bluuebunny/crossref_metadata_2025 huggingface dataset:
Prognostic Value of Global and Segmental LV Myocardial Strain Parameters Derived from CMR-FT Technique in AL-CA Patients: A Pilot Study.
Authors: Nai Rile , Wang Huihui , Liu Jia , Zhao Kai , Ma Shuai , Ma Wei , Wang Zhi , He Jiangkai , Xu Shasha , Lian Jianxiu , Li Wei , Qiu Jianxing
Published: November 2024

Motivation: Early detection of cardiac involvement and continuous monitoring of disease progression could benefit patients with light-chain amyloidosis. The application of left ventricular strain by Cardiac magnetic resonance feature tracking imaging has emerged as an excellent noninvasive imaging modality. Goal(s): This study is dedicated to exploring the prognostic implications of myocardial strain in distinct myocardial segments. Approach: The assessment of both global and segmental myocardial strain was conducted through the application of steady-state free precession (SSFP) cine sequences. Results: Global radial strain (RSglobal), RS in the basal lateral segment (RSbas-lateral) and B-type natriuretic peptides (BNP) are independently associated with all-cause mortality. Impact: Quantification of LV strain could be employed to monitor the progression of amyloid light-chain (AL) amyloidosis. Global radial strain and radial strain in the basal lateral segment may prove valuable for risk stratification in patients with AL amyloidosis.
MR feature tracking in assessment on myocardial deformation in patients on maintenance hemodialysis
Authors: Yan Huang Qian Zhang
Published: November 2024

Motivation: To explore the application value of cardiac magnetic resonance (CMR) feature tracking technique in patients on maintenance hemodialysis(MHD). Goal(s): To explore the application value of CMR feature tracking technique in patients on MHD. Approach: CMR examination was performed in both groups, and the left ventricular myocardial strain values were obtained by FT-CMR to explore the differences in myocardial deformation between the two groups. Results: With the increase of dialysis age, GCS, GRS and GLS were significantly lower than that in the low dialysis age group (-24.97±8.63 vs. -19.11±6.18, P=0.017; 23.31±9.96 vs. 33.23±15.88, P=0.022; -18.26±3.19 vs. -22.96±6.72, P=0.008). Impact: In those with preserved ejection fraction, myocardial strain parameters are reduced, suggesting that the use of FT-CMR technique is helpful for early diagnosis of myocardial deformation injury in MHD patients.
Optimization of variable-density undersampling for accelerated myocardial delayed enhancement imaging with deep learning reconstruction
Authors: Ma Junjie , Vinsky Michael , Zhu Xucheng , Carl Michael , Brada Rafi , Mazor Nir , Janich Martin , Atkins Melany
Published: November 2024

Motivation: Myocardial delayed enhanced (MDE) imaging is the gold standard for assessing myocardial viability in various cardiac pathologies. However, long breath-hold is needed for MDE to achieve reasonable spatial resolution, hampering its utility for patients with insufficient breath-hold capability. Goal(s): The goal for this study is to optimize a variable-density undersampling pattern to achieve highly accelerated MDE imaging combined with deep learning reconstruction. Approach: The optimization was conducted with phantom and post-contrast in vivo studies. Results: The optimized undersampling pattern and deep learning reconstruction enable 4-time acceleration for phase-sensitive MDE imaging with comparable image quality to the reference image. Impact: The optimized variable-density undersampling pattern combined with deep learning reconstruction can potentially expand the clinical utility of MDE imaging to especially patient with insufficient breath-hold capability, and improve the patient comfort.
Instance Segmentation Based approach for Robust automatic 3D Multi-View Planning for Cardiac MRI
Authors: Sharma Sumit , Sudarshan Viswanath Pamulakanty , Hegde Amruta , Mattathodi Razeem A Ali , VS Vineeth , VN Prasad , Saraswathy Suja , Neelavalli Jaladhar
Published: November 2024

Motivation: Manual planning for cardiac MRI (CMR) involving complex oblique views is time-consuming and introduces intra- and inter-technologist variability. Goal(s): To develop a fast and robust model that automatically predicts the standard CMR views from a 3D survey scan (SS) image. Approach: We use a deep learning (DL) based approach to map the 3D-SS image to four standard CMR using an instance segmentation strategy that identifies all planes followed by a finetuning strategy, which potentially offers improved robustness. Results: We achieved mean angulation and offset errors of 3.72±2.1 degrees and 2.36±1.8 mm, respectively across four standard CMR views averaged across 100 test subjects. Impact: Plane estimation as instance segmentation problem understands the spatial conformations of the planes and can adapt to various heart shapes and sizes and can go a long way in reducing intra- and inter-technologist variability and more importantly reducing scanning time.
Impact of isometric hand-grip exercise (IHE) on metrics of diastolic function by MRI
Authors: Xiang Jie , Peters Dana
Published: November 2024

Motivation: Exercise testing is important in evaluation of diastolic dysfunction but there is little use of exercise for diagnosis. Goal(s): Test if MR phase-contrast and cine techniques can identify changes in E, A, and e’ with hand-grip exercise. Approach: Five healthy subjects underwent diastolic function evaluation by MRI, during isometric handgrip exercise. E, A and e’ were evaluated. Results: In this group, E/A decreased with exercise, and recovered after a period of rest. Isometric handgrip exercise is promising approach for revealing diastolic dysfunction in patients. Impact: Isometric hand-grip exercise may be a useful tool in exercise cardiac MRI for diagnosis in patients with heart failure with preserved ejection fraction.
Improved cardiac cine MRI using a deep learning-based ESPIRiT reconstruction with video SWIN transformers
Authors: Jao Terrence , Sandino Christopher , Vasanawala Shreyas
Published: November 2024

