bluuebunny/crossref_metadata_2025 huggingface dataset:
Neurochemical and cognitive changes precede structural abnormalities in the TgF344-AD rat model of Alzheimer’s disease
Authors: Fowler Caitlin , Goerzen Dana , Devenyi Gabriel , Madularu Dan , Chakravarty M Mallar , Near Jamie
Published: August 2023

Alzheimer’s disease is a progressive neurodegenerative disorder with a decades-long pre-symptomatic phase, substantiating the need for prodromal biomarker development and early intervention. We employed longitudinal magnetic resonance imaging and spectroscopy in conjunction with behavioural testing to characterize the chronological order of appearance and progression of multiple facets of disease pathology in the TgF344-AD rat model. The TgF344-AD rat demonstrated impaired spatial reference memory by 4 months of age, followed by neurochemical abnormalities by 10 months and major structural changes by 16 months, most of which recapitulated documented changes in brain structure and tissue chemistry in human Alzheimer’s disease patients.
Machine learning to detect microstructural brain changes in patients with amnestic mild cognitive impairment based on NODDI
Authors: Fu Xiuwei , Zhang Yu , Li Tongtong , Chen Yuanyuan , Zhang Xianchang , Ni Hongyan
Published: August 2023

This study investigated the changes in brain microstructure in patients with amnestic mild cognitive impairment (aMCI) using neurite orientation dispersion and density imaging (NODDI) combined with machine learning. Neurite density index (NDI) was significantly decreased in white matter, orientation dispersion index (ODI) was significantly decreased in gray matter, and volume fraction of isotropic water molecules (Viso) was significantly increased in the aMCI group. Further correlation and receiver operating characteristic (ROC) curve analyses showed NODDI may reflect the clinical cognitive status of aMCI. NODDI combined with a machine learning algorithm could be a promising alternative for early diagnosis of MCI.
Coupled Metabolic and Functional Changes in Default Mode Network Subsystems of Alzheimer's Disease: A Hybrid 3D 1H-MRSI/FDG-PET/fMRI Study
Authors: Li Wenli , Zhang Miao , Guo Rong , Li Yudu , Zhao Yibo , Hu Jialin , Zhang Yaoyu , Liang Zhi-Pei , Li Yao
Published: August 2023

The default mode network (DMN) is the first large-scale system disrupted in Alzheimer’s disease (AD). However, the functional and metabolic changes in the DMN subsystems of AD patients are not yet fully understood. This study investigated the coupled functional and metabolic changes of the DMN subsystems by combining high-resolution 3D MRSI, resting-state fMRI, and 18F-FDG PET on a hybrid PET/MR scanner. Our results showed that the DMN subsystems have distinct functional and metabolic associations and contributions to cognitive decline in AD patients. Our findings may provide useful insights into the system-level pathophysiology of AD.
Correlation between MRI texture analysis and histo-pathology of Alzheimer’s disease: evaluation in the hippocampus of the Tgf344-AD rat model.
Authors: Muñoz-Moreno Emma , Tudela Raul , López-Gil Xavier , Soria Guadalupe
Published: August 2023

β-amyloid plaques and neuroinflammation, two of the pathological processes associated to Alzehimer's disease (AD) are not visible by conventional MRI. However, its presence may induce image changes that can be evaluated using texture analysis. We evaluated the potential of texture analysis of conventional MRI to characterize these changes. T2-weighted MRI of a transgenic rat model of AD were acquired and texture measures in hippocampus were correlated with inmunohistochemical quantification of plaques and microglia, showing significant correlation. Coherently, significant differences in the texture measures were found between transgenic and control animals, pointing to the potential of texture analysis as AD biomarker.
Longitudinal biochemical and behavioral alterations in a gyrencephalic model of blast related mild traumatic brain injury
Authors: Tang Shiyu , Xu Su , Jiang Li , Wilder Donna , Long Joseph , Medina Alexandre , Li Xin , Fiskum Gary , Sajja Venkata Siva Sai Sujith , Gullapalli Rao
Published: August 2023

Compared to rodents, the ferret model has greater similarities to humans in terms of developmental process, brain structure and sophisticated behavior. In this study, we assessed the longitudinal changes in brain metabolism and impulsivity behavior in a ferret model that closely mimics the blast exposure conditions encountered by Warfighters. Ferrets demonstrated concomitantly increased behavioral impulsivity and metabolite alterations in prefrontal cortex following blast exposure. Our findings agree with clinical observations in patients, suggesting that this model is a good gyrencephalic animal model to study brain biochemical profile changes and neuropsychiatric alterations associated with blast exposure.
Minimally-Invasive Surgical Treatment of Lung Cancer using Magnetic Resonance Guided Focused Ultrasound in a Porcine Model
Authors: Miller Braden , Powlovich Lauren , Moore David , Martin Linda , Mata Jaime
Published: August 2023

This new technique has the potential to increase lung cancer survival rates while decreasing morbidity associated with current practices as it allows a less invasive manner for ablation or tumor debulking.
Hyperpolarized 15N-BBCP as a novel probe of H2O2
Authors: Park Hyejin , Chen Jun , Dimitrov Ivan , Park Jae Mo , Wang Qiu
Published: August 2023

In this work, we have developed a novel de novo 15N-molecular probe, 15N-boronobenzyl-4-cyanopyridinium (15N-BBCP), for in vivo detection of H2O2. The long spin-lattice relaxation time (T1), high H2O2 sensitivity and selectivity, low cytotoxicity, and large chemical shift changes upon reaction suggest that the probe is suited for in vivo hyperpolarized 15N MRI.
Holistic Acceleration from Acquisition to Reconstruction for Submilimeter 3D MR Fingerprinting via Deep Learning
Authors: Liu Yilin , Cheng Feng , Chen Yong , Yap Pew-Thian
Published: August 2023

