Mio Mori1, Tomoyuki Fujioka2, Leona Katsuta1, Yuka Kikuchi1, Goshi Oda3, Tsuyoshi Nakagawa3, Yoshio Kitazume1, Kazunori Kubota4, Ukihide Tateishi1. 1. Department of Diagnostic Radiology, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-ku, Tokyo, 113-8510, Japan. 2. Department of Diagnostic Radiology, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-ku, Tokyo, 113-8510, Japan. tomoyukifujioka@hotmail.com. 3. Department of Surgery, Breast Surgery, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-ku, Tokyo, 113-8510, Japan. 4. Department of Radiology, Dokkyo Medical University Hospital, 880 Kitakobayashi, Mibu, Shimotsugagun, Tochigi, 321-0293, Japan.
Abstract
PURPOSE: To generate and evaluate fat-saturated T1-weighted (FST1W) image synthesis of breast magnetic resonance imaging (MRI) using pix2pix. MATERIALS AND METHODS: We collected pairs of noncontrast-enhanced T1-weighted an FST1W images of breast MRI for training data (2112 pairs from 15 patients), validation data (428 pairs from three patients), and test data (90 pairs from 30 patients). From the original images, 90 synthetic images were generated with 50, 100, and 200 epochs using pix2pix. Two breast radiologists evaluated the synthetic images (from 1 = excellent to 5 = very poor) for quality of fat suppression, anatomic structures, artifacts, etc. The average score was analyzed for each epoch and breast density. RESULTS: The synthetic images were scored from 2.95 to 3.60; the best was reduction in artifacts when using 100 epochs. The average overall quality scores for fat suppression were 3.63 at 50 epochs, 3.24 at 100 epochs, and 3.12 at 200 epochs. In the analysis for breast density, each score was significantly better for nondense breasts than for dense breasts; the average score was 2.88-3.18 for nondense breasts and 3.03-3.42 for dense breasts (P = 0.000-0.042). CONCLUSION: Pix2pix had the potential to generate FST1W synthesis for breast MRI.
PURPOSE: To generate and evaluate fat-saturated T1-weighted (FST1W) image synthesis of breast magnetic resonance imaging (MRI) using pix2pix. MATERIALS AND METHODS: We collected pairs of noncontrast-enhanced T1-weighted an FST1W images of breast MRI for training data (2112 pairs from 15 patients), validation data (428 pairs from three patients), and test data (90 pairs from 30 patients). From the original images, 90 synthetic images were generated with 50, 100, and 200 epochs using pix2pix. Two breast radiologists evaluated the synthetic images (from 1 = excellent to 5 = very poor) for quality of fat suppression, anatomic structures, artifacts, etc. The average score was analyzed for each epoch and breast density. RESULTS: The synthetic images were scored from 2.95 to 3.60; the best was reduction in artifacts when using 100 epochs. The average overall quality scores for fat suppression were 3.63 at 50 epochs, 3.24 at 100 epochs, and 3.12 at 200 epochs. In the analysis for breast density, each score was significantly better for nondense breasts than for dense breasts; the average score was 2.88-3.18 for nondense breasts and 3.03-3.42 for dense breasts (P = 0.000-0.042). CONCLUSION: Pix2pix had the potential to generate FST1W synthesis for breast MRI.
Entities:
Keywords:
Breast imaging; Deep learning; Generative adversarial networks; Magnetic resonance imaging; Pix2pix