2 Technical Efficacy: Stage 1 J. MAGN. RESON. IMAGING 2018;48:359-368.

The aim of the study was to determine the quantitative T2 values in prostate tissue and evaluate them for detection and grading of prostate cancer. Materials and Methods: After approval from the local ethics committee, morphological T2-weighted (T2w) images, apparent diffusion coefficient (ADC) maps from diffusion-weighted images, quantitative T2 maps, and calculated T2w images from 75 men (median age, 66.3 years; median PSA, 8.2 ng/mL) were acquired at 3 T magnetic resonance imaging (MRI). Data were retrospectively evaluated for their distinction between prostate pathologies.Eight hundred fifty-seven areas of normal gland (n = 378), prostate cancer (54x Gleason score 6, 98x Gleason score 7, 25x Gleason score 8), benign prostatic hyperplasia (BPH) nodes (n = 150), prostatitis (n = 119), and precancerous lesions (n = 33) were determined on calculated and morphological T2w images. Histological criterion standards were whole gland sections (16 patients), MRI-guided in-bore biopsies (32 patients), MRI/transrectal ultrasound-fusion biopsies (15 patients), and systematic 12-core transrectal ultrasound-guided biopsies (12 patients). Significance was assumed to be P < 0.05. Results:The quantitative T2 values vary significantly between prostate cancer and normal gland tissue (area under the curve [AUC], 0.871), cancer and BPH nodes (AUC = 0.827), and Gleason score 6 and 7 or higher (AUC, 0.742). The quantitative T2 values decrease with increasing Gleason scores and correlate significantly with the ADC values (r = 0.806).The detection accuracy of prostate cancer on calculated (AUC = 0.682) and morphological T2w images (AUC = 0.658) is not significantly different. Conclusions: Quantitative T2 values seem to be suitable for distinguishing between prostate cancer and normal gland tissue or BPH nodes. Similar to the ADC values, they offer an indication of the aggressiveness of the prostate cancer.

Purpose To study the effects of magnetization transfer (MT, in which a semi‐solid spin pool interacts with the free pool), in the context of magnetic resonance fingerprinting (MRF). Methods Simulations and phantom experiments were performed to study the impact of MT on the MRF signal and its potential influence on T1 and T2 estimation. Subsequently, an MRF sequence implementing off‐resonance MT pulses and a dictionary with an MT dimension, generated by incorporating a two‐pool model, were used to estimate the fractional pool size in addition to the B1+, T1, and T2 values. The proposed method was evaluated in the human brain. Results Simulations and phantom experiments showed that an MRF signal obtained from a cross‐linked bovine serum sample is influenced by MT. Using a dictionary based on an MT model, a better match between simulations and acquired MR signals can be obtained (NRMSE 1.3% vs. 4.7%). Adding off‐resonance MT pulses can improve the differentiation of MT from T1 and T2. In vivo results showed that MT affects the MRF signals from white matter (fractional pool‐size ~16%) and gray matter (fractional pool‐size ~10%). Furthermore, longer T1 (~1060 ms vs. ~860 ms) and T2 values (~47 ms vs. ~35 ms) can be observed in white matter if MT is accounted for. Conclusion Our experiments demonstrated a potential influence of MT on the quantification of T1 and T2 with MRF. A model that encompasses MT effects can improve the accuracy of estimated relaxation parameters and allows quantification of the fractional pool size.

Purpose MP2RAGE T1‐weighted imaging has been shown to be beneficial for various applications, mainly because of its good grey‐white matter contrast, its B1‐robustness and ability to derive T1 maps. Even using parallel imaging, the method requires long acquisition times, especially at high resolution. This work aims at accelerating MP2RAGE imaging using compressed sensing. Methods A pseudo‐phyllotactic Cartesian MP2RAGE readout was implemented allowing for flexible reordering and undersampling factors. The sampling pattern was first optimized based on fully sampled data and a compressed sensing reconstruction. Changes in contrast ratios, automated brain segmentation results, and quantitative T1 values were used for benchmarking. In vivo undersampled data from eleven healthy subjects were then acquired using a 4‐fold acceleration with the optimized sampling pattern. The resulting images were compared to the standard parallel imaging MP2RAGE protocol by visual inspection and using the above quality metrics. Results The application of incoherent undersampling and iterative compressed sensing reconstruction on MP2RAGE acquisitions allows for a 57% time reduction (corresponding to 4‐fold undersampling with maintained reference lines, TA = 3:35 minutes) compared to the reference protocol using parallel imaging (GRAPPAx3 acceleration, TA = 8:22 minutes) while obtaining images with similar image quality, morphometric (volume differences = [0.07 ± 1.2–3.8 ± 1.9]%) and T1‐mapping outcomes (T1 error = [6 ± 5.1–37 ± 12.3] ms depending on the different structures). Conclusion A whole‐brain MP2RAGE acquisition is feasible with compressed sensing in less than 4 minutes without appreciably compromising image quality.