We developed a novel deep-learning empowered pipeline for both rapid acquisition and reconstruction of high-resolution 3D MRF with 16X acceleration, thereby significantly improving the clinical feasibility of MRF for quantitative imaging.
Spatial properties of noise in prostate DWI acquired with an endo-rectal coil as critical information for clinical QA
Authors: Medved Milica , Chatterjee Aritrick , Devaraj Ajit , Yousuf Ambereen , Oto Aytekin , Karczmar Gregory
Published: August 2023

Development of quantitative QC methods for prostate DWI is important in clinical practice, necessitating reliable estimates of spatially heterogenous noise. In data obtained at 3T with the use of an endo-rectal coil, we use raw noise measurements to simulate pure-noise k-space datasets that are post-processed to yield noise maps. Good agreement is found between noise levels measured from noise maps in the central prostate and the estimates of noise produced from the anterior rectal ROIs in high TE / high b-value DWI. Thus, noise can be estimated from an anterior rectum region when the endo-rectal coil is used.
Cumulant expansion with localization
Authors: Afzali Maryam , Pieciak Tomasz , Jones Derek , Schneider Jürgen , Özarslan Evren
Published: August 2023

Diffusion MR is sensitive to the microstructural features of a sample. Fine-scale characteristics can be probed by employing strong diffusion gradients while the small gradient regime is determined by the cumulants of the distribution of particle displacements. However, the cumulant expansion suffers from a finite convergence radius and cannot represent the ‘localization regime’ that emerges at higher gradient strengths characterized by a stretched exponential dependence. Here, we propose a new representation for the diffusion MR signal. Our method provides not only a robust estimate of the first few cumulants but also a meaningful extrapolation of the signal decay.
Automated Calculation of Optimum z-Shim Gradient Pulses from a Reference Acquisition for Spinal Cord fMRI
Authors: Chu Ying , Finsterbusch Jürgen
Published: August 2023

Slice-specific z-shimming reduces signal losses caused by through-slice dephasing in spinal cord fMRI acquisitions. Typically, the optimum values are determined from a reference acquisition covering a range of z-shim values by a user who identifies the setting with the least signal loss. However, this procedure requires some experience, is time-consuming, and user-dependent. Here, an automated calculation of the optimum values has been developed, implemented on the MR system, and evaluated in phantoms and in vivo. Without compromising the image quality, the approach is faster and user-independent and, thus, may help to facilitate the application of z-shimming in spinal cord fMRI.
An efficient deep learning framework for consistent superficial white matter tractography parcellation
Authors: Xue Tengfei , Zhang Fan , Zhang Chaoyi , Chen Yuqian , Song Yang , Makris Nikos , Rathi Yogesh , Cai Weidong , O’Donnell Lauren
Published: August 2023

We propose a deep-learning-based framework, Superficial White Matter Analysis (SupWMA), which performs an efficient and consistent parcellation of 198 SWM clusters from whole-brain tractography. A point-cloud-based network is developed for our SWM parcellation task, and supervised contrastive learning enables more discriminative representations between plausible streamlines and outliers. SupWMA obtains highly consistent and accurate SWM parcellation results on a large tractography dataset with ground truth labels and on three independently acquired testing datasets from individuals across ages and health conditions.
Neural Network-supported Fast Online-Customized (FOCUS) parallel transmit (pTx) pulses for slice-selective, large flip angle excitation
Authors: Herrler Jürgen , Liebig Patrick , Majewski Kurt , Gumbrecht Rene , Ritter Dieter , Meixner Christian , Maier Andreas , Dörfler Arnd , Nagel Armin
Published: August 2023

Slice-selective, two-spoke parallel transmit (pTx) pulses for exciting large flip angles can be designed quickly and show robust performance for MRI at 7 Tesla using an 8Tx/32Rx coil. Firstly, clusters of B1+/B0 maps are defined and corresponding pulses are optimized prior to the scan. These serve as initialization for a fast, gradient-descent based online-optimization. For each slice, the best cluster-specific initialization is chosen online via neural networks, which predict their respective spatial distribution of the longitudinal magnetization. This approach outperforms other strategies to initialize the pTx RF pulses such as offline-optimized slab-specific and circularly polarized RF pulses as initializations.
Optimization of the Double-Row 16-channel Transmit-only RF Array Coil for Human Brain Imaging at 9.4T.
Authors: Nikulin Anton , Scheffler Klaus , Avdievich Nikolai
Published: August 2023

At 9.4T, transmit-only RF array coils for human brain imaging provide mediocre transmit efficiency due to a weak loading factor, which implies low power dissipation in the tissues. In this work we aim to optimize using the full-wave simulations a dual row 16-channel transmit-only array to improve the transmit performance. For that purpose, we simulated eight different array designs containing the loops with overlapping and gaps between the rows and two configurations of loopoles. We have shown that the optimized design with the overlapped loops provides improvement ~15% in Tx-efficiency and 16.5% improvement in SAR-efficiency compared to the reference coil.
Resting state functional connectivity subnetwork relates to prosocial behavior and compassion in adolescents
Authors: Sipes Benjamin , Jakary Angela , Morrison Melanie , Yang Tony , Tymofiyeva Olga
Published: August 2023

