Tumor hypoxia represses γδ T cell-mediated antitumor immunity against brain tumors.

The anatomic location and immunologic characteristics of brain tumors result in strong lymphocyte suppression. Consequently, conventional immunotherapies targeting CD8 T cells are ineffective against brain tumors. Tumor cells escape immunosurveillance by various mechanisms and tumor cell metabolism can affect the metabolic states and functions of tumor-infiltrating lymphocytes. Here, we discovered that brain tumor cells had a particularly high demand for oxygen, which affected γδ T cell-mediated antitumor immune responses but not those of conventional T cells. Specifically, tumor hypoxia activated the γδ T cell protein kinase A pathway at a transcriptional level, resulting in repression of the activatory receptor NKG2D. Alleviating tumor hypoxia reinvigorated NKG2D expression and the antitumor function of γδ T cells. These results reveal a hypoxia-mediated mechanism through which brain tumors and γδ T cells interact and emphasize the importance of γδ T cells for antitumor immunity against brain tumors.

Meningeal lymphatic vessels at the skull base drain cerebrospinal fluid.

Recent work has shown that meningeal lymphatic vessels (mLVs), mainly in the dorsal part of the skull, are involved in the clearance of cerebrospinal fluid (CSF), but the precise route of CSF drainage is still unknown. Here we reveal the importance of mLVs in the basal part of the skull for this process by visualizing their distinct anatomical location and characterizing their specialized morphological features, which facilitate the uptake and drainage of CSF. Unlike dorsal mLVs, basal mLVs have lymphatic valves and capillaries located adjacent to the subarachnoid space in mice. We also show that basal mLVs are hotspots for the clearance of CSF macromolecules and that both mLV integrity and CSF drainage are impaired with ageing. Our findings should increase the understanding of how mLVs contribute to the neuropathophysiological processes that are associated with ageing.

SPINNED: Simulation-based physics-informed neural network for deconvolution of dynamic susceptibility contrast MRI perfusion data.

PURPOSE: To propose the simulation-based physics-informed neural network for deconvolution of dynamic susceptibility contrast (DSC) MRI (SPINNED) as an alternative for more robust and accurate deconvolution compared to existing methods. METHODS: The SPINNED method was developed by generating synthetic tissue residue functions and arterial input functions through mathematical simulations and by using them to create synthetic DSC MRI time series. The SPINNED model was trained using these simulated data to learn the underlying physical relation (deconvolution) between the DSC-MRI time series and the arterial input functions. The accuracy and robustness of the proposed SPINNED method were assessed by comparing it with two common deconvolution methods in DSC MRI data analysis, circulant singular value decomposition, and Volterra singular value decomposition, using both simulation data and real patient data. RESULTS: The proposed SPINNED method was more accurate than the conventional methods across all SNR levels and showed better robustness against noise in both simulation and real patient data. The SPINNED method also showed much faster processing speed than the conventional methods. CONCLUSION: These results support that the proposed SPINNED method can be a good alternative to the existing methods for resolving the deconvolution problem in DSC MRI. The proposed method does not require any separate ground-truth measurement for training and offers additional benefits of quick processing time and coverage of diverse clinical scenarios. Consequently, it will contribute to more reliable, accurate, and rapid diagnoses in clinical applications compared with the previous methods including those based on supervised learning.

Quantitative mapping of renal oxygen consumption using pseudo-continuous arterial spin labeling and quantitative susceptibility mapping in humans.