Motivation: Cardiac CINE MRI is used clinically for characterizing heart morphology and function, but requires multiple breath-holds to minimize motion artifacts from respiration. Goal(s): Deep learning based ESPIRiT (DL-ESPIRiT) is able to reconstruct dynamic MRI data with high reconstruction accuracy. However, the method still has difficulty resolving fine anatomic structures. We aim to improve reconstruction accuracy using newer transformer architecture. Approach: We replace the ResNet deep learning backbone by a modified Swin Image Restoration network (SwinIR) with video Swin transformers, called DL-ESPIRiT VSwinIRResults: DL-ESPIRiT VSwinIR has substantially improved reconstruction accuracy and can accelerate acquisitions by up to 20x. Impact: Fast CINE acquisitions using our DL-ESPIRiT VSwinIR may enable multiple CINE slices to be acquired in a shortened breath-hold, which will allow heart morphology and function assessment in patients with difficulty breath-holding or following instructions.
3D Real-Time Whole Heart Cine MR Based on Spiral-In/Out bSSFP Sequence
Authors: Hu Yichen , Zhong Zheng , Hu Junpu , Li Hongyu , Liu Hui , Liu Qi , Ye Yongquan , Xu Jian
Published: November 2024

Motivation: To achieve high-temporal-resolution 3D whole-heart MR cine without the need for ECG, allow direct visualization of physiological motions, and assess cardiac function in clinical practice. Goal(s): To attain 3D multi-slice whole-heart real-time imaging with <50 ms temporal resolution, while preserving adequate spatial resolution for cardiac functional analysis. Approach: We employed golden-angle rotated spiral-in/out trajectory with 32× acceleration, and randomized variable density kz phase-encoding in the bSSFP sequence, and harnessed an iterative reconstruction algorithm. Results: We attained a groundbreaking achievement in advancing MRI capabilities, obtaining an ECG-free 3D real-time cardiac cine with complete 12-slice whole-heart coverage and an impressive sub-50 ms temporal resolution. Impact: We've employed advanced techniques to triumph over MRI's historical disadvantage of slower acquisition compared to other modalities. Pioneering a groundbreaking approach, we achieve high temporal resolution for 3D real-time imaging without ECG, breath-holding, or data segmentation, revolutionizing cardiac motion capture.
Insights from CMR Parameters and their Association with NT-proBNP Levels, A Retrospective Analysis.
Authors: Zhang Rui , Hao Yanhui , Zhu Yi , Guo Jianxin
Published: November 2024

Motivation: To investigate the potential of different CMR parameters as non-invasive, indirect indicators of NT-proBNP levels in HEpEF patients and to assess their utility in enhancing disease prognosis and assessment. Goal(s): The objective of this study is to explore the relationship between NT-proBNP and left ventricular functional parameters, T1 mapping, ECV, and myocardial strain parameters in CMR. Approach: A retrospective study was conducted with a cohort of HEpEF patients. Advanced cardiac MRI techniques were utilized to ascertain each CMR parameter. Results: A notable correlation was discerned between myocardial stress parameters and Native T1 values with serum NT-proBNP levels. Impact: NT-proBNP is a fundamental biomarker for assessing and predicting alterations in left ventricular function. Myocardial stress parameters and Native T1 showcase potential as non-invasive indicators.Future research must focus on multi-center, prospective studies to validate and expand on these findings.
Cardiac Characteristics in Pulmonary Hypertension Patients with Varying Cardiac Index: A Preliminary Study
Authors: Qin Guang Mei , Chen Si Qi , Qin Hao Dong , Zhang Hui Ting , Li Kai
Published: November 2024

Motivation: The prognosis of advanced pulmonary hypertension (PH) is poor, and incorrect treatment methods may accelerate this process. Goal(s): To comprehensively investigate the characteristics of PH by employing various imaging sequences, including 4D Flow, cardiac cine, T1/T2mapping. Approach: CMR imaging was performed on different cardiac index (CI) of PH patients and healthy volunteers to calculate various parameters of the heart and pulmonary arteries. Wilcoxon rank test was used to evaluate inter group differences. Results: A multi-sequence MRI approach effectively detects and characterizes abnormal heart and pulmonary artery features in PH of different CI, offering insights for early diagnosis and clinical management. Impact: The research has provided initial insights into the cardiac characteristics of high-CI and low-CI PH patients using magnetic resonance imaging (MRI) technology. It offers new data and perspectives to better understand the physiological and pathological changes in these patients.
An MRI-based radiomics method combined with clinic-radiological characteristics for the prediction of survival in aggressive spinal tumor
Authors: Wang Qizheng , Zhang Yang , Wang Tongyu , Lang Ning
Published: November 2024