In this study, we found that adolescent resting state functional brain networks contains a subnetwork significantly related to self-reported prosocial behavior and compassion. The subnetwork regions indicated connects many previously identified brain regions associated with prosocial behavioral tasks in adolescents, including the bilateral precuneus, left lateral prefrontal cortex, medial prefrontal cortex, inferior frontal gyri, temporal poles, left amygdala, right posterior cingulate cortex, occipital cortex, left supplementary motor area, and cerebellum.
Functional MRI with three-dimensional reduced field-of-view
Authors: Sun Kaibao , Dan Guangyu , Scotti Alessandro , Karaman Muge , Luo Qingfei , Zhou Xiaohong Joe
Published: August 2023

Functional MRI (fMRI) is typically performed with whole brain coverage, even when only a small brain area is of interest. Although zoomed fMRI has been demonstrated using 2D multi-slice acquisitions, extending this capability to 3D focused volume has not been well explored. We have implemented a 3D reduced field-of-view technique for fMRI by using a 2D RF pulse and applied this technique to a visual fMRI study on ten subjects. Compared to a conventional fMRI sequence with a full field-of-view, our technique produced high isotropic spatial resolution and reduced image distortion, despite a moderate reduction in temporal signal-to-noise ratio.
Differential D1 and D2 receptor internalization and recycling induced by amphetamine in vivo: a PET/fMRI study
Authors: Hansen Hanne , Schain Martin , Deng Helen , Mandeville Joseph , Rosen Bruce , Sander Christin
Published: August 2023

In this study, we imaged the effect of repeated amphetamine injections over >24h using simultaneous PET/fMRI in non-human primates and demonstrate that both internalization and recycling of receptors take place during this timeframe. We provide novel insight into D1 vs. D2 receptor contributions by using a D1 blocking agent in combination with amphetamine injections. These results shed a new light into the timelines of dopamine receptor subtype adaptations and not only provide a link to existing in vitro results but also a foundation for linking receptor-specific mechanisms to initial stages of psychostimulant drug use and abuse.
Multiparametric Characterization of Focal Cortical Dysplasia using MR Fingerprinting
Authors: Wang Zhong Irene , Choi Joon Yul , Hu Siyuan , Tang Yingying , Su TingYu , Blümcke Ingmar , Jones Stephen , Sakaie Ken , Najm Imad , Ma Dan
Published: August 2023

Focal cortical dysplasia (FCD) is a common pathology underlying medically intractable focal epilepsies. Conventional MRI can be limited in characterizing subtle FCD, due to the lack of quantitative measurements for tissue properties. Here, we proposed a multiparametric machine learning (ML) approach based on high-resolution 3D MR fingerprinting (MRF) to characterize FCD lesions. The ML model showed robust accuracy of 96%, 89%, and 79% to separate FCD from normal cortex, FCD type I from type II, and FCD type IIa from type IIb, respectively. Our findings suggest the usefulness of the multiparametric MRF ML approach to improve noninvasive epilepsy presurgical evaluation.
Comparison of high-resolution quantitative peripheral nerve imaging in CMT1A and healthy controls using Double-Echo in Steady-State at 7 Tesla
Authors: Sveinsson Bragi , Barry Robert , Rowe Olivia , Stockmann Jason , Park Daniel , Lally Peter , Rosen Matthew , Sadjadi Reza
Published: August 2023

The ability to estimate neurological disease progression based on objective quantitative measurements could be of great value for assessing treatment efficacy. Here, we demonstrate the feasibility of acquiring high-resolution anatomic and quantitative images at 7T in the sciatic, tibial, and fibular nerves, allowing quantitative assessment of individual fascicles. We also measure the quantities of fascicle cross-sectional area, T2, ADC, and fat fraction for CMT1A patients and healthy controls and investigate any between-group differences.
Age-related differences in white matter tract length assessed via TRACULA
Authors: Robinson Tyler , Chang Paul , Chen Jean
Published: August 2023

We examined differences in median tract length with age across eighteen major white matter tracts in 541 subjects of the Human Connectome Project in Aging. We observed a significant negative age effect on tract length in the forceps minor, left cingulum angular bundle, right superior longitudinal fasciculus parietal, and right uncinate fasciculus. Paradoxically, we also report a significant positive age effect on tract length in the forceps major and right cingulate gyrus. Restricted analyses suggest that these effects are driven by female subjects.
Are vascular risk factors associated with white matter health equally between men and women?
Authors: Patel Arjun , Chad Jordan , Chen J. Jean
Published: August 2023

White matter microstructural degeneration is an early marker of declining brain health, and vascular risk factors such as high blood pressure and adipose fat deposition have been found to be significantly associated with white matter microstructural degeneration in healthy adults. In this study, using diffusion tensor imaging, we find that the vascular-risk influences on white matter are more pronounced in women than in men. This study therefore calls attention to the consideration of sex differences in studying brain vulnerability to vascular risk factors.
Myelin Water Imaging using Dimensionality Reduction
Authors: Song Jae Eun , Vasanawala Shreyas , Kim Dong-Hyun
Published: August 2023

Multi-echo gradient-echo (mGRE)-based myelin water fraction (MWF) mapping is a promising myelin water imaging (MWI) modality but is vulnerable to noise and artifact corruption. The linear dimensionality reduction (LDR) method has recently shown improvements with regard to these challenges. However, the magnitude value based low rank operators have been shown to misestimate the MWF for regions with T2* anisotropy. This paper presents a nonlinear dimensionality reduction (NLDR) method to estimate the MWF map better by encouraging nonlinear low dimensionality of mGRE signal sources.
Whole Liver Phase-Based R2 Mapping in Liver Iron Overload within a Breath-hold
Authors: Navaratna Ruvini , Tamada Daiki , Hernando Diego , Reeder Scott
Published: August 2023