PURPOSE: To propose a new method for quantitatively mapping the renal metabolic rate of oxygen (RMRO2) and to evaluate the proposed method using a caffeine challenge. THEORY AND METHODS: Pseudo-continuous arterial spin labeling (pCASL) and QSM sequences were used to obtain MR images in the kidney. Six healthy volunteers were scanned on caffeine and control days. The pCASL and QSM images were registered using DICOM information and rigid translation. The Fick principle was applied to estimate RMRO2. The results on caffeine and control days were compared to evaluate the capability of the proposed method to estimate renal oxygen consumption. A paired t-test was used to assess the statistical significance. RESULTS: Estimated renal blood flow (RBF), QSM, and RMRO2 maps were consistent with those reported in the literature. RMRO2 values were higher than the cerebral metabolic rate of oxygen (CMRO2) and were significantly reduced on the caffeine days compared to the control days, consistent with findings from non-MRI literature. CONCLUSION: The feasibility of measuring renal oxygen consumption using pCASL and QSM images was demonstrated. To the best of our knowledge, this work provides quantitative maps of renal oxygen consumption in humans for the first time. The results were consistent with the literature, including the statistically significant reduction in renal oxygen consumption with caffeine challenge. These findings suggest the potential utility of our technique in measuring renal oxygen consumption noninvasively, especially for patients with complications associated with contrast agents.

Measurement of changes in cerebrospinal fluid pulsation after traumatic brain injury using echo-planar imaging-based functional MRI.

Traumatic brain injury (TBI) is a major public health concern worldwide, with a high incidence and a significant impact on morbidity and mortality. The alteration of cerebrospinal fluid (CSF) dynamics after TBI is a well-known phenomenon; however, the underlying mechanisms and their implications for cognitive function are not fully understood. In this study, we propose a new approach to studying the alteration of CSF dynamics in TBI patients. Our approach involves using conventional echo-planar imaging-based functional MRI with no additional scan, allowing for simultaneous assessment of functional CSF dynamics and blood oxygen level-dependent-based functional brain activities. We utilized two previously suggested indices of (i) CSFpulse, and (ii) correlation between global brain activity and CSF inflow. Using CSFpulse, we demonstrated a significant decrease in CSF pulsation following TBI (p < 0.05), which was consistent with previous studies. Furthermore, we confirmed that the decrease in CSF pulsation was most prominent in the early months after TBI, which could be explained by ependymal ciliary loss, intracranial pressure increment, or aquaporin-4 dysregulation. We also observed a decreasing trend in the correlation between global brain activity and CSF inflow in TBI patients (p < 0.05). Our findings suggest that the decreased CSF pulsation after TBI could lead to the accumulation of toxic substances in the brain and an adverse effect on brain function. Further longitudinal studies with larger sample sizes, TBI biomarker data, and various demographic information are needed to investigate the association between cognitive decline and CSF dynamics after TBI. Overall, this study sheds light on the potential role of altered CSF dynamics in TBI-induced neurologic symptoms and may contribute to the development of novel therapeutic interventions.

Human glioblastoma arises from subventricular zone cells with low-level driver mutations.

Glioblastoma (GBM) is a devastating and incurable brain tumour, with a median overall survival of fifteen months1,2. Identifying the cell of origin that harbours mutations that drive GBM could provide a fundamental basis for understanding disease progression and developing new treatments. Given that the accumulation of somatic mutations has been implicated in gliomagenesis, studies have suggested that neural stem cells (NSCs), with their self-renewal and proliferative capacities, in the subventricular zone (SVZ) of the adult human brain may be the cells from which GBM originates3-5. However, there is a lack of direct genetic evidence from human patients with GBM4,6-10. Here we describe direct molecular genetic evidence from patient brain tissue and genome-edited mouse models that show astrocyte-like NSCs in the SVZ to be the cell of origin that contains the driver mutations of human GBM. First, we performed deep sequencing of triple-matched tissues, consisting of (i) normal SVZ tissue away from the tumour mass, (ii) tumour tissue, and (iii) normal cortical tissue (or blood), from 28 patients with isocitrate dehydrogenase (IDH) wild-type GBM or other types of brain tumour. We found that normal SVZ tissue away from the tumour in 56.3% of patients with wild-type IDH GBM contained low-level GBM driver mutations (down to approximately 1% of the mutational burden) that were observed at high levels in their matching tumours. Moreover, by single-cell sequencing and laser microdissection analysis of patient brain tissue and genome editing of a mouse model, we found that astrocyte-like NSCs that carry driver mutations migrate from the SVZ and lead to the development of high-grade malignant gliomas in distant brain regions. Together, our results show that NSCs in human SVZ tissue are the cells of origin that contain the driver mutations of GBM.