Motivation: Treatment options for spinal tumors are challenging. Goal(s): To predict the progression-free survival (PFS) of aggressive spinal tumors based on MRI combined with clinical features to aid early personalized treatment decisions. Approach: Clinical, pathological and imaging data of 211 patients underwent spinal tumor surgery at two research centers were retrospectively analyzed. Tumoral and peritumoral features were extracted from T1WI and T2WI images. Clinical-radiomics nomogram was developed by radiomics signatures and the predictive clinical parameters. Results: The integrative model showed best performance in PFS evaluation for the prediction of postoperative PFS, with the AUC was 0.86 and 0.81 in the training and test cohort. Impact: Rad-score is an independent factor for PFS after resection for spinal tumor. The nomogram established in this study could be an effective tool for the clinical prediction of PFS after surgery.
Rapid MRI Protocol for Pediatric MSK Infections: Can We Safely Remove Contrast and Sedation?
Authors: Shah Rohan , Sanders Alison Esteva , Baghdadi Soroush , Krauss Jillian , Sagan Michelle , Wyers Mary , Gibly Romie , Samet Jonathan
Published: November 2024

Motivation: Magnetic resonance imaging (MRI) is the gold standard for diagnosis of acute pediatric musculoskeletal infection. However, there are several barriers in the pediatric population, namely poor efficiency of image acquisition. Goal(s): The present study evaluates physician perspectives on a novel rapid MRI protocol for capturing rapid, non-contrast, limited sequence protocol that has been shown to greatly decrease sedation, scan time, hospital length of stay, and charges without missing actionable diagnoses. Approach: We administered a series of traditional and rapid protocol cases and associated quiz to six providers. Results: We found similar success rates of diagnoses and physician confidence between rapid and traditional sequences. Impact: Our study shows that rapid MRI protocols can be adopted without impeding diagnostic accuracy or reducing the confidence of readers.
Predicting Total Knee Replacement Surgery Using Radiomic Features Extracted from MRI Scans
Authors: Montin Eros , Cigdem Ozkan , Rajamohan Haresh , Cho Kyunghyun , Kijowski Richard , Deniz Cem , Lattanzi Riccardo
Published: November 2024

Motivation: Predicting total knee replacement surgery can help patients and healthcare providers make informed treatment decisions. Goal(s): To develop a machine learning model for predicting patients that will undergo a total knee replacement using radiomics. Approach: To extract radiomic features from images of patients and healthy subjects and train different machine learning models to predict patients' outcomes. Results: The best model achieved an accuracy of 87.2%. Three out of the four most significant radiomic features selected in the All subset were derived from the meniscus areas, suggesting that the meniscus may play a crucial role in predicting patient outcomes. Impact: Radiomic features from MRI scans effectively classify TKR-positive patients, particularly those incorporating meniscus features. These models potentially can predict patient outcomes and guide treatment decisions, but further research is needed to enhance performance and validate findings in broader patient populations.
Improved Quantitative Spatial Analysis of Spontaneous Muscular Activities using Label Uncertainty and Feature Analysis
Authors: Schwartz Martin , Martirosian Petros , Steidle Günter , Yang Bin , Schick Fritz
Published: November 2024

Motivation: Understanding the visual representation of spontaneous activities in DWI. Goal(s): Automatically identifying visual differences in patterns of spontaneous muscular activities. Approach: Deep-learning based detection and segmentation with subsequent feature analysis. Results: Feasibility of feature-based clustering in individual subjects was shown. Impact: Investigation of a pipeline for automated image processing for exploring differences in spontaneous muscular activities visible in DWI.
Free-breathing Magnetic Resonance Fingerprinting for fat fraction and water T1 quantification of upper body muscles
Authors: Slioussarenko Constantin , Marty Benjamin
Published: November 2024

Motivation: Respiratory muscles are often altered in subjects with neuromuscular diseases. Characterizing their structure using quantitative MRI is then crucial but challenging due to respiratory motion. Goal(s): We developed a 3D free-breathing Magnetic Resonance Fingerprinting sequence for quantifying fat fraction (FF) and water T1 (T1H2O) of upper body muscles at 3T. Approach: We estimated the free-form respiration motion deformation on a 3D pre-scan using VoxelMorph and subsequently applied it in an iterative reconstruction framework to retrieve the MRF image series. Results: This method allows a significant reduction of motion artefacts on parametric FF and T1H2O maps. Impact: Free-breathing MRF T1-FF on 3T scanners paves the way for high resolution quantification of FF and T1H2O in the upper body muscles for monitoring their structural alterations in subjects with neuromuscular diseases with high precision.
Single-shot Trace Diffusion Weighted Radial Spectroscopic Imaging of Human Calf Muscle: Intra-/Extra-myocellular Lipids and Metabolites
Authors: Thomas Michael , Joy Ajin , Saucedo Andres , Emir Uzay , Darwin Christine
Published: November 2024

Motivation: To investigate the diffusion of calf muscle intra-myocellular droplets in diabetes and obesity. Goal(s): To validate diffusion-weighted radial echo-planar spectroscopic imaging (DW-REPSI) in calf muscle. Approach: We acquired DW-REPSI spectra using 2 different b-values in 3 young healthy subjects and one type2 diabetic patient; regional variations of intra- and extra- myocellular lipids (IMCL and EMCL), creatine and trimethylamines (TMA) and their diffusivities were quantified. Results: Decreased TMA was recorded in the tibialis anterior muscle compared to that of soleus and gastronomic muscles. ADCs of IMCL and EMCL and metabolites were quantified. Lower ADCs of IMCL indicated restricted motion. Shifts in EMCL was observed. Impact: A diffusion-weighted spectroscopic imaging sequence was evaluated to investigate the diffusion characteristics and regional variations of IMCL, EMCL and metabolites in calf muscle. The outcome may provide new biophysical insights in the investigation of lipid metabolism in diabetes.
One-year longitudinal assessment of CMT1A patients using Quantitative MRI
Authors: FORTANIER Etienne , HOSTIN Marc-Adrien , Michel Constance , DELMONT Emilien , BELLEMARE Marc-Emmanuel , Guye maxime , bendahan David , ATTARIAN Shahram
Published: November 2024

