).q RSNA, 2016 Purpose:To develop a magnetic resonance (MR) "fingerprinting" technique for quantitative abdominal imaging. Materials and Methods:This HIPAA-compliant study had institutional review board approval, and informed consent was obtained from all subjects. To achieve accurate quantification in the presence of marked B 0 and B 1 field inhomogeneities, the MR fingerprinting framework was extended by using a twodimensional fast imaging with steady-state free precession, or FISP, acquisition and a Bloch-Siegert B 1 mapping method. The accuracy of the proposed technique was validated by using agarose phantoms. Quantitative measurements were performed in eight asymptomatic subjects and in six patients with 20 focal liver lesions. A two-tailed Student t test was used to compare the T1 and T2 results in metastatic adenocarcinoma with those in surrounding liver parenchyma and healthy subjects. Results:Phantom experiments showed good agreement with standard methods in T1 and T2 after B 1 correction. In vivo studies demonstrated that quantitative T1, T2, and B 1 maps can be acquired within a breath hold of approximately 19 seconds. T1 and T2 measurements were compatible with those in the literature. Representative values included the following: liver, 745 msec 6 65 (standard deviation) and 31 msec 6 6; renal medulla, 1702 msec 6 205 and 60 msec 6 21; renal cortex, 1314 msec 6 77 and 47 msec 6 10; spleen, 1232 msec 6 92 and 60 msec 6 19; skeletal muscle, 1100 msec 6 59 and 44 msec 6 9; and fat, 253 msec 6 42 and 77 msec 6 16, respectively. T1 and T2 in metastatic adenocarcinoma were 1673 msec 6 331 and 43 msec 6 13, respectively, significantly different from surrounding liver parenchyma relaxation times of 840 msec 6 113 and 28 msec 6 3 (P , .0001 and P , .01) and those in hepatic parenchyma in healthy volunteers (745 msec 6 65 and 31 msec 6 6, P , .0001 and P = .021, respectively). Conclusion:A rapid technique for quantitative abdominal imaging was developed that allows simultaneous quantification of multiple tissue properties within one 19-second breath hold, with measurements comparable to those in published literature.q RSNA, 2016

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.

Background Magnetic resonance fingerprinting (MRF) allows rapid simultaneous quantification of T1 and T2 relaxation times. This study assesses the utility of MRF in differentiating between common types of adult intra-axial brain tumors. Methods MRF acquisition was performed in 31 patients with untreated intra-axial brain tumors: 17 glioblastomas, 6 WHO grade II lower-grade gliomas and 8 metastases. T1, T2 of the solid tumor (ST), immediate peritumoral white matter (PW), and contralateral white matter (CW) were summarized within each region of interest. Statistical comparisons on mean, standard deviation, skewness and kurtosis were performed using univariate Wilcoxon rank sum test across various tumor types. Bonferroni correction was used to correct for multiple comparisons testing. Multivariable logistic regression analysis was performed for discrimination between glioblastomas and metastases and area under the receiver operator curve (AUC) was calculated. Results Mean T2 values could differentiate solid tumor regions of lower-grade gliomas from metastases (mean±sd: 172±53ms and 105±27ms respectively, p =0.004, significant after Bonferroni correction). Mean T1 of PW surrounding lower-grade gliomas differed from PW around glioblastomas (mean±sd: 1066±218ms and 1578±331ms respectively, p=0.004, significant after Bonferroni correction). Logistic regression analysis revealed that mean T2 of ST offered best separation between glioblastomas and metastases with AUC of 0.86 (95% CI 0.69–1.00, p<0.0001). Conclusion MRF allows rapid simultaneous T1, T2 measurement in brain tumors and surrounding tissues. MRF based relaxometry can identify quantitative differences between solid-tumor regions of lower grade gliomas and metastases and between peritumoral regions of glioblastomas and lower grade gliomas.

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.