CSF pulsations measured in Parkinson's disease patients using EPI-based fMRI data.

Introduction: Cerebrospinal fluid (CSF) flow is involved in brain waste clearance and may be impaired in neurodegenerative diseases such as Parkinson's disease. This study aims to investigate the relationship between the CSF pulsation and the development of dementia in Parkinson's disease (PD) patients using EPI-based fMRI. Methods: We measured CSF pulsation in the 4th ventricle of 17 healthy controls and 35 PD patients using a novel CSF pulsation index termed "CSFpulse" based on echo-planar imaging (EPI)-based fMRI. The PD patients were classified into a PD with dementia high-risk group (PDD-H, n = 19) and a low risk group (PDD-L, n = 16), depending on their development of dementia within 5 years after initial brain imaging. The size of the 4th ventricle was measured using intensity-based thresholding. Results: We found that CSF pulsation was significantly higher in PD patients than in healthy controls, and that PD patients with high risk of dementia (PDD-H) had the highest CSF pulsation. We also observed an enlargement of the 4th ventricle in PD patients compared to healthy controls. Conclusion: Our results suggest that CSF pulsation may be a potential biomarker for PD progression and cognitive decline, and that EPI-based fMRI can be a useful tool for studying CSF flow and brain function in PD.

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.

Longitudinal Magnetic Resonance Imaging with ROS-Responsive Bilirubin Nanoparticles Enables Monitoring of Nonalcoholic Steatohepatitis Progression to Cirrhosis.

Despite the vital importance of monitoring the progression of nonalcoholic fatty liver disease (NAFLD) and its progressive form, nonalcoholic steatohepatitis (NASH), an efficient imaging modality that is readily available at hospitals is currently lacking. Here, a new magnetic-resonance-imaging (MRI)-based imaging modality is presented that allows for efficient and longitudinal monitoring of NAFLD and NASH progression. The imaging modality uses manganese-ion (Mn2+)-chelated bilirubin nanoparticles (Mn@BRNPs) as a reactive-oxygen-species (ROS)-responsive MRI imaging probe. Longitudinal T1-weighted MR imaging of NASH model mice is performed after injecting Mn@BRNPs intravenously. The MR signal enhancement in the liver relative to muscle gradually increases up to 8 weeks of NASH progression, but decreases significantly as NASH progresses to the cirrhosis-like stage at weeks 10 and 12. A new dual input pseudo-three-compartment model is developed to provide information on NASH stage with a single MRI scan. It is also demonstrated that the ROS-responsive Mn@BRNPs can be used to monitor the efficacy of potential anti-NASH drugs with conventional MRI. The findings suggest that the ROS-responsive Mn@BRNPs have the potential to serve as an efficient MRI contrast for monitoring NASH progression and its transition to the cirrhosis-like stage.

CycleSeg: Simultaneous synthetic CT generation and unsupervised segmentation for MR-only radiotherapy treatment planning of prostate cancer.

BACKGROUND: MR-only radiotherapy treatment planning is an attractive alternative to conventional workflow, reducing scan time and ionizing radiation. It is crucial to derive the electron density map or synthetic CT (sCT) from MR data to perform dose calculations to enable MR-only treatment planning. Automatic segmentation of relevant organs in MR images can accelerate the process by preventing the time-consuming manual contouring step. However, the segmentation label is available only for CT data in many cases. PURPOSE: We propose CycleSeg, a unified framework that generates sCT and corresponding segmentation from MR images without access to MR segmentation labels METHODS: CycleSeg utilizes the CycleGAN formulation to perform unpaired synthesis of sCT and image alignment. To enable MR (sCT) segmentation, CycleSeg incorporates unsupervised domain adaptation by using a pseudo-labeling approach with feature alignment in semantic segmentation space. In contrast to previous approaches that perform segmentation on MR data, CycleSeg could perform segmentation on both MR and sCT. Experiments were performed with data from prostate cancer patients, with 78/7/10 subjects in the training/validation/test sets, respectively. RESULTS: CycleSeg showed the best sCT generation results, with the lowest mean absolute error of 102.2 and the lowest Fréchet inception distance of 13.0. CycleSeg also performed best on MR segmentation, with the highest average dice score of 81.0 and 81.1 for MR and sCT segmentation, respectively. Ablation experiments confirmed the contribution of the proposed components of CycleSeg. CONCLUSION: CycleSeg effectively synthesized CT and performed segmentation on MR images of prostate cancer patients. Thus, CycleSeg has the potential to expedite MR-only radiotherapy treatment planning, reducing the prescribed scans and manual segmentation effort, and increasing throughput.