Motivation: Intramuscular fat fraction (FF) quantified using MRI has emerged as one of the few responsive outcome measures in neuropathic patients. Goal(s): In the present one-year follow-up study we tracked changes in multiple qMRI biomarkers for CMT1A patients. Approach: We assessed correlations between MRI and clinical parameters and compared 2D vs 3D segmentation analysis. Results: As expected, we quantified a significant FF rise in both thigh and leg muscles and a length-dependent gradient in leg muscles. Given the varied FF distribution, the existence of a length-dependent gradient, and the differential fatty involution across muscles, 3D volume analysis appeared more faithful than single slice analysis. Impact: Our longitudinal study further supports FF as a outcome of interest in neuropathic patients. The complexity of fat infiltration in terms of distribution among muscles and heterogeneity along the proximo-distal axis can be identified using a 3D volume analysis.
Using deep learning to predict muscle function based on anatomical MRI scans from the OAI and SOMMA datasets
Authors: Sveinsson Bragi , Kolachalama Vijaya , Hsieh Evelyn , Felson David
Published: November 2024

Motivation: Estimating physical performance including muscle function is conventionally done by having a subject complete physical tasks. MRI-based estimates leveraging deep learning could complement such measures. Goal(s): To investigate the feasibility of predicting measures physical performance including muscle strength from MRI scans of the leg using deep learning. Approach: We used large MRI databases (OAI and SOMMA) to train a neural network for classification into high or low physical performance. We also tried the method on a small prospective cohort. Results: We obtained over 70% accuracy for estimating high or low muscle function, indicating high predictive power. Impact: We demonstrate the feasibility of predicting muscle function from anatomical MRI scans using deep learning, leveraging advances in deep learning and musculoskeletal MRI databases that include functional measures. Such MRI-based predictions could complement conventional methods for estimating muscle function.
T2-Weighted MR Neurography and Muscle Evaluation Using GRAPPATINI T2 Mapping
Authors: Lu Amy , Lin Yenpo , Hilbert Tom , Cai Xiaoying , Sneag Darryl , Tan Ek
Published: November 2024

Motivation: To evaluate the feasibility and reliability of an accelerated T2 mapping technique (GRAPPATINI) for MR neurography and muscle evaluation. Goal(s): The study aims to qualitatively and quantitatively evaluate GRAPPATINI for MR neurography in patients with foot drop. Approach: To compare the image quality from GRAPPATINI T2-weighted images against conventional T2-weighted images, and to compare T2 values between abnormal and normal muscles. Results: No significant qualitative differences were observed between GRAPPATINI and conventional images diagnostically; there was complete agreement in assessment of nerve signal hyperintensity and size, and muscle edema. Significant differences in GRAPPATINI T2 values were observed between abnormal and normal muscles. Impact: In one acquisition, GRAPPATINI can simultaneously provide clinically equivalent qualitative information and quantitative T2 maps for MR neurography evaluation of foot drop.
Relationship between degree of resorption of lumbar disc herniation and fat content of paraspinal muscles
Authors: Chen Junrong , Wei Xiaocheng
Published: November 2024

Motivation: Conservative treatment can lead to lumbar disc herniation (LDH) regression. However, the relationship between LDH regression and paraspinal muscle (PSM) fat content remains unclear. Goal(s): To assess radiological changes in LDH with conservative treatment and investigate the connection between LDH regression and PSM fat content. Approach: 80 LDH patients underwent MRI before and after conservative treatments. They were divided into resorption and non-resorption groups. PSM fat content and age were analyzed. Results: 64% experienced LDH regression, mainly those with lower PSM fat content. Age-related muscle atrophy played a role. We highlights PSM fat content as a predictor of LDH regression during conservative treatment. Impact: The fat content of paraspinal muscles serves as an important predictor of the conservative treatment outcomes for LDH patients. Patients with lower fat content in their paraspinal muscles are more likely to experience lumbar disc reabsorption.
Disorganized Thalamic Subregional Functional Connectivity in Bipolar Disorder
Authors: Long Xipeng , Wang Xiuli , Cao Yuan , Kong Di , Wu Baolin , Xie Hongsheng , Zhao Ziru , Roberts Neil , Gong Qiyong , Jia Zhiyun
Published: November 2024

Motivation: Compared to cortical regions, the involvement of functional connectivity of subcortical regions in bipolar disorder (BD) are less well known. Goal(s): The potential involvement of subcortical and subregions of thalamus are explored in BD. Approach: Seed-based subcortical and thalamic subregional functional connectivity was compared between the BD patients and HCs, as well as between patients with BD type I (BD-I) subgroup and HCs as a sub-group analysis. Results: BD patients showed increased functional connectivity between left thalamus and right lingual gyrus, between right anterior thalamus and superior frontal gyrus, and between right ventral anterior thalamus and precuneus. Impact: These findings provide evidence of disorganized thalamo-cortical functional connectivity in BD, suggesting that the thalamus and its subregions may play important and specific roles in the neural circuitry of BD.
Mapping region-specific cerebrovascular reactivity in patients with major depressive disorder
Authors: Lin Zixuan , Hou Xirui , Liu Peiying , Lu Hanzhang , Wang Min
Published: November 2024

