bluuebunny/crossref_metadata_2025 huggingface dataset:
A universal B1 shim for the human cerebellum
Authors: Brouwer Emma , van der Zwaag Wietske , Priovoulos Nikos
Published: August 2024

The human cerebellum is underexplored in-vivo due to a lack of acquisition methods that effectively portray it. Its dense architecture necessitates high resolution, which benefits from 7T imaging. Unfortunately, at higher field strengths, images suffer from severely destructive B1-interference with typical coil geometries. Subject-optimized parallel-transmit approaches can mitigate B1 artifacts but increase scan-time. We designed a universal B1-shim specific to the cerebellum and immediately applicable across individuals for high-resolution 7T fMRI. We demonstrate improvements in tSNR and functional responses in cerebellar ROIs during a hand-motor task compared to a quadrature mode shim setting.
The ALFF alterations in maintenance hemodialysis patients with sleep disorder:a rs-fMRI study combined with machine learning analysis
Authors: Feng Menghan , Zhang Yue , Wen Zeying , Wu Yuchi , Fu Chengwei , Deng Kan , Lin Qizhan , Liu Bo
Published: August 2024

The prevalence of SD is high in chronic kidney disorder patients who suffering maintenance hemodialysis whereas little is known about the neuropathologic mechanism. We investigated the ALFF alterations between MHDSD patients and HCs and those meaningful features were used for constructing discriminating model based the SVM algorithm to classify the MHDSD patients. We found the aberrant spontaneous activities in the DMN, AN, CEN, and VIN in MHDSD patients. Additionally, the classifier indicated the discriminative ALFF features in the above regions demonstrated good performance. This will contribute to well understanding the neuropathological mechanism and seeking biomarkers for discrimination in MHDSD patients.
MR-derived breast density (MagDensity) enables reliable density measures across field strengths and vendors
Authors: Ying Jia , Cattell Renee , Huang Chuan
Published: August 2024

The current standard of care for breast density assessment using mammography according to the Breast Imaging Reporting and Data System (BI-RADS) is subjective and can also suffer from intra- and inter-reader variability. Quantitative measurement of breast density is crucial for personalized risk assessment and longitudinal monitoring. Our team has previously developed a quantitative, MR-derived BD (MagDensity) measurement based on fat-water decomposition, which has shown high accuracy and test-retest reproducibility within a single scanner. In order for this measure to be practical for clinical adoption, this study aims to determine the reliability of MagDensity across scanners, field strengths, and vendors.
Feasibility study for developing a reproducible AI-driven breast segmentation and composition algorithm from axial T2-weighted sequences
Authors: Garg Saurabh , Basar Saqib , Akbari Nasrin , Nguyen Thanh-Duc , London Sean , Chodakiewitz Yosef , Attariwala Rajpaul , Hashemi Sam
Published: August 2024

BI-RADS breast tissue composition defines which imaging modality is best suited for tissue examination. However it is subjective and varies between readers whereas AI techniques have been shown to remove subjectivity. We evaluate the use of state-of-the-art AI algorithms on a general whole-body noncontrast MRI to quantify the amount of fat versus nonfat tissue and compare with radiologists reports. Our results show significant correlation between the AI and radiologists' decisions. Further, we show on large dataset that the rate of replacement of nonfat fibroglandular tissue with fatty tissue is almost triple the rate in premenopausal women than postmenopausal women.
Estimation of Fatty Acid Composition in Mammary Adipose Tissue Using Unsupervised Approach of Deep Learning
Authors: Chaudhary Suneeta , Lane Elizabeth , Chang Eileen , McGrath Anika , Mema Eralda , Levy Allison , Reichman Melissa , Dodelzon Katerina , Nickel Marcel , Moy Linda , Drotman Michele , Kim Sungheon
Published: August 2024

The purpose of this study is to develop a non-invasive imaging method to measure the fatty acid composition (FAC) of mammary adipose tissue (MAT) and to investigate its role in breast cancer. A novel unsupervised deep learning approach has been developed using the MRI signal equation of fat peaks in the loss function to generate the FAC maps without using any training data. It takes less computational efforts than conventional voxel-wise analysis techniques. The repeatability and reproducibility of the proposed method have been examined on six subjects, which showed no statistically significant difference between repeated analyses and scans.
Multi-2D ASL-MRA and super-resolution convolutional neural network for improved intracranial peripheral arteries visualization
Authors: Suzuki Yuriko , Koktzoglou Ioannis , Jezzard Peter , Okell Thomas
Published: August 2024

The non-invasive nature of Arterial Spin Labeling (ASL) technique makes ASL-based intracranial dynamic MR angiography (MRA) a potential alternative to diagnostic X-ray digital subtraction angiography. In elderly and diseased patients with slower blood flow, however, the vessel visualization of distal peripheral arteries tends to be poor, as the repeatedly applied RF excitation pulses decrease ASL blood signal rapidly. In this study, we address such a limitation by using multiple 2D slice acquisition to reduce the saturation of arterial blood signal. Additionally, to avoid losing vessel conspicuity and sharpness with 2D slice acquisition, we apply a super-resolution convolutional neural network method.
Weakly supervised learning improves vascular territorial mapping of random vessel-encoded ASL
Authors: He Yining , Yan Lirong
Published: August 2024

