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 The goal of this study is to characterize and improve the accuracy of 2D Magnetic resonance fingerprinting (MRF) scans in the presence of slice profile and B1 imperfections, which are two main factors that affect quantitative results in MRF. Methods The slice profile and B1 imperfections are characterized and corrected separately. The slice profile effect is corrected by simulating the RF pulse in the dictionary, and the B1 is corrected by acquiring a B1-map using the Bloch-Siegert method before each scan. The accuracy, precision and repeatability of the proposed method are evaluated in phantom studies. The effects of both slice profile and B1 imperfections are also illustrated and corrected in the in vivo studies. Results The slice profile and B1 corrections improve the accuracy of the T1 and T2 values, independent of the shape of the RF pulse. The T1 and T2 values obtained from different excitation patterns become more consistent after corrections, which leads to an improvement of the robustness of the MRF design. Conclusion This study demonstrates that MRF is sensitive to both slice profile and B1 effects and that corrections can be made to improve the accuracy of MRF with only a 2 second increase in acquisition time.

This study applied a fast acquisition scheme for a fully quantitative 3D magnetic resonance fingerprinting scan with a total acceleration factor of 144 as compared with the Nyquist rate, such that 3D T , T , and proton density maps can be acquired with whole-brain coverage at clinical resolution in less than 5 min. Magn Reson Med 79:2190-2197, 2018. © 2017 International Society for Magnetic Resonance in Medicine.

Purpose To develop and evaluate an examination consisting of magnetic resonance (MR) fingerprinting-based T1, T2, and standard apparent diffusion coefficient (ADC) mapping for multiparametric characterization of prostate disease. Materials and Methods This institutional review board-approved, HIPAA-compliant retrospective study of prospectively collected data included 140 patients suspected of having prostate cancer. T1 and T2 mapping was performed with fast imaging with steady-state precession-based MR fingerprinting with ADC mapping. Regions of interest were drawn by two independent readers in peripheral zone lesions and normal-appearing peripheral zone (NPZ) tissue identified on clinical images. T1, T2, and ADC were recorded for each region. Histopathologic correlation was based on systematic transrectal biopsy or cognitively targeted biopsy results, if available. Generalized estimating equations logistic regression was used to assess T1, T2, and ADC in the differentiation of (a) cancer versus NPZ, (b) cancer versus prostatitis, (c) prostatitis versus NPZ, and (d) high- or intermediate-grade tumors versus low-grade tumors. Analysis was performed for all lesions and repeated in a targeted biopsy subset. Discriminating ability was evaluated by using the area under the receiver operating characteristic curve (AUC). Results In this study, 109 lesions were analyzed, including 39 with cognitively targeted sampling. T1, T2, and ADC from cancer (mean, 1628 msec ± 344, 73 msec ± 27, and 0.773 × 10 mm/sec ± 0.331, respectively) were significantly lower than those from NPZ (mean, 2247 msec ± 450, 169 msec ± 61, and 1.711 × 10 mm/sec ± 0.269) (P < .0001 for each) and together produced the best separation between these groups (AUC = 0.99). ADC and T2 together produced the highest AUC of 0.83 for separating high- or intermediate-grade tumors from low-grade cancers. T1, T2, and ADC in prostatitis (mean, 1707 msec ± 377, 79 msec ± 37, and 0.911 × 10 mm/sec ± 0.239) were significantly lower than those in NPZ (P < .0005 for each). Interreader agreement was excellent, with an intraclass correlation coefficient greater than 0.75 for both T1 and T2 measurements. Conclusion This study describes the development of a rapid MR fingerprinting- and diffusion-based examination for quantitative characterization of prostatic tissue. RSNA, 2017 Online supplemental material is available for this article.

To develop a fast three-dimensional method for simultaneous T1 and T2 quantification for breast imaging by using MR fingerprinting. Materials and Methods: In this prospective study, variable flip angles and magnetization preparation modules were applied to acquire MR fingerprinting data for each partition of a three-dimensional data set. A fast postprocessing method was implemented by using singular value decomposition. The proposed technique was first validated in phantoms and then applied to 15 healthy female participants (mean age, 24.2 years 6 5.1 [standard deviation]; range, 18-35 years) and 14 female participants with breast cancer (mean age, 55.4 years 6 8.8; range, 39-66 years) between March 2016 and April 2018. The sensitivity of the method to B 1 field inhomogeneity was also evaluated by using the Bloch-Siegert method. Results: Phantom results showed that accurate and volumetric T1 and T2 quantification was achieved by using the proposed technique. The acquisition time for three-dimensional quantitative maps with a spatial resolution of 1.6 3 1.6 3 3 mm 3 was approximately 6 minutes. For healthy participants, averaged T1 and T2 relaxation times for fibroglandular tissues at 3.0 T were 1256 msec 6 171 and 46 msec 6 7, respectively. Compared with normal breast tissues, higher T2 relaxation time (68 msec 6 13) was observed in invasive ductal carcinoma (P , .001), whereas no statistical difference was found in T1 relaxation time (1183 msec 6 256; P = .37). Conclusion: A method was developed for breast imaging by using the MR fingerprinting technique, which allows simultaneous and volumetric quantification of T1 and T2 relaxation times for breast tissues.