Ultrafast dynamic contrast‐enhanced mri of the breast using compressed sensing: breast cancer diagnosis based on separate visualization of breast arteries and veins. Issue 1 (28th May 2017) - Onishi, Natsuko
Abstract : Purpose: To evaluate the feasibility of ultrafast dynamic contrast‐enhanced (UF‐DCE) magnetic resonance imaging (MRI) with compressed sensing (CS) for the separate identification of breast arteries/veins and perform temporal evaluations of breast arteries and veins with a focus on the association with ipsilateral cancers. Materials and Methods: Our Institutional Review Board approved this study with retrospective design. Twenty‐five female patients who underwent UF‐DCE MRI at 3T were included. UF‐DCE MRI consisting of 20 continuous frames was acquired using a prototype 3D gradient‐echo volumetric interpolated breath‐hold sequence including a CS reconstruction: temporal resolution, 3.65 sec/frame; spatial resolution, 0.9 × 1.3 × 2.5 mm. Two readers analyzed 19 maximum intensity projection images reconstructed from subtracted images, separately identified breast arteries/veins and the earliest frame in which they were respectively visualized, and calculated the time interval between arterial and venous visualization (A‐V interval) for each breast. Results: In total, 49 breasts including 31 lesions (breast cancer, 16; benign lesion, 15) were identified. In 39 of the 49 breasts (breasts with cancers, 16; breasts with benign lesions, 10; breasts with no lesions, 13), both breast arteries and veins were separately identified. The A‐V intervals for breasts with cancers were significantly shorter than those for breasts with benign lesions ( P = 0.043) and no lesions ( P = 0.007). Conclusion: UF‐DCE MRI using CS enables the separate identification of breast arteries/veins. Temporal evaluations calculating the time interval between arterial and venous visualization might be helpful in the differentiation of ipsilateral breast cancers from benign lesions. Level of Evidence: 3 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2018;47:97–104.