There is an unmet need for non-invasive methods that provide rapid and accurate quantification of liver iron concentration (LIC) over a wide range of iron overload severity. Current R2 mapping techniques suffer from long acquisition times and limited spatial coverage. Recently, a phase-based R2 mapping technique for rapid whole-liver R2 quantification within a single breath-hold has been introduced. However, the feasibility of this method to quantify liver iron overload has not been demonstrated. In this work, we optimize and validate phase-based R2 mapping to quantify R2 in phantom experiments, healthy volunteers, and demonstrate feasibility in patients with iron overload.
Feasibility of VDP Calculation using 19F Free Breathing Spiral Acquisition with Post-Acquisition Denoising
Authors: Chung Sang Hun , Huynh Khoi Minh , Chen Yong , Yap Pew-Thian , Goralski Jennifer , Donaldson Scott , Lee Yueh
Published: August 2023

19F lung ventilation MRI is usually acquired with extended breath holds that limit the acquisition to relatively healthy patients. New denoising methods have the potential to decrease the scan time significantly while achieving comparable SNRs. We compare 19F breath held scans to spiral acquisition based scans performed while free breathing combined with post-acquisition denoising in human subjects at 3T. VDP and SNR were evaluated. The denoised spiral scans yield higher SNRs (29.3 average increase) but overestimated VDPs (8.5% average increase). The differences in VDP were correlated (R-squared 0.74). The approach shows the potential for free-breathing 19F MRI ventilation imaging.
Derivation of liver R2* and B0 maps from dual-echo MR images via deep learning
Authors: Wu Yan , Kee Yongwook , Alley Marc , Pauly John , Xing Lei , Vasanawala Shreyas
Published: August 2023

Quantitative R2* map is an important liver disease indicator. However, the availability of R2* map is limited by the long scan time. In this study, we present a new paradigm to predict R2* and B0 maps from dual echo images. A self-attention deep convolutional neural network is trained and validated, where promising accuracy has been obtained. The proposed quantitative parametric mapping approach has a potential to eliminate the necessity for additional data acquisition other than clinical routine.
3D T2-Weighted Rectal Cancer Imaging using a 3D Fast Spin Echo Sequence with Deep Learning Reconstruction
Authors: Palmquist Sarah , Salem Usama , Stanietzky Nir , Sun Jia , Wang Xinzeng , Bayram Ersin , Hwang Ken-Pin , Son Jong Bum , Wei Peng , Ernst Randy , Kaur Harmeet , Ma Jingfei
Published: August 2023

Multiplanar high resolution T2-weighted (T2W) imaging with a 2D fast spin echo (FSE) sequence is currently an essential component of rectal cancer MRI. In this work, we performed T2W imaging of rectal cancer with a 3D FSE sequence and evaluated the quality and potential clinical value of the images after applying a postprocessing deep learning reconstruction (DLR) algorithm. We found that DLR images are non-inferior to conventional images and there are fair-moderate inter-reader agreements in a large majority of the categories evaluated for image quality and clinical usefulness.
Classifying healthy versus NAFLD mice from liver fatty acids’ 1H-MRS at 9.4 T using SVMs
Authors: Lehmann Pedro Izquierdo , Xavier Aline , Andia Marcelo , Sing-Long Carlos
Published: August 2023

Nonalcoholic fatty liver disease (NAFLD) is a common liver disorder that is the first step in a cascade of liver damage. Hence, there is a clinical need for non-invasive, early identification of patients. We hypothesize that the intra-hepatocyte fatty acid composition provides information about NAFLD. To test this, the 1H-MRS spectra of fatty acids in the livers of healthy and NAFLD mice were measured at 9.4 T. Exploratory data analysis shows the spectra can be correctly classified with SVMs. Our results open an opportunity to develop a non-invasive tool for staging NAFLD patients without the need of liver biopsy.
Reproducibility of Alkaline Inorganic Phosphate Quantification using 31P-Magnetic Resonance Spectroscopy at 3T
Authors: Matias Alexs , Serviente Corinna , Decker Stephen , Erol Muhammet , Giuriato Gaia , Le Fur Yann , Nagarajan Rajakumar , Layec Gwenael
Published: August 2023

Alkaline inorganic phosphate (Pialk) is a promising biomarker of mitochondrial content in vivo that is technically challenging to assess at 3T. The absolute and relative reproducibility of Pialk in the quadriceps muscle across two visits were assessed in healthy sedentary-to-moderately active young adults using 31P-MRS at 3T. The results of this study demonstrate an excellent absolute and relative reproducibility of the quantification of Pialk in the quadriceps muscle at 3T provided that the coil is positioned at the mid-thigh.
Validation of Muscle Fiber Architecture of the Human Tongue Revealed by Diffusion MRI with Histology Verification
Authors: Liang Xiao , Elsaid Nahla , Jiang Li , Roys Steve , Stone Maureen , Prince Jerry , Puche Adam , Gullapalli Rao , Zhuo Jiachen
Published: August 2023

Advanced dMRI techniques have been used to resolve the complex tongue muscle architecture. However, dMRI-derived tongue muscle architecture has not been validated with histology. In this study, we validated the dMRI-derived tongue muscle architecture with histology of a tongue specimen. dMRI was acquired for a post-mortem head and a healthy volunteer. dMRI-derived muscle fiber orientations, visualized as the tractogram, were compared against microscopic histology slices of the tongue specimen. Muscle fibers in the tractograms show good correspondence with those appearing in the histology images. The study demonstrates that dMRI can accurately reveal the complex muscle architecture of the human tongue.
Transient Biochemical Recovery of Skeletal Muscles Post-exercise with Simultaneous Dynamic T1rho and T2 Mapping
Authors: Rong Esther , Matakas Jason , Shamah Steven , Le Jenna , Sperling Karen , Wu Can , Peng Qi
Published: August 2023