SIMPLEX: Multiple phase-cycled bSSFP quantitative magnetization transfer imaging with physic-guided simulation learning of neural network.

Most quantitative magnetization transfer (qMT) imaging methods require acquiring additional quantitative maps (such as T1) for data fitting. A method based on multiple phase-cycled bSSFP was recently proposed to enable high-resolution 3D qMT imaging based on least square fitting without any extra acquisition, and thus has high potential for simplifying the qMT procedure. However, the quantification of qMT parameters with this method was suboptimal, limiting its potential for clinical application despite its simpler protocol and higher spatial resolution. To improve the fitting of qMT data obtained with multiple phase-cycled bSSFP, we propose SIMulation-based Physics-guided Learning of neural network for qMT parameters EXtraction, or SIMPLEX. In contrast to previous deep learning supervised approaches for quantitative MR that require the acquisition of input data and corresponding ground truth for training, we leveraged the MR signal model to generate training samples without expensive data curation. The network was trained exclusively with simulation data by predicting the simulation parameters. The same network was applied directly to in-vivo data without additional training. The approach was verified with both simulation and in-vivo data. SIMPLEX showed a decrease in fitting mean squared error for all simulation data compared to the existing least-square fitting method. The in-vivo experiment revealed that the network performed well with the real in vivo data unseen during training. For all experiments, we observed that SIMPLEX consistently improved the quantification quality of the qMT parameters whilst being more robust to noise compared to the prior technique. The proposed SIMPLEX will expedite the routine clinical application of qMT by providing qMT parameters (exchange rate, pool fraction) as well as T1, T2, and ΔB0 maps simultaneously with high spatial resolution, better reliability, and reduced processing time.

Perfusion Maps Acquired From Dynamic Angiography MRI Using Deep Learning Approaches.

BACKGROUND: A typical stroke MRI protocol includes perfusion-weighted imaging (PWI) and MR angiography (MRA), requiring a second dose of contrast agent. A deep learning method to acquire both PWI and MRA with single dose can resolve this issue. PURPOSE: To acquire both PWI and MRA simultaneously using deep learning approaches. STUDY TYPE: Retrospective. SUBJECTS: A total of 60 patients (30-73 years old, 31 females) with ischemic symptoms due to occlusion or ≥50% stenosis (measured relative to proximal artery diameter) of the internal carotid artery, middle cerebral artery, or anterior cerebral artery. The 51/1/8 patient data were used as training/validation/test. FIELD STRENGTH/SEQUENCE: A 3 T, time-resolved angiography with stochastic trajectory (contrast-enhanced MRA) and echo planar imaging (dynamic susceptibility contrast MRI, DSC-MRI). ASSESSMENT: We investigated eight different U-Net architectures with different encoder/decoder sizes and with/without an adversarial network to generate perfusion maps from contrast-enhanced MRA. Relative cerebral blood volume (rCBV), relative cerebral blood flow (rCBF), mean transit time (MTT), and time-to-max (Tmax ) were mapped from DSC-MRI and used as ground truth to train the networks and to generate the perfusion maps from the contrast-enhanced MRA input. STATISTICAL TESTS: Normalized root mean square error, structural similarity (SSIM), peak signal-to-noise ratio (pSNR), DICE, and FID scores were calculated between the perfusion maps from DSC-MRI and contrast-enhanced MRA. One-tailed t-test was performed to check the significance of the improvements between networks. P values < 0.05 were considered significant. RESULTS: The four perfusion maps were successfully extracted using the deep learning networks. U-net with multiple decoders and enhanced encoders showed the best performance (pSNR 24.7 ± 3.2 and SSIM 0.89 ± 0.08 for rCBV). DICE score in hypo-perfused area showed strong agreement between the generated perfusion maps and the ground truth (highest DICE: 0.95 ± 0.04). DATA CONCLUSION: With the proposed approach, dynamic angiography MRI may provide vessel architecture and perfusion-relevant parameters simultaneously from a single scan. EVIDENCE LEVEL: 3 TECHNICAL EFFICACY: Stage 5.