Motivation: Impaired global cerebrovascular reactivity (CVR) has been reported in major depressive disorder (MDD). However, regional CVR has not been evaluated in MDD patients due to experimental difficulties. Goal(s): The goal is to use a recently developed resting-state technique to map regional CVR in MDD patients. Approach: Resting-state images was filtered and relative CVR index was calculated by regressing the voxel-wise BOLD signal against the reference signal. Results: CVR was significantly reduced in occipital lobe in MDD. Temporal CVR was negatively correlated with illness duration, while psychomotor retardation was associated with elevated CVR in several emotion-related areas, including amygdala. Impact: Current study indicated a region-specific relationship between cerebrovascular dysfunction and depression.
Abnormal amygdala resting-state functional connectivity in patients with anxiety disorders: A coordinate-based imaging meta-analysis.
Authors: Xu Haoran , Wang Rui , Gong Qiyong
Published: November 2024

Motivation: It is necessary to clarify the neurobiological mechanisms of anxiety disorders (ADs) to provide a unique biomarker for diagnosing ADs, facilitating accurate clinical diagnosis and targeted therapeutic interventions. Goal(s): The primary objective is to elucidate the specific abnormalities in individuals with ADs' amygdala-based whole-brain connectivity. Approach: The coordinate-based meta-analysis was conducted to find amygdala resting-state functional connectivity in patients with ADs. Furthermore, subgroup analysis was performed to explore potential functional lateralization. Results: Distinct functional connectivity abnormalities were found in ADs, which provides a reference for diagnosing ADs. Impact: These findings not only enhance our understanding of the underlying pathophysiological mechanisms in ADs but also may be valuable for the prevention, diagnosis and intervention of ADs.
Multilayer network analysis reveals instability of brain dynamics in untreated first-episode schizophrenia
Authors: Gao Ziyang , Xiao Yuan , Zhu Fei , Tao Bo , Zhao Qiannan , Yu Wei , Sweeney John , Lui Su
Published: November 2024

Motivation: Although aberrant static functional connectome in schizophrenia has been reported, little is known about how the neural dynamics change in first-episode schizophrenia and are modulated by antipsychotic treatment. Goal(s): We aim to characterize dynamic topological reconfiguration of brain connectome in schizophrenia. Approach: Multilayer network analysis was applied to calculate the network switching rates between brain states. We compared switching rates of patients and controls at baseline, and tested for changes after one-year of treatment. Results: Significantly increased network switching rates were found in patients at baseline, mainly in the sensorimotor and dorsal attention networks. Switching rates were reduced after treatment. Impact: The findings of excessive neural flexibility in patients extend our understanding for the disease-related brain dynamics aberrance in schizophrenia, and the normalization of network switching rates further illustrate the biological mechanism underlying antipsychotic treatment from a perspective of neural dynamics.
Dose Dependence of Acute Nicotine on Neurometabolic Activity in Mouse Brain
Authors: Biyani Prajakta , Patel Anant
Published: November 2024

Motivation: Nicotine addiction is a neuropsychiatric disorder with worldwide deaths. Nicotine affects dopaminergic system. However, the dose dependent effect of nicotine on neurometabolic activity is not clear. Goal(s): To evaluate the impact of dose dependence of acute nicotine on the metabolic activity of glutamatergic and GABAergic neurons in the prefrontal cortex in mice. Approach: Different acute subcutaneous doses of nicotine were given to mice. Prefrontal cortex extracts were analysed by 1H-[13C]-NMR spectroscopy to quantify 13C labelled amino acids and rate of glucose oxidation in neurons. Results: Nicotine at low dose has excitatory but at high doses has inhibitory effect on neuronal activity in mice. Impact: Our data shows that nicotine at 0.025 mg/kg increases metabolic activity of glutamatergic neurons but at 1.0 and 2.0 mg/kg decreases. Additionally, nicotine at 1.0 and 2.0 mg/kg suppresses metabolic activity of GABAergic neurons.
Stimulant medication and development of the dopamine system in ADHD: a pharmacological MRI study
Authors: van der Pal Zarah , Mutsaerts Henk Jan , Kaiser Antonia , Bottelier Marco , Geurts Hilde , Reneman Liesbeth , Schrantee Anouk
Published: November 2024

Motivation: The effects of stimulant treatment (e.g. methylphenidate) on the brain’s dopamine system remain unclear. While animal studies and short-term human trials suggest potential lasting impacts, a comprehensive understanding is lacking. Goal(s): This study aimed to investigate the persistent effects of stimulant treatment on the dopamine system. Approach: Using data from a naturalistic follow-up of an ADHD trial, we examined the cerebral blood flow (CBF) response to methylphenidate as a proxy for dopamine function. Results: Our findings reveal age-dependent effects. Specifically, we observed a negative association between medication use and CBF response in adolescents, while such an association was absent in adults. Impact: We found an association between patterns of stimulant treatment for ADHD and the cerebral blood flow response to a dopamine-challenge in adolescents, but not adults. This suggests potential for personalization of stimulant treatment approaches for adolescents with ADHD.
Correlation of cortical gray/white matter ratio and body mass index (BMI) in controls and major depression patients scanned with 7T MRI
Authors: Verma Gaurav , Kirsch Claudia , Balchandani Priti
Published: November 2024