T1ρ and T2 contrasts provide complementary information on tissue properties. Therefore, their transient recovery after exercise to pre-exercise equilibria is potentially a functional biomarker for muscle disorder diagnosis and treatment evaluation. In this pilot study, we implemented and tested a high-temporal-resolution dynamic MRI sequence for simultaneous T1ρ and T2 mapping immediately after exercise. T1ρ and T2 contrasts presented unique transient recovery time courses in different skeletal muscles. Dynamic information derived from our imaging approach could potentially lead to greater sensitivity and specificity in evaluating muscle conditions compared with traditional methods with either T1ρ or T2 contrast alone.
Improving cortical bone imaging using a novel Flexible Ultra Short Echo time (FUSE) sequence for optimal synthetic CT generation
Authors: Cui Lumeng , McWalter Emily , Moran Gerald , Venugopal Niranjan
Published: August 2023

Ultrashort echo time (UTE) pulse sequences are used in synthetic computed tomography (sCT) generation for MR-based radiation treatment planning (RTP). This study used a novel Flexible UTE sequence (FUSE) for improving cortical bone imaging. Using a human skull, we applied the optimized UTE acquisition techniques and parameters and verified the improvements in overall image quality of cortical bone in comparison to a product UTE sequence. Also, we have shown the feasibility and potential of our FUSE sequence for generating the sCT data that is clinically equivalent to the traditional CT for RTP.
Cerebellar damage, by medulloblastoma and related surgery, induces global cerebral microstructural
Authors: Holtrop Joseph , Glass John , McAfee Stuart , Zhang Silu , Scoggins Matthew , Gajjar Amar , Robinson Giles , Reddick Wilbur , Bag Asim
Published: August 2023

Survivors of medulloblastoma patients frequently have cognitive impairment, which is currently thought to be due to radiation and chemotherapy. In this work, we have showed that there is widespread changes in diffusion tensor parameters in cerebral white and gray matters, suggestive of brain microstructure damage, even before start of radiation and chemotherapy and not related to hydrocephalus.
Investigating spatial heterogeneity in diffuse gliomas with ultra-high gradient diffusion MRI and a model-free diffusion tensor distribution
Authors: Ly Ina , Song Yiqiao , Fan Qiuyun , Nummenmaa Aapo , Alvarez Maria Martinez-Lage , Curry William , Nahed Brian , Cahill Daniel , Jones Pamela , Dietrich Jorg , Forst Deborah , Plotkin Scott , Batchelor Tracy , Rosen Bruce , Huang Susie , Gerstner Elizabeth
Published: August 2023

Diffuse gliomas demonstrate significant spatial heterogeneity which is not captured on standard anatomical MRI or with existing diffusion MRI (dMRI) methods. This is because current dMRI approaches provide voxel-averaged information about tissue composition and are based on a priori assumptions about underlying tissue microstructure, thus providing a simplified model of tissue properties. Here, we apply an unbiased, model-agnostic dMRI method (model-free diffusion tensor distribution (FDTD) and K-means clustering) to seven subjects with diffuse gliomas. We confirm the presence of intratumoral heterogeneity and find distinct differences in diffusion properties between gliomas of different histologic grades and pre- and post-treatment.
Identifying Metabolic Differentiators of Small Cell Neuroendocrine versus Adenocarcinoma subtypes of Castration-Resistant Prostate Cancer
Authors: Upadhyay Deepti , Sun Jinny , Agarwal Shubhangi , Bok Robert , Santos Romelyn , Peehl Donna , Kurhanewicz John , Sriram Renuka
Published: August 2023

NMR-based stable isotope resolved metabolomics was used to characterize small cell neuroendocrine (SCNC) and adenocarcinoma (aCRPC) subtypes of castration-resistant prostate cancer patient-derived xenografts (PDXs). Targeted metabolomics indicated distinct upregulation of metabolic pathways in SCNC relative to aCRPC PDXs. Specifically, [U-13C]glucose and [U-13C]glutamine labeling demonstrated that SCNC PDXs had increased glycolytic rate, alanine aminotransferase and tricarboxylic acid cycle activity, including anaplerotic sources. Further, the metabolic differences observed among the aCRPC and SCNC PDXs exhibited a continuous range as predicted by clinical genomic data of these subtypes of CRPC.
Characterizing Restricted Diffusion in Pre-/Post- treatment Gliomas Using Time-dependent Diffusion MRI at Ultra-high-gradient Human 3.0T
Authors: Zhu Ante , Shih Robert , DeMarco James , Morris Herman , Madhavan Radhika , Kohls Gail , Sprenger Tim , Hood Maureen , Marinelli Luca , Gregory Jason , Theeler Brett , Ho Vincent , Foo Thomas
Published: August 2023

Time-dependent diffusion MRI has shown high specificity to non-Gaussian restricted diffusion, which improves assessing cytoarchitectural alternations in pre-/post- treatment glioma. However, the characterization of time-dependent diffusivity ADC(f) and kurtosis MK(f) at short diffusion time using oscillating gradient spin echo (OGSE) diffusion encoding is limited. In our preliminary studies at ultra-high-gradient 3.0T, larger changes of ADC(f) and MK(f) were shown in glioblastoma compared to a low-grade astrocytoma, indicating highly restricted microenvironment as confirmed on histopathology. OGSE-based time-dependent diffusion showed promise for evaluating cytoarchitectural alternations in gliomas. ADC(f) and MK(f) of contrast-enhancing lesions in three post-glioblastoma-treatment patients showed strong time-dependence.
Magnetic Resonance Elastography of Pancreas: Comparison between different drivers and driving frequencies.
Authors: Nanjappa Manjunathan , Kolipaka Arunark
Published: August 2023

