bluuebunny/crossref_metadata_2025 huggingface dataset:
Open-Source Automatic Whole and Subchondral Bone Segmentation using a Deep-Learning-Based Framework, DOSMA
Authors: Goyal Ananya , Pedersen Rune , Vainberg Yael , Haddock Bryan , Chaudhari Akshay , Kogan Feliks , Gatti Anthony
Published: November 2024

Motivation: [18F]NaF PET imaging is a promising technique to study the role of bone metabolism in joint diseases such as osteoarthritis. To ease the corresponding burden of segmentations and data analysis, we developed an automated pipeline. Goal(s): We developed a new automated pipeline for knee bone segmentations. We validated the creation of subchondral bone masks for applications to [18F]NaF PET imaging by measuring changes in PET measures. Approach: We developed an automated segmentation pipeline for bone segmentations and validated the results for PET imaging. Results: DOSMA automated segmentations perform highly for bones and show applicability for quantitative musculoskeletal analysis. Impact: Our automated bone segmentation and PET data analysis pipeline enables a streamlined way of automating PET-MRI processing, including registration, segmentation, quantitative mapping, and visualization of outcome measures.
MRF-Synth: An Image Generation Framework for Learning Contrast-Invariant Brain Segmentation
Authors: Adams Richard , Zhao Walter , Sun Jessie , Hu Siyuan , Ma Dan , Yap Pew-Thian
Published: November 2024

Motivation: Intensity-based brain segmentation methods face challenges with generalizability, as they are susceptible to site, age, and contrast variations. Goal(s): Develop an efficient, unified framework to train segmentation networks that are insensitive to contrast variations. Approach: MRF sequences encode image time series that include both common and uncommon contrasts. We develop MRF-Synth, a framework to generate contrasts from MRF quantitative maps for training and evaluating contrast-invariant networks. Results: We show that a segmentation U-Net trained with MRF-Synth yields highly consistent results across contrasts, vendors, and ages (DICE > 0.86 in adults). Impact: MRF-Synth represents an efficient, generalizable framework for developing and evaluating contrast-invariant segmentation networks. We demonstrate the utility of MRF-Synth in training a U-Net to segment healthy MR brain images into 18 anatomical regions regardless of contrast, scanner, vendor, or age.
Visualization of Thalamic Subnuclei using DiMANI (Diffusion MRI for Anatomical Nuclei Imaging)
Authors: Patriat Remi , Palnitkar Tara , Chandrasekaran Jayashree , Wong Karianne Sretavan , Braun Henry , Yacoub Essa , McGovern Robert , Aman Joshua , Cooper Scott , Vitek Jerrold , Harel Noam
Published: November 2024

Motivation: Lack of direct visualization methods for thalamic subnuclei has resulted in variable patient outcomes and repeat surgeries in deep brain stimulation (DBS). Goal(s): To generate images with sufficient intra-thalamic contrast to visualize subnuclei for surgical intervention. Approach: We introduce DiMANI, an image obtained from combining diffusion-weighted volumes. We compared DiMANI to atlases as well as intra- and post-operative clinical DBS data. Results: DiMANI showed strong correspondence to anatomical organization from atlases, was highly reproducible, and was observable at both 3T and 7T. Clinical data from six DBS patients corroborated DiMANI’s ability to identify the motor and sensory thalamus locations. Impact: Visualization of thalamic subnuclei is now achievable using DiMANI, enabling direct targeting for DBS and MR-guided focused ultrasound procedures. This will provide immediate impact by enhancing clinical workflow efficiency, improving patient outcomes and advancing brain networks conception.
Spatial-temporal segmentation of cine cardiac MRI time-series
Authors: Qin Yingqi , Guo Fumin , Zhou Xin
Published: November 2024

Motivation: Cine cardiac MRI provides a way to quantify additional cardiac indices beyond ejection fraction, including ejection and filling rates, myocardial wall motion, and strain; segmentation of all temporal phases is required. Goal(s): To develop an approach to segmenting images in all cardiac phases in cine MRI. Approach: A U-net and a recurrent-neural-network were integrated to exploit the spatial-temporal information in cine time-series. 100 and 50 subjects labeled at the end-systole and end-diastole phases were used for network training and testing, respectively. Results: The use of spatial-temporal information substantially improved the segmentation accuracy and the algorithm cardiac indices were strongly correlated with manual measurements. Impact: The proposed method made effective use of the spatial-temporal information in a cine time-series and yielded highly accurate and precise segmentation and cardiac functional measurements, suggesting the utility of our approach for clinical cardiac patient care.
Deep learning segmentation of I-125 brachytherapy seeds in prostate cancer patients based on synthetically generated multi-echo training data
Authors: Mücke Lion , Grigo Johanna , Karius Andre , Bert Christoph , Uder Michael , Laun Frederik , Hanspach Jannis
Published: November 2024