Measurement of CSF pulsation from EPI-based human fMRI.

It is recently discovered that the glymphatic system and meningeal lymphatic system are the primary routes for the clearance of brain waste products. The CSF flow is part of these systems, facilitating the clearance procedure. Nonetheless, the relationship between CSF flow and brain functional activity has been underexplored. To investigate CSF dynamics and functional brain activity simultaneously, recent studies have proposed a CSF inflow index measured on edge slices (CSFedge) of echo-planar imaging (EPI) based functional magnetic resonance imaging (fMRI), however, it lacks the quantitative aspect of the CSF pulsation. We proposed a new method for quantifying CSF pulsation (CSFpulse) based on an interslice CSF pulsation model in the 4th ventricle of EPI-based fMRI. The proposed CSFpulse successfully detected the higher CSF flow during the resting state than the typical task states (visual and motor) (p<.05), which is consistent with previous studies based on phase contrast (PC) MRI and CSF volume MRI, while it was not detected in CSFedge based indices or baseline CSF signals in various regions of interest (ROIs). Moreover, CSFpulse demonstrated dynamic functional changes in CSF pulsation: it decreased during the activation-on blocks while it increased during the activation-off blocks. CSFpulse significantly correlated with stroke volume measured using PC MRI, a standard method for CSF pulsation quantification, under the same functional state, while CSFedge based indices or CSF ROIs showed no correlation with the PC MRI stroke volume. Lastly, the correlation of CSFpulse with global BOLD was weaker than that of CSFedge, suggesting that CSFpulse may reflect distinct CSF physiological information that is less affected by global BOLD effects. Based on these results, the proposed CSFpulse provides CSF pulsatility information more accurately in a quantitative manner than CSFedge based indices from the recent CSF studies or the conventional ROI-based analysis. In addition to the high correlation with PC MRI, CSFpulse is much faster than PC MRI and provides information of functional brain activations simultaneously, advantageous over PC MRI or CSF volume MRI. Accordingly, the suggested CSFpulse can be used for investigating intra-subject functional changes in BOLD and CSF pulsation simultaneously and inter-subject CSF pulsation variations based on conventional EPI-based fMRI, which warrants further investigation.

Measuring glomerular blood transfer rate in kidney using diffusion-weighted arterial spin labeling.

PURPOSE: To propose a two-compartment renal perfusion model for calculating glomerular blood transfer rate ( k G $$ {k}_G $$ ) as a new measure of renal function. THEORY: The renal perfusion signal was divided into preglomerular and postglomerular flows according to flow velocity. By analyzing perfusion signals acquired with and without diffusion gradients, we estimated k G $$ {k}_G $$ , the blood transfer rate from the afferent arterioles into the glomerulus. METHODS: A multislice multidelay diffusion-weighted arterial spin labeling sequence was applied to subjects with no history of renal dysfunctions. In the multiple b-value experiment, images were acquired with seven b-values to validate the bi-exponential decays of the renal perfusion signal and to determine the appropriate b-value for suppressing preglomerular flow. In the caffeine challenge, six subjects were scanned twice on the caffeine day and the control day. The k G $$ {k}_G $$ values of the two dates were compared. RESULTS: The perfusion signal showed a bi-exponential decay with b-values. There was no significant difference in renal blood flow and arterial transit time between caffeine and control days. In contrast, cortical k G $$ {k}_G $$ was significantly higher on the caffeine day (caffeine day: 106 . 0 ± 20 . 3 $$ 106.0\pm 20.3 $$ min - 1 $$ {}^{-1} $$ control day: 78 . 8 ± 22 . 9 $$ 78.8\pm 22.9 $$ min - 1 $$ {}^{-1} $$ ). These results were consistent with those from the literature. CONCLUSION: We showed that the perfusion signal consists of two compartments of preglomerular flow and postglomerular flow. The proposed diffusion-weighted arterial spin labeling could measure the glomerular blood transfer rate ( k G $$ {k}_G $$ ), which was sensitive enough to noninvasively monitor the caffeine-induced vasodilation of afferent arterioles.