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.

Deep learning-based k-space-to-image reconstruction and super resolution for diffusion-weighted imaging in whole-spine MRI.

PURPOSE: To assess the feasibility of deep learning (DL)-based k-space-to-image reconstruction and super resolution for whole-spine diffusion-weighted imaging (DWI). METHOD: This retrospective study included 97 consecutive patients with hematologic and/or oncologic diseases who underwent DL-processed whole-spine MRI from July 2022 to March 2023. For each patient, conventional (CONV) axial single-shot echo-planar DWI (b = 50, 800 s/mm2) was performed, followed by DL reconstruction and super resolution processing. The presence of malignant lesions and qualitative (overall image quality and diagnostic confidence) and quantitative (nonuniformity [NU], lesion contrast, signal-to-noise ratio [SNR], contrast-to-noise ratio [CNR], and ADC values) parameters were assessed for DL and CONV DWI. RESULTS: Ultimately, 67 patients (mean age, 63.0 years; 35 females) were analyzed. The proportions of vertebrae with malignant lesions for both protocols were not significantly different (P: [0.55-0.99]). The overall image quality and diagnostic confidence scores were higher for DL DWI (all P ≤ 0.002) than CONV DWI. The NU, lesion contrast, SNR, and CNR of each vertebral segment (P ≤ 0.04) but not the NU of the sacral segment (P = 0.51) showed significant differences between protocols. For DL DWI, the NU was lower, and lesion contrast, SNR, and CNR were higher than those of CONV DWI (median values of all segments; 19.8 vs. 22.2, 5.4 vs. 4.3, 7.3 vs. 5.5, and 0.8 vs. 0.7). Mean ADC values of the lesions did not significantly differ between the protocols (P: [0.16-0.89]). CONCLUSIONS: DL reconstruction can improve the image quality of whole-spine diffusion imaging.

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.

Distortion correction using topup algorithm by single k-space (TASK) for echo planar imaging.

Distortion of echo planar imaging (EPI) can be corrected using B0 field maps, which can be estimated with the topup algorithm that requires two EPI images with opposite distortions. In this study, we propose a new algorithm, termed topup algorithm by single K-space (TASK), to generate two input images from a single k-space for the topup algorithm to correct EPI distortions. The centric EPI contains the opposite phase-encoding polarities in one k-space, which can be divided into two halves with opposite distortions. Therefore, two inputs could be extracted by dividing the k-space into halves and processing them using the proposed procedure including an iterative procedure of automatic brain masking and uniformity correction. The efficiency of TASK was evaluated using 3D EPI. Quantitative evaluations showed that TASK corrected EPI distortion at a similar level to the traditional methods. The estimated field maps from the conventional topup and TASK showed a high correlation ([Formula: see text]). An ablation study showed the validity of every suggested step. Furthermore, it was confirmed that TASK was effective for distortion correction of two-shot centric EPI as well, demonstrating its wider applicability. In conclusion, TASK can correct EPI distortions by its own single k-space information with no additional scan.

Non-invasive flow mapping of parasagittal meningeal lymphatics using 2D interslice flow saturation MRI.

The clearance pathways of brain waste products in humans are still under debate in part due to the lack of noninvasive imaging techniques for meningeal lymphatic vessels (mLVs). In this study, we propose a new noninvasive mLVs imaging technique based on an inter-slice blood perfusion MRI called alternate ascending/descending directional navigation (ALADDIN). ALADDIN with inversion recovery (IR) at single inversion time of 2300 ms (single-TI IR-ALADDIN) clearly demonstrated parasagittal mLVs around the human superior sagittal sinus (SSS) with better detectability and specificity than the previously suggested noninvasive imaging techniques. While in many studies it has been difficult to detect mLVs and confirm their signal source noninvasively, the detection of mLVs in this study was confirmed by their posterior to anterior flow direction and their velocities and morphological features, which were consistent with those from the literature. In addition, IR-ALADDIN was compared with contrast-enhanced black blood imaging to confirm the detection of mLVs and its similarity. For the quantification of flow velocity of mLVs, IR-ALADDIN was performed at three inversion times of 2000, 2300, and 2600 ms (three-TI IR-ALADDIN) for both a flow phantom and humans. For this preliminary result, the flow velocity of the dorsal mLVs in humans ranged between 2.2 and 2.7 mm/s. Overall, (i) the single-TI IR-ALADDIN can be used as a novel non-invasive method to visualize mLVs in the whole brain with scan time of ~ 17 min and (ii) the multi-TI IR-ALADDIN can be used as a way to quantify the flow velocity of mLVs with a scan time of ~ 10 min (or shorter) in a limited coverage. Accordingly, the suggested approach can be applied to noninvasively studying meningeal lymphatic flows in general and also understanding the clearance pathways of waste production through mLVs in humans, which warrants further investigation.