Motivation: Better understand the relationship between depression, obesity and the brain. Goal(s): Investigate the relationship between body mass index and brain gray and white matter volumes using high-resolution 7T MRI in a population of healthy controls and major depression patients. Approach: T1-weighted MRI was obtained at 7T and segmented using FreeSurfer. 44 MDD patients and 47 healthy controls of comparable age and body mass index (BMI) were scanned. Results: BMI showed significant positive correlation with white matter volume and negative correlation between gray matter volume and gray/white ratios in healthy controls and combined cohorts. Similar, though non-significant trends were observed in MDD patients. Impact: The presented study is the first to ultrahigh field MRI to detect significant correlations between BMI and gray/white matter brain volumes. Differences in these correlations were observed between MDD patients and healthy controls.
Abnormal connectome of dorsal raphe in methamphetamine addiction: a resting-state fMRI study
Authors: Zhang Mingyu , Li Yadi , Wang Shuyuan , Cheng Ping , Wang Jie , Wang Gaoyan , Liu Huifen , Shen Wenwen , Wu Pu-Yeh
Published: November 2024

Motivation: This study aims to investigate the role of the dorsal raphe nucleus (DRN) and the ventral tegmental area (VTA) in the mechanism of methamphetamine (MA) induced nerve injury. Goal(s): To further explore the neural mechanism of MA addiction. Approach: Analyze the functional connections, effect connections, and graph theory analysis between DRN and VTA with other brain regions. Results: MA-dependent patients showed enhanced FC between the DRN and multiple brain regions in the whole brain. Topological analysis showed that multiple topological attributes of the dorsal raphe nucleus were abnormal. Impact: Aberrations in the connecome of the dorsal raphe nucleus among individuals with methamphetamine dependence may provide novel insights into the neural mechanisms underpinning addiction to this substance
Transcriptional and Cellular Decoding of Cortical Morphometric Changes in Chronic Insomnia Disorder
Authors: Yu Siyi
Published: November 2024

Motivation: Chronic Insomnia Disorder (CID) influences various levels of brain organization, spanning macroscopic structures to microscopic genomics, the connection between genomic variations and structural brain alterations in CID was not clear. Goal(s): This study analyzed structural brain alterations-related genomes in CID. Approach: Using and morphometric similarity (MS) and imaging-transcription alanalysisResults: The study identified MS reductions in the parietal and limbic regions, as well as enhancements in the temporal and frontal regions in CID patients. The study also discovered spatial correlations between MS alterations and genes associated with excitatory and inhibitory neurons, as well as genes implicated in chronic neuroinflammation processes. Impact: This study bridges the gap between cortical structural changes and the molecular mechanisms in Chronic Insomnia Disorder (CID), shedding light on the genetic basis and brain alterations associated with CID.
Automatic rhythmic pressuring leading to instantly promotes drowsiness at RN12 site.
Authors: Zhou Xiaohan , Liang Yan , Liu Wentao , Fan Yan , Han Dong
Published: November 2024

Motivation: This study is motivated by the increasing interest in non-pharmacological approaches, like RN12-ARP, to improve sleep quality and address sleep-related problems. Goal(s): The specific aim is to investigate RN12-ARP's effects on sleepiness, EEG patterns, and brain activity in individuals with insomnia, exploring its potential as a solution for sleep disturbances. Approach: Seventeen participants undergo RN12-ARP sessions, collecting EEG, sleep diaries, and psychological assessments, analyzed using high-density EEG and MRI. Results: The study shows that RN12-ARP induces immediate sleepiness, supported by EEG-fMRI. Psychological scales have limited effects, but sleep diaries hint at improved sleep, suggesting potential for insomnia treatment, pending further research. Impact: The study might pave the way for non-pharmacological interventions like RN12-ARP to address insomnia, improving the well-being of patients with sleep disturbances. Further investigations into the mechanisms behind quick drowsiness are pending, offering hope for alternative approaches to sleep therapy.
Robustness of Diffusion Model-Based Methods to Distribution Shifts in Medical Imaging
Authors: Jiang Wei , Gao Yang , Liu Feng , Ye Nan , Sun Hongfu
Published: November 2024

Motivation: Diffusion model (DM)-based methods demonstrate competitive performance in solving medical imaging inverse problems. However, it remains uncertain whether these methods are vulnerable to distribution shifts, a common issue in traditional learning-based approaches. Goal(s): Our goal is to conduct experiments to evaluate how well DM-based methods handle distribution shifts in various scenarios. Approach: We utilize three different sampling methods based on a pretrained Diffusion model and apply them to four clinical tasks without fine-tuning. Results: Experiments indicate that DM-based methods efficiently address distribution shifts without requiring fine-tuning. They exhibit robustness to many distribution shifts, even when the test data deviates from the training data. Impact: DM-based methods reduce resource consumption and the need for extensive training datasets, potentially inspiring further development of DM-based techniques for enhancing in-vivo tasks with limited resources.
Retrospective k-Space Synthesis for Cardiac MRI Deep-learning Applications from Magnitude-only Images Using Score-based Diffusion Models
Authors: Yalcinkaya Dilek , Sahin M. Berk , Dharmakumar Rohan , Hashemi Abolfazl , Sharif Behzad
Published: November 2024