This study proposed a weakly supervised learning algorithm for vascular territorial mapping with rVE-ASL. The territory maps generated by the proposed deep learning (DL) method was compared with the territories from the conventional rVE-ASL method by visual inspection and F1 score. Our initial results showed that the DL method outperformed the conventional rVE-ASL method in the vascular territory mapping with improved detection of VA territory. The DL method also provided reliable vascular territorial maps with reduced numbers of encodings, significantly reducing rVE-ASL scan time. These findings suggest that DL could be an effective approach for vascular territorial mapping of rVE-ASL.
Optimizing background suppression for dual-module velocity-selective ASL and characterizing the temporal noise
Authors: Guo Jia
Published: August 2024

Background suppression (BS) was optimized for dual-module velocity-selective arterials spin labeling (dm-VSASL) using VS inversion (dm-VSI). Compared with the Siemens product pulsed ASL (PASL) and pseudo-continuous ASL (PCASL) labeling, dm-VSI with optimized BS produced significantly higher temporal SNR; and a better suppression of the temporal noise from background tissues was observed with dm-VSI labeling by analyzing the temporal noise level with respect to the BS level. Further investigation is needed to verify and understand these findings to take full advantage of the improved SNR performance of dm-VSASL.
Theoretical and experimental evaluation of the test and retest reliability of single- and multi-delay 3D pCASL
Authors: Shao Xingfeng , Liu Zixuan , Shou Qinyang , Jann Kay , Wang Danny JJ
Published: August 2024

The goal of this study is to evaluate the quantification accuracy and measurement reliability of single- and multi-PLD pCASL via both simulation and in-vivo test and retest data. CBF calculated by single-delay ASL achieved highest test and retest reliability as compared to 5-PLD ASL. However, CBF values may be under-estimated due to prolonged ATT. 5-PLD ASL can be used to quantify ATT and CBF simultaneously with slightly reduced test and retest reliability. Model-fitting approach is more preferrable than the weighted-delay approach in terms of both quantification error and reliability.
A deep learning approach for robust and accurate deconvolution of DSC MRI perfusion calculation
Authors: Asaduddin Muhammad , Kim Eung Yeop , Park Sung-Hong
Published: August 2024

The conventional deconvolution method in DSC perfusion MRI suffers from sensitivity to noise and threshold level. Regularization methods to mitigate the noise issue also suffers from other issues. In this study, we present a deep learning approach to perform deconvolution more robustly and accurately. Our result showed multi layers perceptron (MLP) performed deconvolution more accurately in synthetic data compared to the traditional regularization method. We also showed that MLP performed more robustly in patient data with varying levels of noise. This study provides a strong argument for using MLP as a stable and accurate deconvolution method for DSC perfusion calculation.
Fast dual-echo ultrashort echo time (UTE) MRI to estimate long T2 fraction in tendons and detect age-and osteoporosis-related differences
Authors: Jerban Saeed , Afsahi Amir Masoud , Dwek Sophia , Athertya Jiyo , Malhi Bhavsimran , Moazamian Dina , Ma Yajun , Sedaghat Sam , Jang Hyungseok , Woods Gina , Chung Christine , Du Jiang , Chang Eric
Published: August 2024

Tendon and bone comprise a special interacting unit. Bone loss in osteoporosis (OPo) may associate with a reduction in tendon quality that needs to be investigated. We investigated the tendon quality differences between OPo patients, osteopenia (OPe) patients, and healthy volunteers with normal bone (Normal) using the Frac-LongT2 index, a rapid measure performed by dual-echo ultrashort echo time (UTE) MRI. The estimated Frac-LongT2 in the anterior and posterior tibialis tendon (ATT and PTT) were significantly higher in the OPo group compared with the Normal group. This study highlights the potential of this rapid UTE-based technique for in vivo tendon assessment.
Quantification of soft tissue of the buttocks in a seated position
Authors: Slade Jill , Mogale Nkhensani , Schanz Daniel , Ilyasov Viktor , Scott Justin , van Schoor Albert , Bush Tamara
Published: August 2024

MRI is well suited to quantify soft tissue but challenges remain in using MRI to address soft tissue in a seated position and for studying how tissue responds to loading. IDEAL and T1 weighted images were used to quantify apparent fat fraction and soft tissue thicknesses of the buttocks in a side lying position with the tissue unloaded and loaded. Tissue compression during loading predominantly reflected changes in the muscle and tendon. Intramuscular fat was not related to the change in muscle/tendon. These methods may be useful to study soft tissue in populations at risk for pressure ulcer development.
Diffusion Tensor Imaging of tendon and ligament: Influence of crimping behavior and microstructural variations.
Authors: Naughton Noel , Moghaddam Amir , Kersh Mariana , Koyejo Samni , Majumdar Shreyan , Johnson Amy Wagoner , Damon Bruce
Published: August 2024

To guide development and optimization of dMRI protocols, here we present a numerical simulation framework for analyzing how changes in collagen microstructure influence the measured diffusion tensor. We experimentally characterize a porcine digital flexor tendon using second harmonic generation (SHG) microscopy to identify characteristic geometric parameters and use these parameter ranges to perform a sensitivity analysis of what microstructural features most influence diffusion tensor imaging (DTI) metrics. We conclude by exploring how geometric changes in collagen crimping due to tissue strain influence DTI metrics.
Repeatability of Cones UTE-T2* Mapping of Cartilage
Authors: Williams Ashley , Mahtani Gordhan , He Jade , Wadsworth Adam , Chu Constance
Published: August 2024