Attention fusion network with self-supervised learning for staging of osteonecrosis of the femoral head (ONFH) using multiple MR protocols.

BACKGROUND: Osteonecrosis of the femoral head (ONFH) is characterized as bone cell death in the hip joint, involving a severe pain in the groin. The staging of ONFH is commonly based on Magnetic resonance imaging and computed tomography (CT), which are important for establishing effective treatment plans. There have been some attempts to automate ONFH staging using deep learning, but few of them used only MR images. PURPOSE: To propose a deep learning model for MR-only ONFH staging, which can reduce additional cost and radiation exposure from the acquisition of CT images. METHODS: We integrated information from the MR images of five different imaging protocols by a newly proposed attention fusion method, which was composed of intra-modality attention and inter-modality attention. In addition, a self-supervised learning was used to learn deep representations from a large amount of paired MR-CT dataset. The encoder part of the MR-CT translation network was used as a pretraining network for the staging, which aimed to overcome the lack of annotated data for staging. Ablation studies were performed to investigate the contributions of each proposed method. The area under the receiver operating characteristic curve (AUROC) was used to evaluate the performance of the networks. RESULTS: Our model improved the performance of the four-way classification of the association research circulation osseous (ARCO) stage using MR images of the multiple protocols by 6.8%p in AUROC over a plain VGG network. Each proposed method increased the performance by 4.7%p (self-supervised learning) and 2.6%p (attention fusion) in AUROC, which was demonstrated by the ablation experiments. CONCLUSIONS: We have shown the feasibility of the MR-only ONFH staging by using self-supervised learning and attention fusion. A large amount of paired MR-CT data in hospitals can be used to further improve the performance of the staging, and the proposed method has potential to be used in the diagnosis of various diseases that require staging from multiple MR protocols.

An agonistic anti-Tie2 antibody suppresses the normal-to-tumor vascular transition in the glioblastoma invasion zone.

Tumor progression is intimately associated with the vasculature, as tumor proliferation induces angiogenesis and tumor cells metastasize to distant organs via blood vessels. However, whether tumor invasion is associated with blood vessels remains unknown. As glioblastoma (GBM) is featured by aggressive invasion and vascular abnormalities, we characterized the onset of vascular remodeling in the diffuse tumor infiltrating zone by establishing new spontaneous GBM models with robust invasion capacity. Normal brain vessels underwent a gradual transition to severely impaired tumor vessels at the GBM periphery over several days. Increasing vasodilation from the tumor periphery to the tumor core was also found in human GBM. The levels of vascular endothelial growth factor (VEGF) and VEGF receptor 2 (VEGFR2) showed a spatial correlation with the extent of vascular abnormalities spanning the tumor-invading zone. Blockade of VEGFR2 suppressed vascular remodeling at the tumor periphery, confirming the role of VEGF-VEGFR2 signaling in the invasion-associated vascular transition. As angiopoietin-2 (ANGPT2) was expressed in only a portion of the central tumor vessels, we developed a ligand-independent tunica interna endothelial cell kinase 2 (Tie2)-activating antibody that can result in Tie2 phosphorylation in vivo. This agonistic anti-Tie2 antibody effectively normalized the vasculature in both the tumor periphery and tumor center, similar to the effects of VEGFR2 blockade. Mechanistically, this antibody-based Tie2 activation induced VE-PTP-mediated VEGFR2 dephosphorylation in vivo. Thus, our study reveals that the normal-to-tumor vascular transition is spatiotemporally associated with GBM invasion and may be controlled by Tie2 activation via a novel mechanism of action.

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.

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.

Simultaneously optimizing sampling pattern for joint acceleration of multi-contrast MRI using model-based deep learning.

