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Magnetic resonance imaging (MRI) is an important tool for analysis of deep brain grey matter structures. However, analysis of these structures is limited due to low intensity contrast typically found in whole brain imaging protocols. Herein, we propose a big data registration-enhancement (BDRE) technique to augment the contrast of deep brain structures using an efficient large-scale non-rigid registration strategy. Direct validation is problematic given a lack of ground truth data. Rather, we validate the usefulness and impact of BDRE for multi-atlas (MA) segmentation on two sets of structures of clinical interest: the thalamic nuclei and hippocampal subfields. The experimental design compares algorithms using T1-weighted 3 T MRI for both structures (and additional 7 T MRI for the thalamic nuclei) with an algorithm using BDRE. As baseline comparisons, a recent denoising (DN) technique and a super-resolution (SR) method are used to preprocess the original 3 T MRI. The performance of each MA segmentation is evaluated by the Dice similarity coefficient (DSC). BDRE significantly improves mean segmentation accuracy over all methods tested for both thalamic nuclei (3 T imaging: 9.1%; 7 T imaging: 15.6%; DN: 6.9%; SR: 16.2%) and hippocampal subfields (3 T T1 only: 8.7%; DN: 8.4%; SR: 8.6%). We also present DSC performance for each thalamic nucleus and hippocampal subfield and show that BDRE can help MA segmentation for individual thalamic nuclei and hippocampal subfields. This work will enable large-scale analysis of clinically relevant deep brain structures from commonly acquired T1 images.
Copyright © 2019 Elsevier Inc. All rights reserved.

We present a novel framework for characterizing paired brain networks using techniques in hyper-networks, sparse learning and persistent homology. The framework is general enough for dealing with any type of paired images such as twins, multimodal and longitudinal images. The exact nonparametric statistical inference procedure is derived on testing monotonic graph theory features that do not rely on time consuming permutation tests. The proposed method computes the exact probability in quadratic time while the permutation tests require exponential time. As illustrations, we apply the method to simulated networks and a twin fMRI study. In case of the latter, we determine the statistical significance of the heritability index of the large-scale reward network where every voxel is a network node.

Chemical exchange saturation transfer (CEST) imaging of fast exchanging amine protons at 3 ppm offset from the water resonant frequency is of practical interest, but quantification of fast exchanging pools by CEST is challenging. To effectively saturate fast exchanging protons, high irradiation powers need to be applied, but these may cause significant direct water saturation as well as non-specific semi-solid magnetization transfer (MT) effects, and thus decrease the specificity of the measured signal. In addition, the CEST signal may depend on the water longitudinal relaxation time (T ), which likely varies between tissues and with pathology, further reducing specificity. Previously, an analysis of the asymmetry of saturation effects (MTR ) has been commonly used to quantify fast exchanging amine CEST signals. However, our results show that MTR is greatly affected by the above factors, as well as asymmetric MT and nuclear Overhauser enhancement (NOE) effects. Here, we instead applied a relatively more specific inverse analysis method, named AREX (apparent exchange-dependent relaxation), that has previously been applied only to slow and intermediate exchanging solutes. Numerical simulations and controlled phantom experiments show that, although MTR depends on T and semi-solid content, AREX acquired in steady state does not, which suggests that AREX is more specific than MTR . By combining with a fitting approach instead of using the asymmetric analysis to obtain reference signals, AREX can also avoid contaminations from asymmetric MT and NOE effects. Animal experiments show that these two quantification methods produce differing contrasts between tumors and contralateral normal tissues in rat brain tumor models, suggesting that conventional MTR applied in vivo may be influenced by variations in T , semi-solid content, or NOE effect. Thus, the use of MTR may lead to misinterpretation, while AREX with corrections for competing effects likely enhances the specificity and accuracy of quantification to fast exchanging pools.
Copyright © 2017 John Wiley & Sons, Ltd.