Varying undersampling directions for accelerating multiple acquisition magnetic resonance imaging.

In this study, we propose a new sampling strategy for efficiently accelerating multiple acquisition MRI. The new sampling strategy is to obtain data along different phase-encoding directions across multiple acquisitions. The proposed sampling strategy was evaluated in multicontrast MR imaging (T1, T2, proton density) and multiple phase-cycled (PC) balanced steady-state free precession (bSSFP) imaging by using convolutional neural networks with central and random sampling patterns. In vivo MRI acquisitions as well as a public database were used to test the concept. Based on both visual inspection and quantitative analysis, the proposed sampling strategy showed better performance than sampling along the same phase-encoding direction in both multicontrast MR imaging and multiple PC-bSSFP imaging, regardless of sampling pattern (central, random) or datasets (public, retrospective and prospective in vivo). For the prospective in vivo applications, acceleration was performed by sampling along different phase-encoding directions at the time of acquisition with a conventional rectangular field of view, which demonstrated the advantage of the proposed sampling strategy in the real environment. Preliminary trials on compressed sensing (CS) also demonstrated improvement of CS with the proposed idea. Sampling along different phase-encoding directions across multiple acquisitions is advantageous for accelerating multiacquisition MRI, irrespective of sampling pattern or datasets, with further improvement through transfer learning.

Single-shot pseudo-centric EPI for magnetization-prepared imaging.

PURPOSE: To implement a single-shot centric-reordered EPI (1sh-CenEPI), which reduces TE significantly, thus enabling to improve SNR for magnetization-prepared imaging. METHODS: We proposed a 1sh-CenEPI in which grouped oscillating readout gradients, phase-encoding blips within each group, and big phase-encoding jumps between two consecutive groups are incorporated to encode whole k-space from the center to the edges in a single shot. The concept was tested on phantoms and human brains at 3 T. In addition, the proposed reordering scheme was applied to pseudo-continuous arterial spin labeling for evaluating the efficiency of the centric reordering in magnetization-prepared imaging. RESULTS: The proposed 1sh-CenEPI reduced TE from 50 ms to 1.4 ms for gradient-echo EPI, and from 100 ms to 7 ms for spin-echo EPI, while the elongation of readout duration was below 10% of the whole readout duration in most cases. The 1sh-CenEPI images exhibited no distinct geometric distortion both in phantom and human brain, comparable to the conventional two-shot center-out EPI. In pseudo-continuous arterial spin labeling results, 3-fold temporal SNR increase and 2-fold spatial SNR increase in the perfusion-weighted images were achieved with 1sh-CenEPI compared with the conventional linear ordering, whereas the cerebral blood flow values were consistent with previous studies. CONCLUSION: The proposed 1sh-CenEPI significantly reduced TE while maintaining similar readout window and providing images comparable to the conventional linear and multishot center-out EPI images. It can be a qualified candidate as a new readout for various magnetization-prepared imaging techniques.

qMTNet: Accelerated quantitative magnetization transfer imaging with artificial neural networks.