Motivation: Developing deep learning (DL)-based image reconstruction techniques requires raw k-space datasets. The use of magnitude-only MRI images (DICOMs) to obtain k-space can be prohibitive for training robust models. Goal(s): To synthesize phase-maps of DCE cardiac MRI from magnitude-only images by using the recently emerging diffusion models. Approach: A conditional score-based diffusion model (SBDM) is trained to synthesize phase-maps from the magnitude-only images. The value of the synthesized phase-maps is assessed with a DL-based image reconstruction model. Results: SBDM-derived phase-maps outperformed random and GAN-based phase-map generation methods in terms of reconstruction performance. Qualitative assessment suggests that SBDMs can generate realistic-looking phase-maps. Impact: We proposed to leverage the emerging generative diffusion models for retrospective phase-map synthesis of DCE cardiac MRI from the magnitude-only images which has the potential to create large k-space datasets using the magnitude-only multi-center registries to improve deep learning-based reconstruction.
A Denoising Diffusion Probability Model for T1W contrast-enhanced Synthesis based on multi-parametric MRI
Authors: Lei Chen , Zhang Jing , Hu Peian , Li Ruimin , Li Yi , Luo Rong , Zhang Zehua , Xiang Huijing , Duan Yuqi , Li Chunxiang , Zhou Zhengrong , Jia Shuying , Sun Mengzhou , Liang Xiaoyun
Published: November 2024

Motivation: T1W Contrast-enhanced (T1CE) images are obtained with gadolinium administration, but there might be adverse effects related to gadolinium retention. Goal(s): To generated T1CE images from multi-parametric MRI (mp-MRI) without contrast agents. Approach: T1W, T1map, SWI and QSM were obtained from patients with brain metastasis. A novel approach for generating non-contrast enhanced images from mp-MRI was proposed based on the diffusion model, which was trained in 91 cases, evaluated and test 28 and 27 cases respectively. Results: The proposed model achieves the highest SSIM of 0.78, and the synthetic images are capable of revealing the details of brain tissues. Impact: Multi-parametric MRI based DDPM provides a feasible approach for generating contrast enhanced images from non-contrast multi-parametric MRI, therefore circumventing the issue of adverse effects of gadolinium retention, which will benefit patients who have to undergo contrast enhanced MRI scans.
Synthesis from PET to MR and CT modal images using the latent diffusion model
Authors: Zhang Qiyang , Han Tianrun , Huang Zhenxing , Huo Li , Yang Yongfeng , Zheng Hairong , Liang Dong , Cui Ruixue , Hu Zhanli
Published: November 2024

Motivation: Medical images in different modalities (MR\PET\CT) can provide different information, which can help to fully understand a patient's condition and assist physicians in making accurate diagnoses and treatment plans. Goal(s): Using the diffusion model to generate multiple modal images from a single modality. Approach: We propose the conditional latent diffusion model (CLDM) guided by category information to address the challenge of completing the target modal image within the same body. Results: Compared to images generated by GANs, our model produces higher quality images with enhanced capabilities, particularly in capturing intricate details. Impact: Our study offers bright future for diffusion models in the complementary field of medical imaging modalities.
Self-Supervised Deep-Learning Networks for Mono and Bi-exponential T1ρ Fitting in the Knee Joint
Authors: Singh Dilbag , Regatte Ravinder R. , Zibetti Marcelo V. W.
Published: November 2024

Motivation: The nonlinear least squares (NLS)-based estimation of mono- and bi-exponential T1ρ maps in the knee joint is highly time-consuming. Deep-learning (DL) methods are faster alternatives. Goal(s): However, DL requires substantial training data, which is usually obtained using NLS on acquired data. This paper introduces self-supervised DL models that leverage synthetic target data for training, eliminating the need for scanned or NLS data to be used as reference. Approach: We have tested five different DL models, each utilizing a distinct activation function, and compared them against NLS. Results: The proposed models are 25-200x faster than the NLS method, with errors close to NLS. Impact: This study compared five different self-supervised DL models for estimating mono- and bi-exponential T1ρ maps in the knee joint. These models are faster alternatives to NLS, potentially replacing it to produce reference maps.
Denoising intrinsic MRI repetitions using self-supervised iterative residual learning
Authors: Li Zihan , Bilgic Berkin , Li Ziyu , Ying Kui , Salat David , Polimeni Jonathan , Liao Hongen , Huang Susie , Tian Qiyuan
Published: November 2024

Motivation: MRI with high resolution and/or acceleration factor suffers from intrinsic low signal-to-noise ratio. Supervised learning-based denoising significantly improves image quality, but requires high-SNR data as training targets. Goal(s): To denoise images using noisy image repetitions without additional acquisition. Approach: Noise2Average trains CNN to map each noisy image to its residual compared to the average of all noisy images at iteration 1 and all denoised images from iteration k-1 at iteration k. The images from opposite phase-encoding directions of EPI or different echo times of ME-MPRAGE are noisy repetitions. Results: Noise2Average outperforms BM4D, AONLM and Noise2Noise in terms of image quality and DTI metrics. Impact: By reducing the requirement for training data and time, Noise2Average substantially increases the feasibility and accessibility of deep learning based denoising methods for MRI and potentially benefits a wider range of clinical and neuroscientific studies.
A Contrastive Learning Approach for Unsupervised Anomaly Detection on Contrast-Enhanced Brain MRI Images
Authors: Pasumarthi Srivathsa , Kumar Sidharth , Chamberlain Ryan
Published: November 2024