Rapid and efficient transmission of electric signals among neurons of vertebrates is ensured by myelin‐insulating sheaths surrounding axons. Human cognition, sensation, and motor functions rely on the integrity of these layers, and demyelinating diseases often entail serious cognitive and physical impairments. Magnetic resonance imaging radically transformed the way these disorders are monitored, offering an irreplaceable tool to noninvasively examine the brain structure. Several advanced techniques based on MRI have been developed to provide myelin‐specific contrasts and a quantitative estimation of myelin density in vivo. Here, the vast offer of acquisition strategies developed to date for this task is reviewed. Advantages and pitfalls of the different approaches are compared and discussed.

Diffusion-weighted imaging (DWI) provides information that allows the estimation of white-matter (WM) fibre orientation and distribution, but it does not provide information about myelin density, fibre concentration or fibre size within each voxel. On the other hand, quantitative relaxation contrasts (like the apparent transverse relaxation, R2∗) offer iron and myelin-related contrast, but their dependence on the orientation of microstructure with respect to the applied magnetic field, B , is often neglected. The aim of this work was to combine the fibre orientation information retrieved from the DWI acquisition and the sensitivity to microstructural information from quantitative relaxation parameters. The in vivo measured quantitative transverse relaxation maps (R and R2∗) were decomposed into their orientation-dependent and independent components, using the DWI fibre orientation information as prior knowledge. The analysis focused on major WM fibre bundles such as the forceps major (FMj), forceps minor (FMn), cingulum (CG) and corticospinal tracts (CST). The orientation-dependent R parameters, despite their small size (0-1.5 Hz), showed higher variability across different fibre populations, while those derived from R2∗, although larger (3.1-4.5 Hz), were mostly bundle-independent. With this article, we have, for the first time, attempted the in vivo characterization of the orientation-(in)dependent components of the transverse relaxation rates and demonstrated that the orientation of WM fibres influences both R and R2∗ contrasts.

Background and objectivesQuantitative MRI (qMRI) permits the quantification of brain changes compatible with inflammation, degeneration and repair in multiple sclerosis (MS) patients. In this study, we propose a new method to provide personalized maps of tissue alterations and longitudinal brain changes based on different qMRI metrics, which provide complementary information about brain pathology.MethodsWe performed baseline and two-years follow-up on (i) 13 relapsing-remitting MS patients and (ii) four healthy controls. A group consisting of up to 65 healthy controls was used to compute the reference distribution of qMRI metrics in healthy tissue. All subjects underwent 3T MRI examinations including T1, T2, T2* relaxation and Magnetization Transfer Ratio (MTR) imaging. We used a recent partial volume estimation algorithm to estimate the concentration of different brain tissue types on T1 maps; then, we computed a deviation map (z-score map) for each contrast at both time-points. Finally, we subtracted those deviation maps only for voxels showing a significant difference with healthy tissue in one of the time points, to obtain a difference map for each subject.Results and conclusionControl subjects did not show any significant z-score deviations or longitudinal z-score changes. On the other hand, MS patients showed brain regions with cross-sectional and longitudinal concomitant increase in T1, T2, T2* z-scores and decrease of MTR z-scores, suggesting brain tissue degeneration/loss. In the lesion periphery, we observed areas with cross-sectional and longitudinal decreased T1/T2 and slight decrease in T2* most likely related to iron accumulation. Moreover, we measured longitudinal decrease in T1, T2 - and to a lesser extent in T2* - as well as a concomitant increase in MTR, suggesting remyelination/repair.In summary, we have developed a method that provides whole-brain personalized maps of cross-sectional and longitudinal changes in MS patients, which are computed in patient space. These maps may open new perspectives to complement and support radiological evaluation of brain damage for a given patient.

Purpose To exploit the improved comparability and hardware independency of quantitative MRI, databases of MR physical parameters in healthy tissue are required, to which tissue properties of patients can be compared. In this work, normative values for longitudinal and transverse relaxation times in the brain were established and tested in single‐subject comparisons for detection of abnormal relaxation times. Methods Relaxometry maps of the brain were acquired from 52 healthy volunteers. After spatially normalizing the volumes into a common space, T1 and T2 inter‐subject variability within the healthy cohort was modeled voxel‐wise. A method for a single‐subject comparison against the atlases was developed by computing z‐scores with respect to the established healthy norms. The comparison was applied to two multiple sclerosis and one clinically isolated syndrome cases for a proof of concept. Results The established atlases exhibit a low variation in white matter structures (median RMSE of models equal to 32 ms for T1 and 4 ms for T2), indicating that relaxation times are in a narrow range for normal tissues. The proposed method for single‐subject comparison detected relaxation time deviations from healthy norms in the example patient data sets. Relaxation times were found to be increased in brain lesions (mean z‐scores >5). Moreover, subtle and confluent differences (z‐scores ~2–4) were observed in clinically plausible regions (between lesions, corpus callosum). Conclusions Brain T1 and T2 quantitative norms were derived voxel‐wise with low variability in healthy tissue. Example patient deviation maps demonstrated good sensitivity of the atlases for detecting relaxation time alterations.