PURPOSE: To develop a set of artificial neural networks, collectively termed qMTNet, to accelerate data acquisition and fitting for quantitative magnetization transfer (qMT) imaging. METHODS: Conventional and interslice qMT data were acquired with two flip angles at six offset frequencies from seven subjects for developing the networks and from four young and four older subjects for testing the generalizability. Two subnetworks, qMTNet-acq and qMTNet-fit, were developed and trained to accelerate data acquisition and fitting, respectively. qMTNet-2 is the sequential application of qMTNet-acq and qMTNet-fit to produce qMT parameters (exchange rate, pool fraction) from undersampled qMT data (two offset frequencies rather than six). qMTNet-1 is one single integrated network having the same functionality as qMTNet-2. qMTNet-fit was compared with a Gaussian kernel-based fitting. qMT parameters generated by the networks were compared with those from ground truth fitted with a dictionary-driven approach. RESULTS: The proposed networks achieved high peak signal-to-noise ratio (>30) and structural similarity index (>97) in reference to the ground truth. qMTNet-fit produced qMT parameters in concordance with the ground truth with better performance than the Gaussian kernel-based fitting. qMTNet-2 and qMTNet-1 could accelerate data acquisition at threefold and accelerate fitting at 5800- and 4218-fold, respectively. qMTNet-1 showed slightly better performance than qMTNet-2, whereas qMTNet-2 was more flexible for applications. CONCLUSION: The proposed single (qMTNet-1) and two joint neural networks (qMTNet-2) can accelerate qMT workflow for both data acquisition and fitting significantly. qMTNet has the potential to accelerate qMT imaging for clinical applications, which warrants further investigation.

Quantitative susceptibility map reconstruction using annihilating filter-based low-rank Hankel matrix approach.

PURPOSE: Quantitative susceptibility mapping (QSM) inevitably suffers from streaking artifacts caused by zeros on the conical surface of the dipole kernel in k-space. This work proposes a novel and accurate QSM reconstruction method based on k-space low-rank Hankel matrix constraint, avoiding the over-smoothing problem and streaking artifacts. THEORY AND METHODS: Based on the recent theory of annihilating filter-based low-rank Hankel matrix approach (ALOHA), QSM is formulated as deconvolution under low-rank Hankel matrix constraint in the k-space. The computational complexity and the high memory burden were reduced by successive reconstruction of 2-D planes along 3 independent axes of the 3-D phase image in Fourier domain. Feasibility of the proposed method was tested on a simulated phantom and human data and were compared with existing QSM reconstruction methods. RESULTS: The proposed ALOHA-QSM effectively reduced streaking artifacts and accurately estimated susceptibility values in deep gray matter structures, compared to the existing QSM methods. CONCLUSIONS: The suggested ALOHA-QSM algorithm successfully solves the 3-dimensional QSM dipole inversion problem using k-space low rank property with no anatomical constraint. ALOHA-QSM can provide detailed brain structures and accurate susceptibility values with no streaking artifacts.

Artificial neural network for Slice Encoding for Metal Artifact Correction (SEMAC) MRI.

PURPOSE: To develop new artificial neural networks (ANNs) to accelerate slice encoding for metal artifact correction (SEMAC) MRI. METHODS: Eight titanium phantoms and 77 patients after brain tumor surgery involving metallic neuro-plating instruments were scanned using SEMAC at a 3T Skyra scanner. For the phantoms, proton-density, T1-, and T2-weighted images were acquired for developing both multilayer perceptron (MLP) and convolutional neural network (CNN). For the patients, T2-weighted images were acquired for developing CNN. All networks were trained with the SEMAC factor 4 or 6 as input and the factor 12 as label, yielding an acceleration factor of 3 or 2. Performance of the CNN model was compared against parallel imaging and compressed sensing on the phantom datasets. Two extra T1-weighted in vivo sets were acquired to investigate generalizability of the models to different contrasts. RESULTS: Both multilayer perceptron and CNN provided artifact-suppressed images better than the input images and comparable to the label images visually and quantitatively, a trend observable regardless of input SEMAC factor and image type (P < .01). CNN suppressed the artifacts better than multilayer perceptron, parallel imaging, and compressed sensing (P < .01). Tests on the patient datasets demonstrated clear metal artifact suppression visually and quantitatively (P < .01). Tests on T1 datasets also demonstrated clear visual metal artifact suppression. CONCLUSION: Our study introduced a new effective way of artificial neural networks to accelerate SEMAC MRI while maintaining the comparable quality of metal artifact suppression. Application on the preliminary patient datasets proved the feasibility in clinical usage, which warrants further investigation.