This work examines the combined acquisition and segmentation repeatability of Cones UTE-T2* measures in tread mark cartilage regions and in small 2-D cartilage and meniscus ROIs in the knees of uninjured participants. Both intra- and inter-day repeatability assessments showed excellent average CVs of less than 10% in all tread mark ROIs and in all but 2 small 2-D ROIs segmented from only a single slice. ICC estimates were good to excellent (0.79–0.98) in all tread mark ROIs and ranged from moderate to excellent (0.54-0.98) in small 2-D ROIs corresponding to absolute precision errors of less than 1ms in most cases.
Rapid high-resolution musculoskeletal MR imaging with a super resolution deep learning constrained Compressed SENSE reconstruction
Authors: Sun Zheng , Huang Shan , Zhao Xiance , Chen Weibo , Chan Queenie , Zhang Yajing , Peeters Johannes , Li Yuehua
Published: August 2024

The integration of compressed-SENSE and artificial intelligence allows to acquire high-resolution images within relatively short scan times The purpose of this study is to compare the image quality of the musculoskeletal images reconstructed with Compressed -SENSE (CS), CS with integrated artificial intelligence (CS-AI) and CS-AI combined with superresolution(CS-AI-HR). We found that the image quality was significantly improved by using CS-AI-HR compared to CS. This study provides supportive information for the application of CS-AI-HR in routine clinical practice.
Individual-specific TBSS of diffusion and relaxometry measures reveal differences of global white matter in autistic individuals
Authors: Guerrero-Gonzalez Jose , Surgent Olivia , Adluru Nagesh , Kirk Gregory , Kecskemeti Steven , Dean Douglas , Travers Brittany , Alexander Andrew
Published: August 2024

For neurological conditions that effect brain regions differently across individuals, quantitative neuroimaging-based metrics of individual deviation may be more informative in characterizing neuroanatomical variation including normal from abnormal variations than commonly used group-wise analyses. This work uses a joint analysis of iTBSS (individual-specific Tract-Based Spatial Statistics) of multiple neuroimaging measures from diffusion and relaxometry to better characterize differences between autistic and non-autistic individuals.
Unsupervised Super-Resolution of Magnetic Resonance Images Using Deep Image Prior
Authors: Chen# Geng , Yang# Hao , Wissal Lemroussi , Xia* Yong , Yap* Pew-Thian
Published: August 2024

The anatomical resolution of MRI is typically limited by acquisition time constraints. While deep learning networks have shown great potential for post-acquisition MRI resolution enhancement, their training typically relies on low-high resolution image pairs, which are not always available in practice. Here, we propose using deep image prior (DIP) for unsupervised MRI resolution enhancement with network training relying only on low-resolution images. Experimental results indicate that our method super-resolve MR images effectively with realistic details.
Reproducibility of diffusion tensor image analysis along the perivascular space (DTI-ALPS) on variable diffusion times
Authors: Jung Won Beom , Ryu Jaekyun , Otgonbaatar Chuluunbaatar , Song Jeonghak , Kim Juho , Shim Hackjoon
Published: August 2024

Diffusion tensor imaging (DTI) allows noninvasively to investigate the function of glymphatic system with ALPS index by utilizing the behavior of water molecules along the perivascular space. The effects of diffusion time with fixed TE for the evaluation of glymphatic system using diffusion tensor imaging have not been investigated. Here, we evaluated the effects of DTI with different diffusion time to ALPS index in the glymphatic system.
Radiation Dependent Demyelination in Normal Appearing White Matter in Glioma Patients, Determined Using Quantitative MRI
Authors: Ljusberg Anna , Blystad Ida , Lundberg Peter , Adolfsson Emelie , Tisell Anders
Published: August 2024

Adding quantitative MRI to existing examinations for patients with malignant glioma is valuable for observing changes in tissue specific parameters such as R1, R2, proton density and myelin concentration. Changes observed in this study, with a decrease of myelin concentration and increase of proton density, in normal appearing white matter can be interpreted as a demyelination caused by radiation therapy. For low doses (<30 Gy), but not for high (>30 Gy) the myelin concentration returns to baseline.
Non-invasive MRI of Blood-Cerebrospinal-Fluid-Barrier Function: A Novel Functional Biomarker of Alzheimer's Disease Pathology
Authors: Perera Charith , Cruz Renata , Shemesh Noam , Thomas David , Wells Jack , Ianus Andrada
Published: August 2024

Alzheimer’s disease (AD) is characterised by many pathophysiological changes, such as the accumulation of amyloid-β. The clearing of detrimental agents, including amyloid-β proteins, from brain tissue is linked to the function of choroid plexus (CP) or blood-cerebrospinal fluid barrier (BCSFB). This study investigates for the first time BCSFB function using non-invasive ASL-based methods in a mouse model of AD. Significantly higher values of total BCSFB-mediated water delivery in AD mice relative to controls were observed as early as 8 weeks of age, and a possible (though currently non-significant) correlation with behavioural tests was identified.
Functional-structural decoupling in visual network is associated with cognitive decline in patients with type 2 diabetes mellitus.
Authors: Ni Minhua , Li Zeyang , Yu Ying , Li Sining , Yan Linfeng , Cui Guangbin
Published: August 2024

In this study, functional and structural changes in visual network (VN) of type 2 diabetes mellitus (T2DM) were investigated using multimodal magnetic resonance imaging. We explored degree centrality (DC), amplitude of low frequency fluctuation (ALFF), fractional anisotropy (FA), DC-FA and ALFF-FA in VN. Compared with healthy controls, deteriorated DC, ALFF and DC-FA coefficients in VN were observed in T2DM. These indicators showed positive correlations with cognitive function in T2DM, especially memory and executive function. Functional-structural decoupling may be a potential image biomarker of cognitive function change in T2DM.
Microstructural neurodegeneration of the entorhinal-hippocampus pathway in preclinical Alzheimer’s disease: ex vivo 11.7T MRI and histology
Authors: Uchida Yuto , Onda Kengo , Chotiyanonta Jill , Hou Zhipeng , Troncoso Juan , Mori Susumu , Oishi Kenichi
Published: August 2024