Magnetic Resonance Elastography is an emerging alternative diagnostic tool for palpation and it is known that stiffness is altered in chronic pancreatitis and as well as in pancreatic tumors. In this study we compared performances of a drum-shaped rigid driver with a flat soft-pad passive driver in 10 volunteers at 40Hz and 60Hz diver frequencies. The variations of mean stiffness values measured between the drivers were found to be insignificant at a given frequency. Additionally, stiffness measured at 40Hz is lower compared to 60Hz. Therefore, users can make their choice of the driver based on patient comfort, longevity and cost.
Weekly Apparent Diffusion Coefficient Kinetics in HPV-positive Oropharyngeal Cancer Patients Undergoing Definitive Radiation Therapy
Authors: McDonald Brigid , He Renjie , Ding Yao , Salzillo Travis , Wang Jihong , Fuller Clifton , Mohamed Abdallah
Published: August 2023

In this study, we investigate weekly changes in mean and median apparent diffusion coefficient (ADC) values from diffusion-weighted MRI for HPV+ oropharyngeal cancer patients undergoing definitive radiation therapy (RT). Trends of increasing ADC values in GTV_P and GTV_N were observed with clear separation at each time point between patients with no evidence of disease vs. recurrence at 2 months post-RT. Because of our small sample size (15), a larger cohort is needed for more robust outcome prediction analysis.
Comparison between machine learning and radiologists’ readings for prediction of chemoradiation therapy response in rectal cancer using MRI
Authors: Zhang Yang , Shi Liming , Zhou Weiwen , Sun Xiaonan , Jabbour Salma , Yue Ning , Su Min-Ying , Nie Ke
Published: August 2023

A radiomics model with pre-treatment MRI for pCR prediction and compared with two oncologists’ readings. Their performance with and without the model assistances was cross-compared to see the potential role of radiomics model in assisting clinical decision. A total of 203 patients receiving neoadjuvant CRT followed by total mesorectal excision (TME) were enrolled. For training set, AUC from radiomics model is 0.85 and AUC from clinical model is 0.74. For testing set, AUC from radiomics model is 0.82 and AUC from clinical model is 0.69. For oncologist’s reading, with the assistance of radiomics model, positive prediction value (PPV) and specificity increased significantly for both experienced and inexperienced oncologist.
Pre-clinical Fast Field-Cycling NMR for the detection and classification of breast cancer
Authors: Hanna Katie , Husain Ehab , Masannat Yazan , Abu-Eid Rasha , Lurie David , Speirs Valerie , Broche Lionel
Published: August 2023

This study aims to explore novel biomarkers of breast cancer using FFC-NMR. NMR dispersion profiles were generated by analysing patient-matched tumour, peritumoral zone and distant normal fixed tissue samples from twenty breast cancer patients. These profiles were analysed using piecewise power models and the numerical parameters derived from these models could significantly classify the different regions of breast tissue and distinguish patients from different prognostic categories. The numerical parameters investigated in this study may have potential as quantitative biomarkers for breast cancer detection and risk stratification.
Assessment of anti-mitotic treatment response in breast cancer using temporal diffusion spectroscopy
Authors: jiang xiaoyu , Devan Sean P , Xie Jingping , Gore John C. , xu junzhong
Published: August 2023

Reliable and sensitive methods for assessing the response of breast cancer to treatment are critical for rapid selection of the most appropriate therapy for individual patients, and development of novel therapies. Paclitaxel has been reported to slow/block mitosis at the metaphase-anaphase transition and induce apoptosis, leading to cell shrinkage and membrane breakdown. Using a biophysical model that allows derivation of microstructural parameters (e.g., cell size d) and an indicator of the intracellular water lifetime (τin ) from the tdiff dependence of diffusion MRI signals, we monitored the treatment response of breast cancer in both cell cultures and solid tumors in vivo.
Optimization of a highly sensitivity 19F MRI probe for NK cell immunotherapy tracking
Authors: Martinez Gary , Nevozhay Dmitry , Bournat Juan , Cheng Donghang , Sharma Ajay , King Tyler , Que Emily , Gopalakrishnan Vidya , Sokolov Konstantin , Bankson James
Published: August 2023

NK cell immunotherapy has great potential as a safe and effective anticancer therapy yet there are significant challenges for immunotherapy in brain tumors. We have developed a 19F probe with enhanced sensitivity for the purposes of 19F labeling of NK cells to monitor NK cell biodistribution and immunotherapy. In this paper, we report on the relaxometric characteristics of the probe, acquisition optimization, and initial ex vivo imaging tests in murine models. The agent displayed improved sensitivity relative to Celsense and was clearly visible in the cerebral cortex in approximately equivalent amounts that will be used for cell tracking experiments.
Bayesian Quantitative T1 Mapping with Variable-Density and Poisson-Disk sampling
Authors: Huang Shuai , Lah James , Allen Jason , Qiu Deqiang
Published: August 2023

We propose a Bayesian approach with built-in parameter estimation to perform T1 mapping from undersampled measurements. Apart from using measurements acquired at multiple flip angles, the Bayesian approach offers a convenient way to synthesize measurements from multiple echoes as well to obtain better image quality. The sparse prior on the image wavelet coefficients could further improve the performance when we perform undersampling in the k-space to reduce scan time. The proposed Bayesian approach automatically and adaptively estimates the induced regularization and other parameters by undersampling, making it a better choice over approaches that require manual regularization parameter tuning.
A Subspace EPTI Reconstruction with Magnitude-only Bases and Synergistic Phase Bias Updating for Distortion-Free Diffusion-Relaxometry MRI
Authors: Dai Erpeng , Dong Zijing , Setsompop Kawin , McNab Jennifer
Published: August 2023