Motivation: Unsupervised anomaly detection (UAD) approaches on T1 contrast-enhanced (T1CE) images are currently not feasible as T1CE images of healthy individuals are not typically available. Goal(s): In this work, we aim to eliminate the need for large labeled datasets that are required for manual anomaly detection on T1CE images. Approach: Using deep learning (DL), we synthesized healthy T1CE images from non-contrast images available in public datasets. We also synthesized healthy-anomalous paired images and forced the DL network to learn the healthy reconstruction. The anomalies were localized by subtracting the reconstruction from the input image. Results: The proposed method achieves state-of-the art dice score coefficients. Impact: This work opens up new avenues of research in unsupervised anomaly detection on T1CE images which has been infeasible due to lack of healthy post-contrast images. We also propose a novel contrastive learning paradigm using synthesis of healthy-anomalous image pairs.
Joint Multi-Contrast Image Reconstruction with Self-Supervised Learning
Authors: Kadota Brenden , Millard Charles , Chiew Mark
Published: November 2024

Motivation: Self-supervised learning via data undersampling (SSDU) uses single contrast images in reconstruction, but a typical protocol contains multiple contrasts that provide additional information. Goal(s): Our goal is to improve self-supervised image reconstruction fidelity by jointly reconstructing multi-contrast images. Approach: We modify SSDU by concatenating independently under-sampled contrasts along the channel dimension in a VarNet architecture. Results: Joint multi-contrast SSDU reconstructs with higher SSIM and lower NMSE than single contrast supervised and self-supervised methods. Impact: Joint multi-contrast SSDU produces higher quality reconstructions than single-contrast methods, without fully-sampled training data. Accelerated multi-contrast imaging protocols will benefit from higher diagnostic quality or higher acceleration factors.
Non-central chi likelihood loss for quantitative MRI from parallel acquisitions with self-supervised deep learning
Authors: Parker Christopher , Alexander Daniel , Zhang Hui
Published: November 2024

Motivation: The distribution of reconstructed MRI signals, used as input for quantitative MRI with self-supervised deep learning, depends on the number of receiver coils. Current loss functions do not account for this, leading to bias. Goal(s): Develop a non-central chi likelihood (NLC) loss that accounts for the distribution of MRI measures in the most common scenario of parallelised acquisitions. Approach: Implement and evaluate the NLC loss and compare its performance against the MSE and Rician likelihood loss in simulated data. Results: The NLC improves performance compared to the Rician likelihood and MSE loss for the mono-exponential ADC model in simulated data. Impact: The NLC loss permits fast inference of parameters from MRI signals reconstructed from parallelised acquisitions and may reduce bias compared to the Rician and MSE loss. The NLC loss is widely applicable due to the abundance of parallelised MRI acquisitions.
Enhancing Low-Count PET Image Quality via Unsupervised Deep Learning using Novel Cost Function – A PET/MRI study
Authors: Zhao Tianyun , Huang Chuan
Published: November 2024

Motivation: PET/MRI enables MR-assisted PET denoising for low-count PET. Recent work has demonstrated the potential of unsupervised deep learning (uDL) denoising method with the advantage of not requiring large amount of training data. We believe the performance of uDL denoising with MR guidance can be further improved. Goal(s): To demonstrate the efficacy of a novel cost function in enhancing the quality of denoised low-count PET images using uDL techniques. Approach: We utilized dNet with MRI as input and low-count PET as target, along with a novel cost function consisting of Bowsher prior and mean square error. Results: Our method outperformed all other denoising methods. Impact: The impact of the study is the potential for significant improvements in low-count PET image quality through advanced denoising techniques, which could enhance diagnostic accuracy while reducing radiation exposure for patients.
Defining right ventricular energy metabolism in patients with pulmonary arterial hypertension using hyperpolarized [1-13C]pyruvate
Authors: Park Jae Mo , Barton Gregory , Lin Sung-Han , Harrison Crystal , Malloy Craig , Goss Kara
Published: November 2024

Motivation: Right ventricular (RV) failure in pulmonary arterial hypertension (PAH) increases mortality risk, yet its metabolic characteristics is largely unknown, primarily due to limitations of imaging the RV. Goal(s): This study examines the relative contribution of oxidative metabolism versus glycolysis in the failing RV before and after initiation of pulmonary vasodilator therapy for PAH. Approach: Patients with newly diagnosed PAH are examined by HP [1-13C]pyruvate MRI at baseline and again 4-6 months after initiation of pulmonary vasodilator therapy. Results: Patients exhibited hyperintense HP bicarbonate signals in RV free wall. After treatment, normalized myocardial [13C]bicarbonate production increased. Impact: Importance of metabolic flexibility in RV heart failure is underexplored. A better understanding of how the RV myocardium remodels in RV failure from PAH and in response to therapy may allow for development of RV-targeted therapies to maintain RV function.
Preliminary study of myocardial substance metabolism in amateur athletes - Based on 1H-MRS
Authors: Chen Jing , Zheng Xue , Feng Xiaolan , Wang Li , Chen Meining
Published: November 2024

Motivation: Exploring metabolic markers for detecting cardiac remodeling (CR) in athletes to differentiate adaptive from harmful changes. Goal(s): To use 1H-MRS for identifying metabolites predictive of CR in athletes. Approach: Recruitment of male athletes and controls for CMR examination, analysis of myocardial metabolites using 1H-MRS, and application of machine learning algorithms to construct predictive models for CR. Results: Discovery of a correlation between increased myocardial creatine levels and lipid ratios in athletes with CR, with 1H-MRS proving effective in predicting CR, highlighting MYCL-CH3/W as a particularly predictive metabolite, and positioning the KNN algorithm as a robust predictive tool. Impact: The study advanced sports cardiology by identifying myocardial metabolites as noninvasive markers for differentiating between healthy and adverse cardiac remodeling in athletes, enhancing training strategies and early detection of cardiac risk.