Motivation: This work was driven by the need for efficient, high-fidelity multi-parametric MRI for advanced breast cancer applications, such as treatment response prediction. Goal(s): To develop a streamlined single-scan approach for acquiring co-aligned relaxometry and diffusion parametric maps of the breast, addressing challenges in multi-contrast MRI, including acquisition speed, image distortion, alignment and post-processing. Approach: We developed a multi-dimensional breast MR fingerprinting (mdMRF) sequence, incorporating multiple T1-, T2- and diffusion-preparation modules, with an acquisition time of 23 seconds/slice. Results: Our mdMRF T1, T2 and apparent diffusion coefficient (ADC) values agreed with reference methods in both phantom validations and in-vivo experiments, particularly in cancer patients. Impact: We present the first combined relaxometry-diffusion MR fingerprinting framework for breast imaging, paving the way for advancements in predictive breast cancer imaging, including improved capabilities for prediction of treatment response.
Moinian et al. (Tue,) studied this question.