The distortion-free diffusion and relaxometry images provided by echo-planar time resolved imaging (EPTI) ) represent a valuable acquisition strategy for mapping brain tissue microstructure. However, given the large under-sampling factor of EPTI acquisition and the intrinsically low SNR of diffusion MRI, an SNR-efficient reconstruction is vital. Subspace reconstruction can improve SNR efficiency by reducing the number of unknowns. In subspace reconstruction, the selection of bases strongly affects the reconstruction image fidelity, SNR, and computational efficiency. Here, we explore a new subspace-based EPTI reconstruction with magnitude-only bases and synergistic phase bias updating and demonstrate its performance for microstructural mapping.
Diffusion Tensor Imaging of the Brain on a Prototype 0.55T System using SNR-Enhancing Joint Reconstruction
Authors: Kung Hao-Ting , Cui Sophia , Kaplan Jonas , Joshi Anand , Leahy Richard , Nayak Krishna , Acharya Jay , Haldar Justin
Published: August 2023

There has been substantial recent interest in MRI systems with lower $$$B_0$$$ field strengths, which can improve the value and accessibility of MRI. This work investigates the performance of diffusion tensor imaging on a prototype whole-body 0.55T system equipped with high-performance shielded gradients. Although the images suffer from noise contamination when using conventional image reconstruction techniques, we demonstrate that the use of an SNR-enhancing joint reconstruction technique can substantially reduce noise concerns, enabling high quality diffusion tensor imaging results. In addition, compared to diffusion data acquired on a conventional 3T scanner, the 0.55T images demonstrate substantially reduced susceptibility-induced geometric distortions.
Assessment of T1, T2, and T1ρ Values of Knee Cartilage and Menisci in Healthy Subjects using 3T MRI: A Preliminary Study
Authors: Zeitoun Ralph , Noda Chikara , Ambale-Venkatesh Bharath , Kassai Yoshimori , Lima João , Liu Chia-Ying
Published: August 2023

Efforts have been focused on early detection of osteoarthritis using MRI. Our study reports T1, T1ρ and T2 values for knee cartilage and menisci in normal subjects using 3T MRI, and evaluates associations with age, gender, and BMI. We found that WB-FC had higher T1 and lower T2 values than LWB-FC, and WB-FC had higher T1 values in females. T1 values of WB-FC and LM were associated with age, and T1ρ values of WB-FC were associated with BMI. Our study presents novel data on T1 mapping, reinforcing the utility of MRI as a potential tool for early detection of osteoarthritis.
DL-MOTIF: Deep Learning Based Motion Transformation Integrated Forward-Fourier Reconstruction for Free-Breathing Liver DCE-MRI
Authors: Chen Sihao , Gan Weijie , Eldeniz Cihat , Kamilov Ulugbek , Fraum Tyler , An Hongyu
Published: August 2023

Dynamic contrast-enhanced MRI (DCE-MRI) of the liver offers structural and functional information for assessing the contrast uptake visually. However, respiratory motion and the requirement of high temporal resolution make it difficult to generate high-quality DCE-MRI. In this study, we proposed a novel deep learning based motion transformation integrated forward-Fourier (DL-MOTIF) reconstruction using motion fields derived from a deep learning Phase2Phase (P2P) network and deep learning priors from a residual network on severely undersampled DCE. This approach reconstructs sharp motion-free DCE images with artifacts removal by incorporating deep learning motion fields for motion integration and deep learning priors for regularization.
Self-supervised and physics informed deep learning model for prediction of multiple tissue parameters from MR Fingerprinting data
Authors: Adams Richard , Chen Yong , Yap Pew-Thian , Ma Dan
Published: August 2023

Magnetic resonance fingerprinting (MRF) simultaneously quantifies multiple tissue properties. Deep learning accelerates MRF’s tissue mapping time; however, previous deep learning MRF models are supervised, requiring ground truths of tissue property maps. It is challenging to acquire quality reference maps, especially as the number of tissue parameters increases. We propose a self-supervised model informed by the physical model of the MRF acquisition without requiring ground truth references. Additionally, we construct a forward model that directly estimates the gradients of the Bloch equations. This approach is flexible for modeling MRF sequences with pseudo-randomized sequence designs where an analytical model is not available.
Understanding and Reducing structural bias in deep learning-based MR reconstruction
Authors: Pal Arghya , Rathi Yogesh
Published: August 2023

Deep learning methods are increasingly being used for accelerating magnetic resonance imaging (MRI) acquisition and reconstruction strategies. However, it is important to understand whether or not deep learning models have an inherent structural bias that may effectively create concerns in real-world settings. In this abstract, we show a strategy to understand and then reduce structural prior bias in deep models. The proposed approach decouples the spurious structural bias (prior) of a deep learning model by intervening in the input. Our proposed debiasing strategy is fairly robust and can work with any pre-trained deep learning MR reconstruction model.
Polynomial Preconditioning for Accelerated Convergence of Proximal Algorithms including FISTA
Authors: Iyer Siddharth , Ong Frank , Setsompop Kawin
Published: August 2023

