Highland Avenue, Madison, WI 53792, USA

3. GE Healthcare, 3000 N Grandview Blvd, Waukesha, WI 53188, USA

4. Carbone Cancer Center, University of Wisconsin-Madison, 600 Highland Avenue, Madison, WI 53792, USA

5. Department of Radiology, University of Iowa, 169 Newton Road, Iowa City, IA 52242, USA

6. Department of Biomedical Engineering, University of Iowa, 3100 Seamans Center, Iowa City, IA 52242, USA

7. Holden Comprehensive Cancer Center, University of Iowa, 200 Hawkins Drive, Iowa City, IA 52242, USA

Abstract

Deep learning (DL) reconstruction techniques to improve MR image quality are becoming commercially available with the hope that they will be applicable to multiple imaging application sites and acquisition protocols. However, before clinical implementation, these methods must be validated for specific use cases. In this work, the quality of standard-of-care (SOC) T2w and a high-spatial-resolution (HR) imaging of the breast were assessed both with and without prototype DL reconstruction. Studies were performed using data collected from phantoms, 20 retrospectively collected SOC patient exams, and 56 prospectively acquired SOC and HR patient exams. Image quality was quantitatively assessed via signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and edge sharpness. Qualitatively, all in vivo images were scored by either two or four radiologist readers using 5-point Likert scales in the following categories: artifacts, perceived sharpness, perceived SNR, and overall quality. Differences in reader scores were tested for significance. Reader preference and perception of signal intensity changes were also assessed. Application of the DL resulted in higher average SNR (1.2–2.8 times), CNR (1.0–1.8 times), and image sharpness (1.2–1.7 times). Qualitatively, the SOC acquisition with DL resulted in significantly improved image quality scores in all categories compared to non-DL images. HR acquisition with DL significantly increased SNR, sharpness, and overall quality compared to both the non-DL SOC and the non-DL HR images. The acquisition time for the HR data only required a 20% increase compared to the SOC acquisition and readers typically preferred DL images over non-DL counterparts. Overall, the DL reconstruction demonstrated improved T2w image quality in clinical breast MRI.

Funder

National Institutes of Health

Departments of Radiology and Medical Physics at the University of Wisconsin-Madison

GE Healthcare

Publisher

MDPI AG

Subject

Radiology, Nuclear Medicine and imaging

Reference29 articles.

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4. American College of Radiology (2023, July 01). Practice Parameter for the Performance of Contrast-Enhanced Magnetic Resonance Imaging (CE-MRI) of the Breast. Available online: https://www.acr.org/-/media/ACR/Files/Practice-Parameters/MR-Contrast-Breast.pdf?la=en.

5. The Current Status of Breast MR Imaging Part I. Choice of Technique, Image Interpretation, Diagnostic Accuracy, and Transfer to Clinical Practice;Kuhl;Radiology,2007

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