Motivation: Deep learning (DL) networks trained with synthetically generated data enable the visualization of I-125 brachytherapy seeds in prostate cancer patients in quantitative susceptibility mapping (QSM), possibly eliminating the need for a CT-scan in future. Goal(s): The Goal was to automatically detect and segment I-125 seeds in-vivo by using a DL network directly (without QSM) on gradient-echo-sequence (GRE) data. Approach: A U-Net was trained with synthetically generated multi-echo GRE magnitude and phase input data and corresponding target seed segmentations. Results: The seed segmentations were of high visual quality and showed good agreement (85% detection rate) with corresponding CT-scans in five prostate cancer patients. Impact: This work proposes a fast and completely automatic MRI-only based workflow for segmenting in-vivo brachytherapy seeds in prostate cancer patients. This approach has the potential to eliminate the need for a CT-scan, thereby reducing the use of ionizing radiation.
Approaches for correcting motion in diffusion-weighted imaging acquired using diffusion gradient cycling
Authors: Xu Boyan , Hu Shaojun , Fan Yang , Wu Bing , Song Ming
Published: November 2024

Motivation: Diffusion gradient cycling (DGC) enables more efficient diffusion-weighted imaging (DWI) scanning, but it is not compatible with typical preprocssing pipelines. Goal(s): Our goal is to propose two approaches for correcting motion in DWI data acquired using DGC: slice-to-volume registration and volume-to-volume registration with the assistance of slice reordering. Approach: Intentional motion was introduced during the DWI acquisition, and the proposed approaches were implemented and applied to remove artifacts caused by this motion. Results: Both approaches effectively eliminated motion-induced artifacts, and the intentional motion was estimated correctly. Impact: Motion-induced artifacts can be eliminated, and correct motion estimation can be achieved in DWI acquired with DGC. Our proposed approaches are publicly available and can be easily integrated into preprocessing pipelines.
Efficient and High-Quality Ray Marched Glyphs for Diffusion Tensor Imaging
Authors: Guerra Javier Guaje , Delgado Tania Castillo , Koudoro Serge , Jaramillo Francisco Gomez , Garyfallidis Eleftherios
Published: November 2024

Motivation: Traditional rendering engines represent surfaces using simpler polygons (triangles or quads). This discretization can compromise real-time performance as more polygons are required to build smoother surfaces. Goal(s): Develop a new efficient method for building and displaying DTI-related glyphs without compromising their visual quality using continuous geometric representations. Approach: Ray marching is a robust technique for tracing implicit surfaces using signed distance functions (SDFs). Our method uses these techniques to create representations of commonly used DTI-related glyphs. Results: Our SDFs glyphs make it possible to define smooth surfaces in a more efficient way that allows us to visualize more objects than traditional polygon-based renderers. Impact: By using SOTA efficient rendering algorithms for complex elements, such as DTI-related glyphs, we improved the performance of scientific visualization systems, enabling the visualization of more extensive datasets and/or multiple types of data in real-time without sacrificing visual quality.
Using time-dependent diffusion MRI to investigate brain tumor differentiation
Authors: Mao Jiaji , Hu Chihhsuan , Yan Zhuoheng , Qin Weifeng , Wang Mengzhu , Yan Xu , Chen Meining , Shen Jun
Published: November 2024

Motivation: Imaging microstructural parameters using limited spectrally edited diffusion (IMPULSED) based on time-dependent diffusion MRI (dMRI) could be utilized to quantify cell size, cellularity, and extracellular space, but its ability to distinguish common brain tumors remains unknown. Goal(s): To determine the capacity of IMPULSED to distinguish between glioma, brain metastases, and meningioma. Approach: Time-dependent dMRI was performed on patients with untreated glioma (n = 24), brain metastases (n = 67), and meningioma (n = 38). These patients’ IMPULSED-based parameters were evaluated and compared. Results: Glioma, brain metastasis, and meningioma have distinctive IMPULSED-based parameters. Impact: IMPULSED-based parameters could distinguish glioma, brain metastases, and meningioma. Time-dependent diffusion MRI is a potential approach for assessing the microstructures of brain tumors.
HARP: Hierarchical Anatomical Refinement of Pathways in Tractography
Authors: Leserri Simona , Aydogan Dogu Baran
Published: November 2024