This work aims to accelerate the convergence of proximal gradient algorithms (like FISTA) by designing a preconditioner using polynomials that targets the eigenvalue spectrum of the forward linear model to enable faster convergence. The preconditioner does not assume any explicit structure and can be applied to various imaging applications. The efficacy of the preconditioner is validated on four varied imaging applications, where it seen to achieve 2-4x faster convergence.
Locally low-rank denoising of multi-echo fMRI data preserves detection of resting-state networks following scan truncation
Authors: Meyer Nolan , Kang Daehun , Ahmed Zaki , In Myung-Ho , Shu Yunhong , Huston John , Bernstein Matt , Trzasko Joshua
Published: August 2023

Functional magnetic resonance imaging (fMRI) has inherent limitations of fast acquisitions due to low signal-to-noise ratio (SNR) and artifacts. Multi-echo fMRI acquires images at multiple TEs, increasing robustness to off-resonance based signal loss and improving sensitivity to neural activity. However, noise is substantial given limitations of EPI, although consistent across TEs contributing to prolonged scan times required for sufficient statistical power. Reduction of acquisition time with reconstruction and processing techniques is of interest. This study extends preliminary work on locally low-rank denoising of multi-echo fMRI data to explore scan time reduction through processing with retrospective truncation.
MAP-MRI diffusion estimates are biased by simultaneous multi-slice acceleration
Authors: Muftuler L. Tugan , Nencka Andrew , Arpinar Volkan , Shin Jaemin , Yang Baolian , McKinnon Graeme , Banerjee Suchandrima , Koch Kevin
Published: August 2023

Advanced diffusion MRI models are being explored to study the complex microstructure of the brain with higher accuracy. However, these techniques require long acquisition times. Simultaneous multi-slice (SMS) accelerates data acquisition by exciting multiple image slices simultaneously and separating the overlapping slices using a mathematical model. However, SMS acceleration introduces increased noise in reconstructed images and crosstalk between simultaneously excited slices. These compounded effects from SMS acceleration could affect tissue microstructure parameters derived from advanced diffusion MRI models.
Characterizing Corticobulbar and Corticospinal Tracts into the Cervical Spinal Cord with Sub-Millimeter Isotropic DTI
Authors: Iain Bruce , Ma Yixin , Song Allen
Published: August 2023

An ability to characterize the corticobulbar and corticospinal tracts from the motor cortex into the spinal cord with diffusion tensor imaging remains a challenge due to limiting factors such as achievable spatial resolutions, spatial coverages, and signal-to-noise ratio in the spinal cord. By extending the field-of-view through a combination of sub-millimeter isotropic axial and sagittal acquisitions, this study presents a technique to delineate the complete pyramidal tracts with data acquired at a sufficient spatial resolution to resolve intricate structural details such as the pyramidal decussation. Such a delineation can facilitate placement of spinal cord stimulation electrodes for movement disorder treatments.
Simultaneous triglyceride characterization and water T1 estimation in a breath-hold applied to brown adipose tissue using MR fingerprinting
Authors: Ostenson Jason , Damon Bruce , Brittain Evan
Published: August 2023

Multi-parametric fat-water imaging of brown adipose tissue (BAT) is an important tool to study BAT’s composition and metabolism. We propose a single breath-hold MR fingerprinting method to simultaneously estimate the triglyceride number of double bonds (NDB) and number of methylene-interrupted double bonds (NMIDB), as well as water T1. We provide results of simulation, phantom, and example human BAT studies. The proposed method’s NDB and NMIDB estimates strongly correlate with MRS estimates (ρ = 0.998), and the T1 estimates are similar to those from MRS. The NDB, NMIDB, and T1 estimates in/around BAT are similar to those from the literature.
MRI contrast synthesis from low-rank coefficient images
Authors: Zhang Xiaoxia , Flassbeck Sebastian , Assländer Jakob
Published: August 2023

Synthetic contrasts are commonly derived from parameter maps via Bloch simulation.Typically, model imperfections, in particular partial volume effects, cause artifacts in those images. Recently, it has been proposed to overcome this problem by mapping directly from MR-Fingerprinting data to synthetic contrasts with neural networks. Those methods, however, face the MRF-typical undersampling artifacts, as well as the computational burden of hundreds of input images. We propose to first reconstruct images in a low-rank sub-space, which maintains the correct partial volume contrast, but allows for removal of undersampling artifacts, and to map from this space to synthetic contrasts with a neural network.
Joint recovery of time aligned multi-slice dynamic speech MR images from under-sampled data using a deep generative manifold model
Authors: Rusho Rushdi Zahid , Zou Qing , Jacob Mathews , Lingala Sajan
Published: August 2023

Dynamic speech MRI is a powerful tool to characterize complex speech articulations. Current accelerated 2D dynamic speech MRI schemes can achieve high time resolutions of the order of (10-20 ms) while sequentially acquiring multiple 2D slices. However, the complex articulatory motion is difficult to interpret jointly across the slices due to mis-alignment of motion patterns. Here, we apply a novel generative manifold model which reconstructs and generates a time aligned multi-slice 2D speech dataset at 18 ms/frame from under-sampled k-space v/s time data sequentially acquired from multiple 2D slices. We evaluate this scheme on two speakers producing repeated speech tasks.
Flexible FOV image reconstruction using multi-echo 13C imaging with rotating spiral arms
Authors: Lin Sung-Han , Ma Junjie , Khalyavin Leon , Park JaeMo
Published: August 2023

Metabolic imaging with hyperpolarized 13C substrates often detects signals only from small subregions within the prescribed FOV, primarily due to lacking background signals and limited perfusion or metabolic activities in the adjacent tissues. In this study, we propose an imaging method that provides flexible reconstruction options with variable FOV. The proposed pulse sequence was implemented, and the feasibility of the reconstruction method was validated with a 13C phantom.