Accurate quantification of chemical exchange saturation transfer (CEST) effects, including dipole-dipole mediated relayed nuclear Overhauser enhancement (rNOE) saturation transfer, is important for applications and studies of molecular concentration and transfer rate (and thereby pH or temperature). Although several quantification methods, such as Lorentzian difference (LD) analysis, multiple-pool Lorentzian fits, and the three-point method, have been extensively used in several preclinical and clinical applications, the accuracy of these methods has not been evaluated. Here we simulated multiple-pool Z spectra containing the pools that contribute to the main CEST and rNOE saturation transfer signals in the brain, numerically fit them using the different methods, and then compared their derived CEST metrics with the known solute concentrations and exchange rates. Our results show that the LD analysis overestimates contributions from amide proton transfer (APT) and intermediate exchanging amine protons; the three-point method significantly underestimates both APT and rNOE saturation transfer at -3.5 ppm (NOE(-3.5)). The multiple-pool Lorentzian fit is more accurate than the other two methods, but only at lower irradiation powers (≤1 μT at 9.4 T) within the range of our simulations. At higher irradiation powers, this method is also inaccurate because of the presence of a fast exchanging CEST signal that has a non-Lorentzian lineshape. Quantitative parameters derived from in vivo images of rodent brain tumor obtained using an irradiation power of 1 μT were also compared. Our results demonstrate that all three quantification methods show similar contrasts between tumor and contralateral normal tissue for both APT and the NOE(-3.5). However, the quantified values of the three methods are significantly different. Our work provides insight into the fitting accuracy obtainable in a complex tissue model and provides guidelines for evaluating other newly developed quantification methods.
Copyright © 2017 John Wiley & Sons, Ltd.

RF arrays with a large number of independent coil elements are advantageous for parallel transmission (pTx) and reception at high fields. One of the main challenges in designing RF arrays is to minimize the electromagnetic (EM) coupling between the coil elements. The induced current elimination (ICE) method, which uses additional resonator elements to cancel coils' mutual EM coupling, has proven to be a simple and efficient solution for decoupling microstrip, L/C loop, monopole and dipole arrays. However, in previous embodiments of conventional ICE decoupling, the decoupling elements acted as "magnetic-walls" with low transmit fields and consequently low MR signal near them. To solve this problem, new resonator geometries including overlapped and perpendicular decoupling loops are proposed. The new geometries were analyzed theoretically and validated in EM simulations, bench tests and MR experiments. The isolation between two closely-placed loops could be improved from about -5dB to <-45dB by using the new geometries.
Copyright © 2017 Elsevier Inc. All rights reserved.

PURPOSE - MRI of cortical bone has the potential to offer new information about fracture risk. Current methods are typically performed with 3D acquisitions, which suffer from long scan times and are generally limited to extremities. This work proposes using 2D UTE with half pulses for quantitatively mapping bound and pore water in cortical bone.
METHODS - Half-pulse 2D UTE methods were implemented on a 3T Philips Achieva scanner using an optimized slice-select gradient waveform, with preparation pulses to selectively image bound or pore water. The 2D methods were quantitatively compared with previously implemented 3D methods in the tibia in five volunteers.
RESULTS - The mean difference between bound and pore water concentration acquired from 3D and 2D sequences was 0.6 and 0.9 mol H/L (3 and 12%, respectively). While 2D pore water methods tended to slightly overestimate concentrations relative to 3D methods, differences were less than scan-rescan uncertainty and expected differences between healthy and fracture-prone bones.
CONCLUSION - Quantitative bound and pore water concentration mapping in cortical bone can be accelerated by 2 orders of magnitude using 2D protocols with optimized half-pulse excitation. Magn Reson Med 77:945-950, 2017. © 2017 International Society for Magnetic Resonance in Medicine.
© 2017 International Society for Magnetic Resonance in Medicine.

Sickle cell anemia (SCA) is a genetic disorder resulting in reduced oxygen carrying capacity and elevated stroke risk. Pseudo-continuous arterial spin labeling (pCASL) measures of cerebral blood flow (CBF) may have relevance for stroke risk assessment; however, the effects of elevated flow velocity and reduced bolus arrival time (BAT) on CBF quantification in SCA patients have not been thoroughly characterized, and pCASL model parameters used in healthy adults are often applied to patients with SCA. Here, cervical arterial flow velocities and pCASL labeling efficiencies were computed in adults with SCA (n = 19) and age- and race-matched controls without sickle trait (n = 7) using pCASL in sequence with phase contrast MR angiography (MRA). Controls (n = 7) and a subgroup of patients (n = 8) also underwent multi-post-labeling-delay pCASL for BAT assessment. Mean flow velocities were elevated in SCA adults (velocity = 28.3 ± 4.1 cm/s) compared with controls (velocity = 24.5 ± 3.8 cm/s), and mean pCASL labeling efficiency (α) was reduced in SCA adults (α = 0.72) relative to controls (α = 0.91). In patients, mean whole-brain CBF from phase contrast MRA was 91.8 ± 18.1 ml/100 g/min, while mean pCASL CBF when assuming a constant labeling efficiency of 0.86 was 75.2 ± 17.3 ml/100 g/min (p < 0.01), resulting in a mean absolute quantification error of 23% when a labeling efficiency appropriate for controls was assumed. This difference cannot be accounted for by BAT (whole-brain BAT: control, 1.13 ± 0.06 s; SCA, 1.02 ± 0.09 s) or tissue T variation. In conclusion, BAT variation influences pCASL quantification less than elevated cervical arterial velocity and labeling efficiency variation in SCA adults; thus, a lower labeling efficiency (α = 0.72) or subject-specific labeling efficiency should be incorporated for SCA patients.
Copyright © 2017 John Wiley & Sons, Ltd.

