Learning‐based optimization of acquisition schedule for magnetization transfer contrast MR fingerprinting

Abstract

Magnetization transfer contrast MR fingerprinting (MTC‐MRF) is a novel quantitative imaging method that simultaneously quantifies free bulk water and semisolid macromolecule parameters using pseudo‐randomized scan parameters. To improve acquisition efficiency and reconstruction accuracy, the optimization of MRF sequence design has been of recent interest in the MRF field, but has been challenging due to the large number of degrees of freedom to be optimized in the sequence. Herein, we propose a framework for learning‐based optimization of the acquisition schedule (LOAS), which optimizes RF saturation‐encoded MRF acquisitions with a minimal number of scan parameters for tissue parameter determination. In a supervised learning framework, scan parameters were subsequently updated to minimize a predefined loss function that can directly represent tissue quantification errors. We evaluated the performance of the proposed approach with a numerical phantom and in in vivo experiments. For validation, MRF images were synthesized using the tissue parameters estimated from a fully connected neural network framework and compared with references. Our results showed that LOAS outperformed existing indirect optimization methods with regard to quantification accuracy and acquisition efficiency. The proposed LOAS method could be a powerful optimization tool in the design of MRF pulse sequences.

Only‐train‐once MR fingerprinting for B0 and B1 inhomogeneity correction in quantitative magnetization‐transfer contrast

“…Therefore, the proposed OTOM‐based B 0 and B 1 correction could benefit CEST‐MRF reconstruction or imaging of the brainstem, frontal lobes, and temporal lobes, where severe B 0 inhomogeneity in the air–tissue interfaces remains. In addition to the B 0 and B 1 correction, OTOM could be further extended to MRF schedule optimization by analyzing the quantification error of a given schedule 46,60,61 . The feasibility was already demonstrated in Kang et al 62 …”

“…In addition to the B 0 and B 1 correction, OTOM could be further extended to MRF schedule optimization by analyzing the quantification error of a given schedule 46,60,61 . The feasibility was already demonstrated in Kang et al 62 …”

“…The MRF schedule was optimized from the learning-based optimization of the acquisition schedule (LOAS) method. 46 In the second digital phantom study, the quantification accuracy was evaluated with various numbers of dynamic scans (#10, #20, #30, and #40). Quantification results from the nominal scan parameters were compared with those from the B 0 -corrected and B 1 -corrected scan parameters.…”

Accelerated and quantitative three‐dimensional molecular MRI using a generative adversarial network

“…Second, the performance of the proposed method is dependent on the original discrimination ability of the M‐length acquisition schedule, as only images from the end of the acquisition schedule can be removed, and not the beginning, due to the acquired spin history induced by earlier acquisitions. Therefore, the parameter discrimination ability could be further improved, while benefiting from the spatial denoising and extrapolation capabilities demonstrated here, by combining the proposed GAN approach with an optimized acquisition schedule, which could be discovered using recently developed deep‐learning‐based sequence optimization approaches 63,64 …”

Molecular MRI-Based Monitoring of Cancer Immunotherapy Treatment Response

“…Perlman et al developed an end-to-end framework for generating rapid (less than 1 min) CEST acquisition protocols while simultaneously training a reconstruction network that extracts quantitative molecular maps from the raw data [ 205 ]. Kang et al developed a learning-based approach for the optimization of semisolid magnetization transfer MRF protocols [ 206 ] and demonstrated its applicability on human subjects.…”