Magnetic resonance fingerprinting is a technique for acquiring and processing MR data that simultaneously provides quantitative maps of different tissue parameters through a pattern recognition algorithm. A predefined dictionary models the possible signal evolutions simulated using the Bloch equations with different combinations of various MR parameters and pattern recognition is completed by computing the inner product between the observed signal and each of the predicted signals within the dictionary. Though this matching algorithm has been shown to accurately predict the MR parameters of interest, one desires a more efficient method to obtain the quantitative images. We propose to compress the dictionary using the singular value decomposition (SVD), which will provide a low-rank approximation. By compressing the size of the dictionary in the time domain, we are able to speed up the pattern recognition algorithm, by a factor of between 3.4-4.8, without sacrificing the high signal-to-noise ratio of the original scheme presented previously.

Purpose To reduce acquisition time needed to obtain reliable parametric maps with Magnetic Resonance Fingerprinting. Methods An iterative-denoising algorithm is initialized by reconstructing the MRF image series at low image resolution. For subsequent iterations, the method enforces pixel-wise fidelity to the best-matching dictionary template then enforces fidelity to the acquired data at slightly higher spatial resolution. After convergence, parametric maps with desirable spatial resolution are obtained through template matching of the final image series. The proposed method was evaluated on phantom and in-vivo data using the highly-undersampled, variable-density spiral trajectory and compared with the original MRF method. The benefits of additional sparsity constraints were also evaluated. When available, gold standard parameter maps were used to quantify the performance of each method. Results The proposed approach allowed convergence to accurate parametric maps with as few as 300 time points of acquisition, as compared to 1000 in the original MRF work. Simultaneous quantification of T1, T2, proton density (PD) and B0 field variations in the brain was achieved in vivo for a 256×256 matrix for a total acquisition time of 10.2s, representing a 3-fold reduction in acquisition time. Conclusions The proposed iterative multiscale reconstruction reliably increases MRF acquisition speed and accuracy.

MORF improves the insensitivity of MRF toward rigid-body motion occurring during any part of the MRF acquisition.

Background Conventional MRI can be limited in detecting subtle epileptic lesions or identifying active/epileptic lesions among widespread, multifocal lesions. Purpose We developed a high‐resolution 3D MR fingerprinting (MRF) protocol to simultaneously provide quantitative T1, T2, proton density, and tissue fraction maps for detection and characterization of epileptic lesions. Study type Prospective. Population National Institute of Standards and Technology (NIST) / International Society for Magnetic Resonance in Medicine (ISMRM) phantom, five healthy volunteers and 15 patients with medically intractable epilepsy undergoing presurgical evaluation with noninvasive or invasive electroclinical data. Field Strength/Sequence 3D MRF scans and routine clinical epilepsy MR protocols were acquired at 3 T. Assessment The accuracy of the T1 and T2 values were first evaluated using the NIST/ISMRM phantom. The repeatability was then estimated with both phantom and volunteers based on the coefficient of variance (CV). For epilepsy patients, all the maps were qualitatively reviewed for lesion detection by three independent reviewers (S.E.J., M.L., I.N.) blinded to clinical data. Region of interest (ROI) analysis was performed on T1 and T2 maps to quantify the multiparametric signal differences between lesion and normal tissues. Findings from qualitative review and quantitative ROI analysis were compared with patients' electroclinical data to assess concordance. Statistical Tests Phantom results were compared using R‐squared, and patient results were compared using linear regression models. Results The phantom study showed high accuracy with the standard values, with an R2 of 0.99. The volunteer study showed high repeatability, with an average CV of 4.3% for T1 and T2 in various tissue regions. For the 15 patients, MRF showed additional findings in four patients, with the remaining 11 patients showing findings consistent with conventional MRI. The additional MRF findings were highly concordant with patients' electroclinical presentation. Data Conclusion The 3D MRF protocol showed potential to identify otherwise inconspicuous epileptogenic lesions from the patients with negative conventional MRI diagnosis, as well as to correlate with different levels of epileptogenicity when widespread lesions were present. Level of Evidence: 3. Technical Efficacy Stage: 3. J. Magn. Reson. Imaging 2019;49:1333–1346.

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Purpose The unpleasant acoustic noise is an important drawback of almost every magnetic resonance imaging scan. Instead of reducing the acoustic noise to improve patient comfort, a method is proposed to mitigate the noise problem by producing musical sounds directly from the switching magnetic fields while simultaneously quantifying multiple important tissue properties. Theory and Methods MP3 music files were converted to arbitrary encoding gradients, which were then used with varying flip angles and TRs in both 2D and 3D MRF exam. This new acquisition method named MRF-Music was used to quantify T1, T2 and proton density maps simultaneously while providing pleasing sounds to the patients. Results The MRF-Music scans were shown to significantly improve the patients' comfort during the MRI scans. The T1 and T2 values measured from phantom are in good agreement with those from the standard spin echo measurements. T1 and T2 values from the brain scan are also close to previously reported values. Conclusions MRF-Music sequence provides significant improvement of the patient's comfort as compared to the MRF scan and other fast imaging techniques such as EPI and TSE scans. It is also a fast and accurate quantitative method that quantifies multiple relaxation parameter simultaneously.

Standard clinical magnetic resonance imaging protocols can be made quieter through adequate gradient wave form optimization. In scans with high signal-to-noise ratio, parallel imaging can be used to further reduce acoustic noise.

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