Conventional neuroimaging biomarkers for the neurodegeneration of Alzheimer’s disease (AD) are not sensitive enough to detect neurodegenerative alterations in preclinical AD individuals. We aimed to examine microstructural neurodegeneration of the entorhinal-hippocampus pathway during the pathological process of AD. Our ex vivo comparative study of 11.7T MRI and histology successfully visualized microstructural neurodegeneration of the entorhinal-hippocampus pathway in preclinical AD brain tissues. In a future study, we aim to translate the findings from ex vivo 11.7T MRI and histology into in vivo MRI clinical research for preclinical AD individuals.
Longitudinal changes in hippocampal texture from healthy aging to Alzheimer’s disease
Authors: Wearn Alfie , Raket Lars , Spreng Nathan
Published: August 2024

Microstructural brain changes caused by early Alzheimer’s disease neuropathology may cause subtle changes in MR signal that are quantifiable using texture analysis, a branch of radiomics. We used cross-sectional and longitudinal analysis techniques in the ADNI dataset to examine changes in texture across the disease continuum. We found that biomarker positive but cognitively healthy older adults had measurably different hippocampal texture than those without biomarker risk. Longitudinal modelling revealed progressive textural change with disease severity, but with high inter-subject variability. Nonetheless, hippocampal texture provided additional information to volume in predicting cognitive decline in older adults without a diagnosis of dementia.
Relationship between white matter hyperintensity load and cognitive decline in patients with carotid artery stenosis
Authors: Zhang Wen , Zhao Xiance , Zhang Bing
Published: August 2024

Patients with carotid artery stenosis (CAS) have a high prevalence of cognitive impairment. White matter hyperintensity (WMH) is one of the most common imaging signs of chronic brain injury in the elderly. We investigated the relationship between WMH load and cognitive decline in CAS patients. Our results suggested that Fazekas score and WMH volume quantification can be a potential simple biomarker for predicting cognitive function in CAS patients.
Functional connectivity changes of olfactory neural circuits in Subjective Cognitive Decline under task fMRI
Authors: Zhu Yajing , Zhang Xin , Zhang Bing
Published: August 2024

Analysis of functional connectivity changes of olfactory neural circuits in subjective cognitive decline(SCD)under specific odor stimuli using generalized physiological and psychological interactions.
7-Tesla in vivo 1H-MRS-measured prefrontal glutathione increases during oral fumarate therapy in relapsing-remitting multiple sclerosis
Authors: Swanberg Kelley , Prinsen Hetty , Pelletier Daniel , Juchem Christoph
Published: August 2024

Multiple sclerosis (MS) is an autoimmune disease that damages the central nervous system. Oxidative stress, thought to play a role in MS-related pathophysiology, can be modulated in the cell by endogenous antioxidants such as glutathione (GSH), hypothesized to participate in the therapeutic effect of MS disease-modifying therapy dimethyl fumarate. We used proton magnetic resonance spectroscopy (1H MRS) to measure in vivo cortical glutathione concentrations in individuals with relapsing-remitting multiple sclerosis (RR-MS) before and during 12 months of dimethyl fumarate therapy and observed a significant positive effect of time on prefrontal cortex glutathione. No such change was shown in healthy controls.
Association of subcortical structural shapes with fatigue in Multiple Sclerosis.
Authors: Li Yujing , Yang Tingli , Li Min , Wang Rui , Ren Xinying , Xiong Diaohan , Ai Kai , Zhang Jing
Published: August 2024

This study investigated the shape changes in the subcortical structure of patients with multiple sclerosis (PwMS) and how they are associated with MS-related fatigue. Surface-based vertex analysis was performed on 25 fatigued RRMS (F-MS), 25 non-fatigued RRMS (NF-MS), and 40 healthy controls (HCs). Compared to HCs, PwMS showed regional shape contractions in the areas of bilateral thalamus. The morphological changes in the bilateral thalamus occur only on the side closest to the ventricles. Compared to the NF-MS subgroup, F-MS patients showed regional surface atrophy in the areas of left caudate nucleus, which was negatively correlated with the severity of fatigue.
Personalized-NODDI (pNODDI) discriminates between healthy and multiple sclerosis subjects
Authors: Grosso Elena , Tur Carmen , Calvi Alberto , Collorone Sara , Grussu Francesco , Battiston Marco , Carrasco Ferran Prados , Kanber Baris , Samson Rebecca , D'Angelo Egidio , Wheeler-Kingshott Claudia Gandini , Palesi Fulvia
Published: August 2024

Neurite Orientation Dispersion and Density Imaging (NODDI) is a multi-compartmental model for microstructure characterization utilising diffusion-weighted MRI. Here, a recently proposed personalized-NODDI (pNODDI) modelling technique was used to assess microstructural alterations in a cohort of Multiple Sclerosis (MS) patients. Our findings showed that pNODDI metrics were sensitive to MS pathology and could discriminate between healthy controls and MS. Furthermore, pNODDI metrics of key brain regions increased the discriminative power between the two groups. Overall, pNODDI provides a personalized and clinically feasible model for microstructure, increasing sensitivity to pathological alterations.
A pilot in vivo study of the sciatic nerve in multiple sclerosis using quantitative magnetic resonance imaging
Authors: Boonsuth Ratthaporn , Battiston Marco , Samson Rebecca , Calvi Alberto , Wheeler-Kingshott Claudia Gandini , Yiannakas Marios
Published: August 2024