Rapid framework for quantitative magnetization transfer imaging with interslice magnetization transfer and dictionary-driven fitting approaches.

PURPOSE: To develop a rapid framework for quantitative magnetization transfer (qMT) imaging based on the 2D interslice MT and dictionary-driven fitting approaches. METHODS: Sequential balanced steady-state free precession (bSSFP) scanning was performed on a whole human brain in a total of 12 conditions from six different interslice gaps and two different flip angles. To obtain qMT maps, the acquired 12 datasets were fitted to a dictionary predefined by using Bloch equation simulations based on the two-pool MT model. The proposed qMT method was compared to the conventional qMT methods, in terms of qMT parameter maps and processing time. RESULTS: The proposed method yielded qMT maps similar to those of the conventional method, indicating feasibility of modulating MT saturation frequency and power with the interslice gap and flip angle. The whole-brain qMT imaging could be completed in 8 min because of the absence of separate MT pulses. The time for processing qMT parameters was significantly reduced through the dictionary-driven approach; it is 1000 times shorter than that without the dictionary-driven approach and 3 times shorter than that with the spoiled gradient recalled echo-qMT method that uses an analytical solution. CONCLUSION: The proposed dictionary-driven interslice qMT imaging provides qMT maps close to those from the conventional method with significantly reduced scan time and postprocessing time, which can make qMT imaging more clinically acceptable.

Improving Arterial Spin Labeling by Using Deep Learning.

Purpose To develop a deep learning algorithm that generates arterial spin labeling (ASL) perfusion images with higher accuracy and robustness by using a smaller number of subtraction images. Materials and Methods For ASL image generation from pair-wise subtraction, we used a convolutional neural network (CNN) as a deep learning algorithm. The ground truth perfusion images were generated by averaging six or seven pairwise subtraction images acquired with (a) conventional pseudocontinuous arterial spin labeling from seven healthy subjects or (b) Hadamard-encoded pseudocontinuous ASL from 114 patients with various diseases. CNNs were trained to generate perfusion images from a smaller number (two or three) of subtraction images and evaluated by means of cross-validation. CNNs from the patient data sets were also tested on 26 separate stroke data sets. CNNs were compared with the conventional averaging method in terms of mean square error and radiologic score by using a paired t test and/or Wilcoxon signed-rank test. Results Mean square errors were approximately 40% lower than those of the conventional averaging method for the cross-validation with the healthy subjects and patients and the separate test with the patients who had experienced a stroke (P < .001). Region-of-interest analysis in stroke regions showed that cerebral blood flow maps from CNN (mean ± standard deviation, 19.7 mL per 100 g/min ± 9.7) had smaller mean square errors than those determined with the conventional averaging method (43.2 ± 29.8) (P < .001). Radiologic scoring demonstrated that CNNs suppressed noise and motion and/or segmentation artifacts better than the conventional averaging method did (P < .001). Conclusion CNNs provided superior perfusion image quality and more accurate perfusion measurement compared with those of the conventional averaging method for generation of ASL images from pair-wise subtraction images. © RSNA, 2017.

Investigation of control scans in pseudo-continuous arterial spin labeling (pCASL): Strategies for improving sensitivity and reliability of pCASL.