Motivation: Whole brain tractography is highly challenging, and it is prone to false positive streamlines despite the use of Anatomically Constraint Tractography (ACT). Goal(s): This study aims to reduce implausible streamlines in tractograms by introducing a novel refinement approach, thereby enhancing the accuracy of white matter connectivity analyses. Approach: We developed Hierarchical Anatomical Refinement of Pathways (HARP), an advanced modification of ACT. HARP integrates increasingly detailed anatomical priors in a hierarchical fashion to improve accuracy of tractograms. Results: Amount of implausible streamlines in ACT-based tractograms are relatively low but they are systematic. HARP is effective in further reducing false positive connections in tractograms. Impact: Hierarchical Anatomical Refinement of Pathways (HARP) enhances tractography, offering neuroscientists and clinicians a new level of precision in brain connectivity analysis, which could impact our understanding of the brain and its disorders.
Resolving complex white matter architecture in the human brain at 64 mT
Authors: Gholam James , Planchuelo-Gómez Álvaro , Ametepe Joshua , Padormo Francesco , Beltrachini Leandro , Cercignani Mara , Jones Derek
Published: November 2024

Motivation: Facilitating white matter mapping in traditionally inaccessible low income settings. Goal(s): Demonstration of a viable protocol to resolve complex white matter architecture at low field. Approach: A multi-modality protocol was devised to allow feasible acquisition of multi-direction diffusion weighted imaging (DWI) datasets at 64mT. A modified DWI protocol is shown, employing voxel-wise encoding non-uniformity calibration, extended readout, and optimal sampling density. Machine learning based denoising was employed to significantly reduce scan duration. T1 weighted scans were super-resolved to guide tractography. Results: Anatomically-constrained tractography was shown to be viable at low field, and fine white matter microstructure was successfully recovered. Impact: This work demonstrates that a 64mT system is capable of resolving microstructural detail even with lowered SNR, and gradient nonuniformity. This may have relevance in studies examining white matter neurodevelopment in historically underrepresented populations in low-income settings.
Deep Learning-Driven Enhancement of Fibre Orientation Distribution: Effect of Choice of Gradient Direction Number
Authors: Wang Xinyi , Tang Zihao , Cabezas Mariano , D’Souza Arkiev , Liu Dongnan , Barnett Michael , Calamante Fernando , Wang Chenyu , Cai Weidong
Published: November 2024

Motivation: Learning-based methods effectively enhance fibre orientation distributions (FODs) derived from limited single-shell acquisitions. However, the enhancement capacity with different number of gradient directions is not fully characterised. Goal(s): This study aims to explore the impact of initial gradient directions on FOD enhancement capacity of clinically accessible single-shell diffusion data. Approach: We employ a FOD enhancement framework on single-shell (b=1000) data with different numbers of gradient directions. The enhanced FODs and derivatives are evaluated through FOD-based, fixel-based and connectome analysis metrics. Results: The optimal trade-off between the learning-based FOD enhancement outcome and the choice of number of gradient directions is at around 24 directions. Impact: This work provides guidelines for the optimal design of dMRI acquisition protocols meeting the expectations of clinical research on FOD enhancement according to the capability of learning-based frameworks, ensuring high-quality tractography and connectomes without the need for multi-shell HARDI protocols.
Visualization of fine white matter bundles in the living human brain with diffusion MRI using 500 mT/m gradient strength
Authors: Maffei Chiara , Ma Yixin , Ramos-Llordén Gabriel , Mahmutovic Mirsad , Keil Boris , Yendiki Anastasia , Lee Hong-Hsi , Huang Susie
Published: November 2024