The peripheral nervous system is not routinely examined objectively in multiple sclerosis (MS), despite evidence from neuropathology that demonstrates its implication. In this pilot in vivo study, the sciatic nerve was examined using multi-shell diffusion-weighted imaging, quantitative magnetisation transfer and T1 relaxometry to investigate whether pathological neural tissue damage could be detected in people with relapsing-remitting MS as compared to healthy controls.
Therapy effects on the structural connectome of children treated for medulloblastoma
Authors: Glass John , Liang Jia , Song Ruitian , Li Yimei , Robinson Giles , Gajjar Amar , Merchant Thomas , Reddick Wilburn
Published: August 2024

This study reports on the effects of pediatric medulloblastoma therapy in a population of 68 subjects. Structural connectivity was evaluated after surgery and again at the end of both radiation and chemotherapy. Linear models were written for the difference between these two time points controlling for age and risk arm. Fourteen significant edges were found connecting the basal ganglia to the dorsolateral prefrontal and prefrontal cortices as well as the cingulate. These regions are part of the executive function network and may point to the mechanisms of decreases in neurocognitive performance.
Increased regional homogeneity and correlation with clinical indicators and scales in type 2 diabetes
Authors: Du Wei , Liu Yangyingqiu , Wang Man , Li Wanyao , Gao Bingbing , Wang Weiwei , Song Qingwei , Miao Yanwei
Published: August 2024

To further investigate the changes in brain function of type 2 diabetes we prospectively enrolled 38 patients clinically diagnosed with T2DM and 24 age-matched healthy controls and underwent blood oxygen level-dependent fMRI scans. The image data was processed to obtain regional homogeneity values and compared by two sample t test. Results found that the local synchronization of spontaneous neural activity of the supramarginal gyrus in patients with T2DM was abnormal and correlated with cognitive function, which may be involved in the pathophysiological mechanism of T2DM.
Evaluation of altered brain activity in type 2 diabetes by multiple indices of brain function: a resting-state functional MRI study
Authors: Zhang Ge , Zhang Xianchang , Wang Meiyun
Published: August 2024

We combined multiple rs-fMRI indices to investigate the abnormal functional changes among T2DM patients. 52 T2DM patients and matched control participants were enrolled in this study. The functional abnormalities primarily involved motor and sensory function areas. More brain regions were revealed by the combination strategy than any single method and the indices of rs-fMRI showed significantly concordant alterations in most abnormal brain regions. Correlations of between clinical parameters and rs-fMRI indices were found. The current study demonstrated an enriched picture showing the brain functional alterations reflected by the combination of multiple indices in T2DM patients.
RF Safety Measurements of External Defibrillator Electrodes: E-Field and Temperature Mapping
Authors: Reiss Simon , Özen Ali , Lottner Thomas , Bock Michael
Published: August 2024

Currently, no external defibrillation system is available that is labeled MR safe. To create an MR safe device heating induced by the electrodes that are attached to the patient needs to be prevented. In this study, we propose a setup for high resolution RF E-field mapping of commercially available defibrillation electrodes and show that this setup can be used to precisely detect hot spots. At these hot spots, we then perform temperature measurements within a 1.5T to assess the MR safety of the electrodes.
Consideration of peripheral nerve stimulation in the optimization of spiral k-space trajectories
Authors: Schad Oliver , Wech Tobias , Köstler Herbert
Published: August 2024

The algorithm we present in this work designs variable density spiral (VDS) trajectories and their respective gradient time courses based on the Stanford VDS-tool. In addition to hardware limits (maximum gradient strength and slew rate) a PNS threshold based on the SAFE-Model is introduced.
MRI Gradient Induced Vibration of Leadless Pacemakers
Authors: Childers Michael , Sethna Dorab , Sison Shiloh
Published: August 2024

Simulations were performed to assess potential gradient induced vibration of leadless pacemakers. Results of the evaluation showed leadless pacemakers sized objects, made from commonly used conductive materials, generated very small vibrations (all less than 0.3mm in displacement) when exposed to conservative gradient induced vibration safety test conditions per ISO/TS 10974. These calculated displacements are negligible in comparison to the interacting displacements between a typical leadless pacemaker and cardiac tissue through each cardiac cycle. As such, the risk of leadless pacemaker gradient induced vibration tissue damage is minimal and assessments for this hazard per ISO/TS 10974 may not be necessary.
Influence of arm rotations on peripheral nerve stimulation thresholds for the interventionalist in MR guided procedures
Authors: Jia Feng , Littin Sebastian , Amrein Philipp , Russe Maximilian , Zaitsev Maxim
Published: August 2024

Simulation of peripheral nerve stimulation of MRI gradient coils for rotated arms of an interventional radiologist. The arm rotation has a direct influence on the site of stimulation in the arm.
Reproducibility of radiomics features between MRI-derived synthetic-CT and true CT in prostate MR-guided radiotherapy
Authors: Zhou Yihang , Yuan Jing , Xue Cindy , Yang Bin , Cheung Kin Yin , Yu Siu Ki
Published: August 2024

MR-guided radiotherapy and radiomics have gained considerable attention. Synthetic-CT (sCT) derived from MRI has been adopted to facilitate MRI-only radiotherapy planning. Researches have evaluated the sCT both qualitatively and quantitatively. However, few studies have looked into the potential that sCT offers at radiomics level. We hypothesize that sCT generated from MR can faithfully reproduce radiomics features compared to those extracted from true planning-CT. We aim to investigate the reproducibility of radiomics features derived from a commercially available sCT generation (MRCAT) acquired on a 1.5T MR-simulator to those obtained from a CT simulation scan in a cohort of prostate cancer patients.
Prediction of overall survival for astrocytoma with whole-tumor radiomics analysis based on diffusion kurtosis imaging
Authors: Tan Yan , Tian Dawei , Zheng Wenqiao , Wang Xiaochun , Zhang Hui
Published: August 2024

