Breast lesion detection scheme for low gadolinium dose DCE-MRI using radon cumulative distribution transform and domain transfer: preliminary results

Juhun Lee; Federico Pineda; Gregory S. Karczmar; Robert M. Nishikawa

doi:10.1117/12.3004216

3 April 2024 Breast lesion detection scheme for low gadolinium dose DCE-MRI using radon cumulative distribution transform and domain transfer: preliminary results

Juhun Lee, Federico Pineda, Gregory S. Karczmar, Robert M. Nishikawa

Author Affiliations +

Proceedings Volume 12927, Medical Imaging 2024: Computer-Aided Diagnosis; 129271W (2024) https://doi.org/10.1117/12.3004216
Event: SPIE Medical Imaging, 2024, San Diego, California, United States

Conference Poster

Abstract

Our goal was to create a deep network-based lesion detection algorithm for low dose dynamic contrast-enhanced MRI (DCE-MRI) breast images, using Radon Cumulative Distribution Transform (RCDT) to highlight subtle enhancement. We had a dataset of 11 enhancing lesions in eight women with suspected fibroadenomas who underwent a dual-dose DCEMRI protocol on a 3T Philips scanner. To overcome the data limitation, we used a domain-transfer approach, training the YOLOv5 detection model on a publicly available Duke DCE-MRI dataset of 922 biopsy-confirmed invasive breast cancer cases acquired using Siemens or GE scanners. The training data included 23,426 pre-contrast slices with corresponding post-contrast slices and biopsy-proven lesions. The dataset was split into a training set (830 women) and a validation set (92 women). We resized all slices to 400 x 400 pixels and applied RCDT on pre- and post-contrast pairs to highlight lesion enhancement. By combining RCDT images with pre- and post-contrast images, we created RGB images as input for our algorithm. The results were promising, with the algorithm successfully detecting a total of 6 lesions in both regular and low-dose slices, 3 lesions only in regular dose, and 1 lesion only in low dose. However, it missed 1 lesion in both regular and low-dose images. Our study demonstrated the feasibility of a domain-transferred and RCDT-assisted lesion detection algorithm for low-dose MRI, even when data was acquired from scanners made by three different vendors.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

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Juhun Lee, Federico Pineda, Gregory S. Karczmar, and Robert M. Nishikawa "Breast lesion detection scheme for low gadolinium dose DCE-MRI using radon cumulative distribution transform and domain transfer: preliminary results", Proc. SPIE 12927, Medical Imaging 2024: Computer-Aided Diagnosis, 129271W (3 April 2024); https://doi.org/10.1117/12.3004216

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