IMPORTANCEPer the World Health Organization 2016 integrative classification, newly diagnosed glioblastomas are separated into isocitrate dehydrogenase gene 1 or 2 (IDH)-wild-type and IDH-mutant subtypes, with median patient survival of 1.2 and 3.6 years, respectively. Although maximal resection of contrast-enhanced (CE) tumor is associated with longer survival, the prognostic importance of maximal resection within molecular subgroups and the potential importance of resection of non-contrast-enhanced (NCE) disease is poorly understood.OBJECTIVE To assess the association of resection of CE and NCE tumors in conjunction with molecular and clinical information to develop a new road map for cytoreductive surgery.

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.

The Bayesian framework and algorithm shown provide accurate solutions for the partial-volume problem in magnetic resonance fingerprinting. The flexibility of the method will allow further study into different priors and hyperpriors that can be applied in the model. Magn Reson Med 80:159-170, 2018. © 2017 International Society for Magnetic Resonance in Medicine.

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.

he peripheral zone is the most common site of prostate cancer. However, 25%-40% of prostate cancers arise from the transition and central zones (1-3). Because of their anterior and apical involvement, transition zone cancers are often missed at digital rectal examination and transrectal US-guided systematic biopsy but are detected with MRI-guided targeted biopsies and template saturation biopsies (4-7).At MRI, the transition zone has a heterogeneous appearance because of varying degrees of glandular and stromal hyperplasia and foci of prostatitis. The Prostate Imaging Reporting and Data System (PI-RADS) version 2 considers T2-weighted imaging as the most important sequence for detection and characterization of transition zone lesions, followed by diffusion-weighted (DW) imaging for upgrading certain category 3 lesions

Craniosynostosis is encountered in the pediatric population in isolated or syndromic forms. The resulting deformity depends on the number and type of sutures involved and, in multi-sutural synostosis, the order of suture fusion. Primary craniosynostosis needs to be differentiated from the secondary variety and positional or deformational mimics. Syndromic craniosynostoses are associated with other craniofacial deformities. Evaluation with 3-D CT plays an important role in accurate diagnosis and management; however, implementation of appropriate CT techniques is essential to limit the radiation burden in these children. In this article, the authors briefly review the classification, embryopathogenesis and epidemiology and describe in detail the radiologic appearance and differential diagnoses of craniosynostosis.

Magnetic resonance fingerprinting (MRF) is a quantitative imaging technique that maps multiple tissue properties through pseudorandom signal excitation and dictionary-based reconstruction.The aim of this study is to estimate and validate partial volumes from MRF signal evolutions (PV-MRF), and to characterize possible sources of error.Partial volume model inversion (pseudoinverse) and dictionary-matching approaches to calculate brain tissue fractions (cerebrospinal fluid, gray matter, white matter) were compared in a numerical phantom and seven healthy subjects scanned at 3 T. Results were validated by comparison with ground truth in simulations and ROI analysis in vivo. Simulations investigated tissue fraction errors arising from noise, undersampling artifacts, and model errors. An expanded partial volume model was investigated in a brain tumor patient.PV-MRF with dictionary matching is robust to noise, and estimated tissue fractions are sensitive to model errors. A 6% error in pure tissue T 1 resulted in average absolute tissue fraction error of 4% or less. A partial volume model within these accuracy limits could be semi-automatically constructed in vivo using k-means clustering of MRFmapped relaxation times. Dictionary-based PV-MRF robustly identifies pure white matter, gray matter and cerebrospinal fluid, and partial volumes in subcortical structures. PV-MRF could also estimate partial volumes of solid tumor and peritumoral edema.We conclude that PV-MRF can attribute subtle changes in relaxation times to altered tissue composition, allowing for quantification of specific tissues which occupy a fraction of a voxel.