For investigating the usefulness of radiomics signature based on diffusion kurtosis imaging in overall survival prediction for astrocytoma, radiomics features extracted from whole-tumor on MK and MD images were selected to construct radiomics signature. Then the radiomics signature was integrated with clinical-genetic risk factors to develop a combined model, which was represented as nomogram. The results showed that the radiomics signature have offered clinically relevant prognostic information for astrocytoma and further stratified patients into different risk groups. The combined model achieved the highest predictive performance and facilitated clinical decision-making by nomogram, demonstrating the incremental value of radiomics signature.
Rado – A Cloud-Based Toolbox for Radiomics Analysis
Authors: Montin Eros , Lattanzi Riccardo
Published: August 2024

Rado is a web-based application designed to fully automatically perform a radiomic analysis. Rado allows users to extract features from medical images, apply feature selections, and mine the dataset using the most common classifiers and regressors. It also offers the option to augment the dataset by means of rigid transformations. The current version of Rado allows users to interact with the dataset by means of restful APIs or using a standardized web GUI. The application will be distributed via the Cloud MR portal, which allows running the feature extraction on sparse servers as well as on local computers.
Radiomics-based Machine Learning for Predicting Clinically Significant Cancer in Multicenter Cohort: Comparison to PI-RADS Reading
Authors: Nketiah Gabriel , Sunoqrot Mohammed , Sandsmark Elise , Langørgen Sverre , Selnæs Kirsten , Bertilsson Helena , Elschot Mattijs , Bathen Tone
Published: August 2024

Synopsis: Recently, predictive machine learning models have shown promise for prostate cancer diagnosis. The utility of MRI radiomic features for prostate cancer detection and classification has been shown several studies, but mostly using relatively small and single centre cohort. In this study, we showed that radiomics-based machine learning can perform relatively well compared to clinical practice, especially in large multicentre settings. On the patient-level analysis, the areas under the receiver-operating curves for PI-RADS reading by a radiologist and machine learning model were 90% and 89%, respectively.
Preoperative MRI-based radiomic-clinical nomogram to predict residual tumor for advanced high-grade serous ovarian carcinoma
Authors: Lu Jingjing , Cai Songqi , Wang Fang , Wu Pu-Yeh , Pan Xianpan , Qiang Jinwei , Li Haiming , Zeng Mengsu
Published: August 2024

Residual tumor (RT) status is associated with the prognosis and survival rate of patients with high-grade serous ovarian carcinoma (HGSOC). However, current RT status prediction approach through laparoscopy has disadvantages of invasiveness, high cost and incidence of tumor metastases. In this study, we proposed a radiomic-clinical nomogram, based on multiple-sequence MRI combined with score of abdominal metastases and clinical markers, for preoperative prediction of RT status. We demonstrated that the radiomic-clinical nomogram had satisfactory prediction performance in all cohorts (AUC = 0.900-0.936). The clinical application value of the nomogram was further confirmed by decision curves.
Deep Learning-denoised Isotropic 2mm Whole Brain pseudo-Continuous Arterial Spin Labeling at 7T
Authors: Zhao Chenyang , Shou Qinyang , Shao Xingfeng , Wang Danny
Published: August 2024

Optimized pseudo-Continuous Arterial Spin Labeling has been implemented at 7T. To achieve whole brain high-resolution (2mm isotropic) perfusion imaging at 7T, however, requires prolonged scan time with an increased number of segments. A deep learning (DL) model was trained to boost the signal-to-noise ratio (SNR) for a scan with fewer repetitions and thus a shorter scan time. The analysis of SNR and temporal SNR suggests that at least 3 repetitions are needed to make a high-SNR prediction comparable to the full scan without compromising quantification accuracy. With DL denoising, the original 12 mins scan can be finished in 4 min.
Rapid Bayesian inference for perfusion quantification using ASL-MRI with a VAE-based neural network structure
Authors: Zhang Yechuan , Chappell Michael , Zheng Jian-Qing
Published: August 2024

A Variational Autoencoder (VAE) based framework was created to solve perfusion parameter estimation problem for ASL non-linear forward models. The ultimate goal was to build up an efficient and uncertainty-aware framework for parameter estimation problem in medical imaging, using the concept from Variaitonal Bayes (VB) which was already applied to ASL. Evaluation was performed using simulation and real data experiments with a bi-exponential model and two ASL-MRI forward models with different complexity. Compared with Markov Chain Monte Carlo (MCMC) and analytical VB (aVB), our VAE-based model achieved comparable accuracy, and hundredfold improvement in computational time.
Intensity-based Deep Learning for SPION concentration estimation in MR imaging
Authors: Di Biase Alberto , Liu Shuang , Sekino Masaki , Irarrázabal Pablo
Published: August 2024

SPION is a contrast agent with a wide range of biomedical applications. A new Deep Learning based method is presented for the quantification of SPION from intensity images. This contrast agent cause off-resonance artifacts, distorting the image. The field map is encoded in the difference of two images taken alternating the direction of the slice selection gradient. The network was trained on simulated data. The network is based on U-net and uses only 2D convolution to process the whole 3D volume, interpreting the last dimension as filters. Results are shown in simulations and on phantoms acquired on a 7T scanner.
Accelerate Single-Channel 3D MRI through Undersampling and Deep Neural Network Reconstruction
Authors: Man Christopher , Lau Vick , Hu Jiahao , Zhang Junhao , Xiao Linfang , Wu Ed
Published: August 2024