PURPOSE: To investigate the performance of control scans in pseudo-continuous ASL (pCASL) and propose strategies for improving sensitivity and reliability of pCASL. METHODS: The labeling efficiencies of pCASL with conventional control scan and distal control scan were investigated at various radiofrequency (RF) duration/spacing of 0.5/1-2/4 ms, mean slice-selection gradients (GSS ) of 1 and 0 mT/m, and total labeling durations of 1.5-3 s, through Bloch equation simulations and in vivo experiments. In addition, the feasibility of three-dimensional (3D) pCASL with the distal control scan and control scan with no RF preparation was demonstrated in a wide brain area, by suppressing the magnetization transfer (MT) effects with high GSS while maintaining the GSS /mean GSS ratio. RESULTS: The distal control scan provided pCASL signals approximately 40% higher and more robust to variations in the labeling conditions than those from the conventional control scan. The distal and no RF control scans with high GSS provided uniform pCASL signals in approximately 8-cm-thick imaging region with MT contributions <10% of the perfusion signals. CONCLUSIONS: pCASL perfusion signals can be enhanced (∼40%) and become more stable by using the distal or no RF control scan, which can be applied in a wide area by increasing GSS while maintaining GSS /mean GSS . Magn Reson Med 78:917-929, 2017. © 2016 International Society for Magnetic Resonance in Medicine.

Single and double acquisition strategies for compensation of artifacts from eddy current and transient oscillation in balanced steady-state free precession.

PURPOSE: To develop single and double acquisition methods to compensate for artifacts from eddy currents and transient oscillations in balanced steady-state free precession (bSSFP) with centric phase-encoding (PE) order for magnetization-prepared bSSFP imaging. THEORY AND METHODS: A single and four different double acquisition methods were developed and evaluated with Bloch equation simulations, phantom/in vivo experiments, and quantitative analyses. For the single acquisition method, multiple PE groups, each of which was composed of N linearly changing PE lines, were ordered in a pseudocentric manner for optimal contrast and minimal signal fluctuations. Double acquisition methods used complex averaging of two images that had opposite artifact patterns from different acquisition orders or from different numbers of dummy scans. RESULTS: Simulation results showed high sensitivity of eddy-current and transient-oscillation artifacts to off-resonance frequency and PE schemes. The artifacts were reduced with the PE-grouping with N values from 3 to 8, similar to or better than the conventional pairing scheme of N = 2. The proposed double acquisition methods removed the remaining artifacts significantly. The proposed methods conserved detailed structures in magnetization transfer imaging well, compared with the conventional methods. CONCLUSION: The proposed single and double acquisition methods can be useful for artifact-free magnetization-prepared bSSFP imaging with desired contrast and minimized dummy scans. Magn Reson Med 78:254-263, 2017. © 2016 International Society for Magnetic Resonance in Medicine.

MRI artifact correction using sparse + low-rank decomposition of annihilating filter-based hankel matrix.

PURPOSE: Magnetic resonance imaging (MRI) artifacts are originated from various sources including instability of an magnetic resonance (MR) system, patient motion, inhomogeneities of gradient fields, and so on. Such MRI artifacts are usually considered as irreversible, so additional artifact-free scan or navigator scan is necessary. To overcome these limitations, this article proposes a novel compressed sensing-based approach for removal of various MRI artifacts. THEORY: Recently, the annihilating filter based low-rank Hankel matrix approach was proposed. The annihilating filter based low-rank Hankel matrix exploits the duality between the low-rankness of weighted Hankel structured matrix and the sparsity of signal in a transform domain. Because MR artifacts usually appeared as sparse k-space components, the low-rank Hankel matrix from underlying artifact-free k-space data can be exploited to decompose the sparse outliers. METHODS: The sparse + low-rank decomposition framework using Hankel matrix was proposed for removal of MRI artifacts. Alternating direction method of multipliers algorithm was employed for the minimization of associated cost function with the initialized matrices from a factorization-based matrix completion. RESULTS: Experimental results demonstrated that the proposed algorithm can correct MR artifacts including herringbone (crisscross), motion, and zipper artifacts without image distortion. CONCLUSION: The proposed method may be a robust correction solution for various MRI artifacts that can be represented as sparse outliers. Magn Reson Med 78:327-340, 2017. © 2016 International Society for Magnetic Resonance in Medicine.