High-magnetic-field (7 T) chemical exchange saturation transfer (CEST) MRI provides information on the tissue biochemical environment. Multiple sclerosis (MS) affects the entire central nervous system, including the spinal cord. Optimal CEST saturation parameters found via simulation were implemented for CEST MRI in 10 healthy controls and 10 patients with MS, and the results were examined using traditional asymmetry analysis and a Lorentzian fitting method. In addition, T1 - and T2 *-weighted images were acquired for lesion localization and the transmitted B1 (+) field was evaluated to guide imaging parameters. Distinct spectral features for all tissue types studied were found both up- and downfield from the water resonance. The z spectra in healthy subjects had the expected z spectral shape with CEST effects apparent from 2.0 to 4.5 ppm. The z spectra from patients with MS demonstrated deviations from this expected normal shape, indicating this method's sensitivity to known pathology as well as to tissues appearing normal on conventional MRI. Examination of the calculated CESTasym revealed increased asymmetry around the amide proton resonance (Δω = 3.5 ppm), but it was apparent that this measure is complicated by detail in the CEST spectrum upfield from water, which is expected to result from the nuclear Overhauser effect. The z spectra upfield (negative ppm range) were also distinct between healthy and diseased tissue, and could not be ignored, particularly when considering the conventional asymmetry analysis used to quantify the CEST effect. For all frequencies greater than +1 ppm, the Lorentzian differences (and z spectra) for lesions and normal-appearing white matter were distinct from those for healthy white matter. The increased frequency separation and signal-to-noise ratio, in concert with prolonged T1 at 7 T, resulted in signal enhancements necessary to detect subtle tissue changes not possible at lower field strengths. This study presents CEST imaging metrics that may be sensitive to the extensive and temporally varying biochemical neuropathology of MS in the spinal cord. Copyright © 2016 John Wiley & Sons, Ltd.
Copyright © 2016 John Wiley & Sons, Ltd.

Image labeling is essential for analyzing morphometric features in medical imaging data. Labels can be obtained by either human interaction or automated segmentation algorithms, both of which suffer from errors. The Simultaneous Truth and Performance Level Estimation (STAPLE) algorithm for both discrete-valued and continuous-valued labels has been proposed to find the consensus fusion while simultaneously estimating rater performance. In this paper, we first show that the previously reported continuous STAPLE in which bias and variance are used to represent rater performance yields a maximum likelihood solution in which bias is indeterminate. We then analyze the major cause of the deficiency and evaluate two classes of auxiliary bias estimation processes, one that estimates the bias as part of the algorithm initialization and the other that uses a maximum a posteriori criterion with a priori probabilities on the rater bias. We compare the efficacy of six methods, three variants from each class, in simulations and through empirical human rater experiments. We comment on their properties, identify deficient methods, and propose effective methods as solution.

The mechanical functions of muscles involve the generation of force and the actuation of movement by shortening or lengthening under load. These functions are influenced, in part, by the internal arrangement of muscle fibers with respect to the muscle's mechanical line of action. This property is known as muscle architecture. In this review, we describe the use of diffusion tensor (DT)-MRI muscle fiber tracking for the study of muscle architecture. In the first section, the importance of skeletal muscle architecture to function is discussed. In addition, traditional and complementary methods for the assessment of muscle architecture (brightness-mode ultrasound imaging and cadaver analysis) are presented. Next, DT-MRI is introduced and the structural basis for the reduced and anisotropic diffusion of water in muscle is discussed. The third section discusses issues related to the acquisition of skeletal muscle DT-MRI data and presents recommendations for optimal strategies. The fourth section discusses methods for the pre-processing of DT-MRI data, the available approaches for the calculation of the diffusion tensor and the seeding and propagating of fiber tracts, and the analysis of the tracking results to measure structural properties pertinent to muscle biomechanics. Lastly, examples are presented of how DT-MRI fiber tracking has been used to provide new insights into how muscles function, and important future research directions are highlighted. Copyright © 2016 John Wiley & Sons, Ltd.
Copyright © 2016 John Wiley & Sons, Ltd.