3D MRI data contains more redundant information than 2D MRI data, which is favourable for reconstruction. However, deep learning reconstruction of 3D MRI data remains to be explored due to the computational burden that scales exponentially with spatial dimensions. This study presents a deep learning method to reconstruct single-channel 3D MRI data with uniform undersampling along two phase-encoding directions, in which conventional multi-channel parallel imaging methods are generally not applicable. The results demonstrate the robust reconstruction for single-channel 3D MRI data at high acceleration and in the presence of anomaly.
Model-based neural ODE network for parallel MRI reconstruction
Authors: Lee Jun-Hyeok , Lee Gawon , Oh Se-Hong
Published: August 2024

In parallel MRI, DNN-based models have recently outperformed conventional reconstruction techniques and can reconstruct high-quality MRI images, especially at high acceleration factors. We propose a model-based neural ODE network to reconstruct artifact-free MR images from under-sampled k-space data. We replaced the existing U-Net with a modified U-Net framework using neural ODEs with E2E-VarNet as the backbone. Our network solves unrolled iterations of reconstruction optimization with neural ODEs, and each neural ODE uses a gradient update step as a dynamics step. Our approach showed the improved reconstruction performance comparable to the SOTA method with few parameters.
Multicoil Deep Equilibrium MRI Reconstruction
Authors: Shamim Muntasir , Tang Chenwei , Rivera Leonardo , Johnson Kevin
Published: August 2024

Deep equilibrium (DEQ) image reconstruction models have demonstrated improved performance while remaining memory efficient for single coil MRI reconstructions; however, DEQ models are known to be susceptible to model drift due to variabilities in the forward model. In the case of multicoil MRI, the forward model is changed due to the sampling patterns and coil sensitivity encoding. In this work, we implemented a multicoil DEQ model and analyzed model drift with respect to the coil sensitivities.
Deep Learning-based MRI Reconstruction with Artificial Fourier Transform(AFT)-Net
Authors: Yang Yanting , Laine Andrew F. , Guo Jia
Published: August 2024

Conventional medical image reconstruction methods are less parametric and lack generality due to random error and noise. A novel artificial Fourier transform (AFT) framework is developed which determines the mapping between k-space and i-space like DFT while can be fine-tuned with further training. The flexibility of AFT allows it to be simply incorporated into any existing deep learning network as learnable or static blocks. Reconstruction and denoising tasks are combined into a unified network that simultaneously enhances the image quality. AFT-Net achieves competitive results compared with other methods and proofs to be more robust to additional noise and contrast differences.
Image distortion correction for diffusion MRI using U-Net and Transformer
Authors: Ueyama Tsuyoshi , Takahashi Erika , Fujita Naoto , Suzuki Yuichi , Iwanaga Hideyuki , Abe Osamu , Terada Yasuhiko
Published: August 2024

Although several end-to-end deep neural networks have been proposed to correct image distortion directly from distorted images, no study has verified the distortion correction performance for high b-values diffusion-weighted image (DWI) and diffusion tensor image (DTI) parameters. For example, the U-Net-based Synb0-DisCo was only validated for distortion correction of b0 images. Here, we used two networks, U-Net and Trans-DisCo, to verify distortion correction performance for DWIs and DTI parameter images. Trans-DisCo is our proposed model that replaces the convolutional neural network in U-Net with Swin Transformer, and we have shown that it outperforms U-Net.
CloudBrain-LabelAI: An Online Intelligent Medical Imaging Annotation and Training Platform
Authors: Chen Bangjun , Wang Jian , Zhou Yirong , Hu Yu , Niu Shuxian , Qu Biao , Guo Di , Xu Jingjing , Dong Jiyang , Qu Xiaobo
Published: August 2024

Deep learning and cloud computing technologies have shown remarkable performance in medical imaging research. Using deep learning for clinical research requires a programming foundation, while the annotation of image data is a highly specialized and time-consuming process. In this work, we develop a high-performance online medical image annotation and training platform (CloudBrain-LabelAI). It provides medical image researchers with an efficient image annotation platform for the rapid construction of datasets for deep learning. Meanwhile, it provides codeless image segmentation training and prediction based on cloud computing, which greatly reduces the threshold of medical image segmentation efforts and simplifies the overall workflow.
Self-contained comprehensive quantification of dynamic contrast-enhanced MRI using physics+kinetics-based network learning (PKNet)
Authors: Kargar Soudabeh , Cohen Ouri , Woo Sungmin , Vargas Hebert , Otazo Ricardo
Published: August 2024

This work develops a deep learning technique that is trained according to physics and kinetics for self-contained comprehensive quantification of dynamic contrast-enhanced MRI. In addition to perfusion parameters, patient-specific parameters that affect the quantification are estimated, including bolus arrival time, T1, steady state magnetization, and AIF. The DCE-MRI quantification network was tested on a patient with cervical cancer and demonstrated high concordance between two scans separated by 24 hours. Physics plus kinetics informed network learning (PKNet) enabled the quantification of multiple parameters which has the potential to increase reproducibility of quantitative DCE-MRI, a long-desired goal.
The value of Intravoxel incoherent motion quantitative parameters in predicting Peritoneal Metastasis of Ovarian Cancer
Authors: Song Qingling , Li Ye , Liu Ailian
Published: August 2024