BACKGROUND: Acceleration of MR imaging (MRI) is a popular research area, and usage of deep learning for acceleration has become highly widespread in the MR community. Joint acceleration of multiple-acquisition MRI was proven to be effective over a single-acquisition approach. Also, optimization in the sampling pattern demonstrated its advantage over conventional undersampling pattern. However, optimizing the sampling patterns for joint acceleration of multiple-acquisition MRI has not been investigated well. PURPOSE: To develop a model-based deep learning scheme to optimize sampling patterns for a joint acceleration of multi-contrast MRI. METHODS: The proposed scheme combines sampling pattern optimization and multi-contrast MRI reconstruction. It was extended from the physics-guided method of the joint model-based deep learning (J-MoDL) scheme to optimize the separate sampling pattern for each of multiple contrasts simultaneously for their joint reconstruction. Tests were performed with three contrasts of T2-weighted, FLAIR, and T1-weighted images. The proposed multi-contrast method was compared to (i) single-contrast method with sampling optimization (baseline J-MoDL), (ii) multi-contrast method without sampling optimization, and (iii) multi-contrast method with single common sampling optimization for all contrasts. The optimized sampling patterns were analyzed for sampling location overlap across contrasts. The scheme was also tested in a data-driven scenario, where the inversion between input and label was learned from the under-sampled data directly and tested on knee datasets for generalization test. RESULTS: The proposed scheme demonstrated a quantitative and qualitative advantage over the single-contrast scheme with sampling pattern optimization and the multi-contrast scheme without sampling pattern optimization. Optimizing the separate sampling pattern for each of the multi-contrasts was superior to optimizing only one common sampling pattern for all contrasts. The proposed scheme showed less overlap in sampling locations than the single-contrast scheme. The main hypothesis was also held in the data-driven situation as well. The brain-trained model worked well on the knee images, demonstrating its generalizability. CONCLUSION: Our study introduced an effective scheme that combines the sampling optimization and the multi-contrast acceleration. The seamless combination resulted in superior performance over the other existing methods.

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.

Unsupervised resolution-agnostic quantitative susceptibility mapping using adaptive instance normalization.

Quantitative susceptibility mapping (QSM) is a useful magnetic resonance imaging (MRI) technique that provides the spatial distribution of magnetic susceptibility values of tissues. QSMs can be obtained by deconvolving the dipole kernel from phase images, but the spectral nulls in the dipole kernel make the inversion ill-posed. In recent years, deep learning approaches have shown a comparable QSM reconstruction performance to the classic approaches, in addition to the fast reconstruction time. Most of the existing deep learning methods are, however, based on supervised learning, so matched pairs of input phase images and ground-truth maps are needed. Moreover, it was reported that the deep learning-based methods fail to reconstruct QSM when the resolution of test data is different from the trained resolution. To address this, here we propose an unsupervised resolution-agnostic QSM deep learning method. The proposed method does not require QSM labels for training and reconstructs QSM with various resolutions by using adaptive instance normalization. Experimental results and clinical validation confirm that the proposed method provides accurate QSM with various resolutions compared to other deep learning approaches, and shows competitive performance to the best classical approaches in addition to the ultra-fast reconstruction.

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.

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.

Feasibility of Synthetic Computed Tomography Images Generated from Magnetic Resonance Imaging Scans Using Various Deep Learning Methods in the Planning of Radiation Therapy for Prostate Cancer.

We aimed to evaluate and compare the qualities of synthetic computed tomography (sCT) generated by various deep-learning methods in volumetric modulated arc therapy (VMAT) planning for prostate cancer. Simulation computed tomography (CT) and T2-weighted simulation magnetic resonance image from 113 patients were used in the sCT generation by three deep-learning approaches: generative adversarial network (GAN), cycle-consistent GAN (CycGAN), and reference-guided CycGAN (RgGAN), a new model which performed further adjustment of sCTs generated by CycGAN with available paired images. VMAT plans on the original simulation CT images were recalculated on the sCTs and the dosimetric differences were evaluated. For soft tissue, a significant difference in the mean Hounsfield unites (HUs) was observed between the original CT images and only sCTs from GAN (p = 0.03). The mean relative dose differences for planning target volumes or organs at risk were within 2% among the sCTs from the three deep-learning approaches. The differences in dosimetric parameters for D98% and D95% from original CT were lowest in sCT from RgGAN. In conclusion, HU conservation for soft tissue was poorest for GAN. There was the trend that sCT generated from the RgGAN showed best performance in dosimetric conservation D98% and D95% than sCTs from other methodologies.