Ovarian cancer is a common fatal malignant tumor in clinic, and it is also a malignant tumor in the female reproductive system, which is third common followed cervical cancer and uterine cancer. It is easy to metastasize, among which peritoneal implantation is a common way of metastasis in the early stage.
Quantitative Placental R2* Mapping on Rhesus Macaques with Ischemic Injury Model
Authors: Liu Ruo-Yu , Keding Logan , Vazquez Jessica , Starekova Jitka , Zhu Ante , Chen Ruiming , Antony Kathleen , Simmons Heather , Stanic Aleksandar , Hernando Diego , Shah Dinesh , Golos Thaddeus , Wieben Oliver
Published: August 2024

Placental R2* mapping is a non-invasive, quantitative biomarker with the potential to identify deficiency in uteroplacental oxygenation and blood flow, which would be of significant value in predicting and monitoring pregnancy complications. We introduce a novel thrombotic placental injury model and report mean values, histograms, and spatial distribution of baseline R2* at different gestational ages in healthy and ischemic rhesus macaque placenta based on blood oxygenation level dependent (BOLD) MR imaging. Higher increases in mean values and different spatial patterns of R2* are observed in placenta with induced ischemia.
IVIM helps accurately diagnose PAS by showing the basal plate of placenta
Authors: Ting Liu , Jiaojiao Lu , Junjun Li , Huilian Hou , Jian Yang
Published: August 2024

Placenta accreta spectrum (PAS) is a serious threat to maternal life, but ultrasound and conventional MRI have limitations in the diagnosis of PAS. Intravoxel incoherent motion (IVIM) is a technique of diffusion weighted MR imaging, which allows separation of tissue diffusivity and perfusion information based on the signal intensity at varying degrees of b values. We found that IVIM was able to diagnose PAS by showing the basal plate of placenta, the structure dividing the placenta and the uterus. This new finding could improve the diagnostic efficacy of MRI in the diagnosis of PAS.
In silico optimization of iopamidol CEST MRI for renal pH mapping at 3 Tesla
Authors: Stabinska Julia , Zaiss Moritz , Bibic Adnan , Sedaghat Farzad , Sadowsky Cristina , van Zijl Peter , McMahon Michael
Published: August 2024

In vivo optimization of CEST iopamidol contrast in human subjects is complicated and requires multiple examinations and injections of the agent. To address this challenge, we propose application of a numerical approach that utilizes exchange rates determined under physiological conditions at 17.6T to perform kidney-like multi-pool Bloch-McConnell simulations for in silico optimization of saturation parameters for 3T applications. Our results suggest that the iopamidol-based CEST MRI is sensitive to pH in the range between 6 and 7.2 with the optimal results when short CEST saturation pulses (3x100 ms), low B1 strength (B1~0.8 µT) and short recovery time (Trec~T1w) are applied.
The exchange rates of protein guanidinium protons in the mouse brain
Authors: Wang Kexin , Sui Ran , Chen Lin , Li Yuguo , Xu Jiadi
Published: August 2024

We develop a novel two-step multi-B1 Bloch-McConnell fitting approach for calculating the exchange rate of CEST protons in vivo, and apply it to guanidinium protons, the exchange rate of which is 70.1 ± 5.5 s-1 with a concentration of 40.4 ± 5.2 mM in mouse brain at an optimized B1 of 0.8 µT. Guanidinoacetate N-methyltransferase deficiency (GAMT-/-) mice that have low creatine and phosphocreatine concentrations in brain are studied for protein guanidinium, i.e., arginineCEST (ArgCEST). The low exchange rate of ArgCEST suggests that the inverse pH dependence in GuanCEST with low B1 is dominated by CrCEST compared to ArgCEST.
Memorizing Transformer for Small-scale Multi-parametric MRI Brain Tumor Diagnosis
Authors: Shen Yiqing , Le Nhat , Wu Jingpu , Guo Pengfei , Zhou Jinyuan , Unberath Mathias , Jiang Shanshan
Published: August 2024

Deep learning approaches have been widely applied to the MRI field. Among them, transformers have received increasing popularity due to their capability in handling multi modalities. Yet, transformers are hungry for large-scale data, which is expensive to collect. Here, we develop a novel memorizing transformer for small-scale multi-parametric MRI analysis. Empirically, we evaluate the proposed method on a dataset curated from 147 brain post-treatment malignant glioma cases for classifying treatment effect and tumor recurrence. The proposed memorizing transformer boosted a 5.15% improvement in the test area under the receiver-operating-characteristic curve (AUC) to the baseline transformer approaches.
Sequential PET/MR reveals evidence of parasagittal dural hypertrophy in the setting of elevated Aβ burden in adults with Alzheimer’s disease
Authors: Song Alexander , Hett Kilian , Eisma Jarrod , Mcknight Colin , Elenberger Jason , Stark Adam , Kang Hakmook , Considine Ciaran , Donahue Manus , Claassen Daniel
Published: August 2024

A pathological hallmark of Alzheimer’s disease (AD) is the elevated aggregation of protein amyloid-β (Aβ) in the cerebrum. Recent studies have suggested a role for the parasagittal dural (PSD) space in cerebrospinal fluid (CSF) egress and associated protein clearance. A fully connected neural network was used to generate PSD segmentation masks from 3D T2-weighted turbo-spin-echo data to assess the relationship between PSD space volume and Aβ burden estimated by 11C-Pittsburgh Compound B in AD participants. PSD space hypertrophy was significantly associated with elevated Aβ levels and was localized to the frontal and parietal subsegments of the PSD.