Motivation: There is a pressing need to move beyond the brain major white matter fasciculi in our understanding and characterization of human connectional neuroanatomy. Goal(s): To perform initial in vivo high-resolution diffusion MRI on the next-generation Connectome 2.0 scanner equipped with 500 mT/m gradient strength. Approach: We acquired 1-mm isotropic dMRI in one healthy subject on Connectome 2.0 and evaluated its capabilities in characterizing fine fiber bundles in comparison to Connectome 1.0. Results: The SNR boost enabled by the Connectome 2.0 stronger gradient system allows resolution of fine white matter structures in deep brain regions and near the gray-white interface. Impact: The ability to characterize fine white matter circuitry in the living human brain within reasonable scan times opens new possibilities for investigating their role in psychiatric and neurological disorders and enables the application of clinical interventions that target these pathways.
Characterization of Radiation Response of Muscular Tissue of the Head and Neck using Quantitative Magnetic Resonance Relaxometry
Authors: Mulder Samuel , Salzillo Travis , McCullum Lucas , McDonald Brigid , West Natalie , Kaffey Zaphanlene , Naser Mohamed , Mohamed Abdallah , Ding Yao , Wang Jihong , He Renjie , Hutcheson Kate , Fuller Clifton
Published: November 2024

Motivation: Due to limited options for early monitoring and interventions, radiation therapy (RT) for head and neck cancer patients often leads to dysphagia following treatment due to fibrosis development in swallowing muscles. Goal(s): The goal of this work is to report findings from a retrospective cohort who received RT treatment and longitudinal T1/T2 map imaging. Approach: Evaluating at pre-, mid-, post-, and late- RT allows for statistical analysis of changes in median T1/T2 values. Results: Our hypothesis was confirmed that kinetics are discernable for T1 only, but limited correlation to dose and objective measures of muscle damage was seen in various structures. Impact: At present, there exists no option for the early monitoring of injury from radiation therapy of swallowing muscles related to dysphagia. The aim of this work is to investigate Quantitative MR Relaxometry as a method to monitor for iatrogenic injury.
Ultrafast simultaneous T2 and T2* mapping in 150 ms using non-Cartesian single-shot SPEN MRI
Authors: Jin Junxian , Chen Lin , Chen Zhong
Published: November 2024

Motivation: T2 and T2* mapping can quantitatively characterize tissue pathologies, improving diagnosis and treatment, but their clinical applications are hindered by relatively long scan times. Goal(s): To develop an ultrafast method for simultaneously obtaining T2 and T2* mapping. Approach: A biaxial spatiotemporally encoded (SPEN) sequence with multi-spin-echo trains and spiral out-in-out-in trajectory was developed to obtain multiple images with different echo times within a single shot. The acquired signal was fitted to yield simultaneous T2 and T2* mapping. Results: Numerical simulations and in vivo rat brain and kidney experiments were conducted to validate the proposed method. Impact: We developed an ultrafast technique to simultaneously obtain T2 and T2* mapping in 150 ms, potentially facilitating the use of T2 and T2* mapping in scenarios requiring high time resolution.
Discrepancies between Magnetic Resonance Fingerprinting and other T1 and T2 mapping methods are microstructure dependent
Authors: Kukran Simran , Smith Joely , Statton Ben , Dixon Luke , Thust Stefanie , Dragonu Iulius , Cardona Sarah , Finnegan Mary , Quest Rebecca , Bangerter Neal , Koh Dow Mu , Lally Peter , Orton Matthew , Sollars Matthew Grech
Published: November 2024

Motivation: T1 and T2 accuracy in the brain is difficult to assess, since there is no ground truth available. Goal(s): To investigate how well relaxometry methods agree. Approach: We compare Magnetic Resonance Fingerprinting (MRF) T1 and T2 mapping with Variable Flip Angle (VFA) T1 mapping and Multi-Echo Spin Echo (T2) mapping in 11 anatomical brain regions for 10 healthy volunteers, and in the relevant spheres of the NIST phantom. Results: MRF underestimates T1 and T2 in comparison with T1 VFA and T2 MESE in the human brain, especially in myelin-dense areas. Less T1 and no T2 bias is present in the NIST phantom. Impact: Quantitative T1 and T2 relaxometry techniques are more consistent in the NIST phantom than the human brain. Deviations could be caused by magnetisation transfer, whose impact on T1 and T2 relaxation mechanisms needs further investigation.
Feasiblity of Driven Equilibrium Single Pulse Observationof T1 (DESPOT1) at Ultra-Low Field
Authors: Dean Douglas , Guerrero-Gonzalez Jose , Weaver Jayse , DiPiero Marissa , Ragunathan Sudarshan , Ljungberg Emil , Padormo Francesco , Deoni Sean
Published: November 2024

Motivation: Quantitative magnetic resonance imaging can provide novel insights into the brain’s tissue microstructure, however, such methods have limited availability at ultra-low field. Goal(s): To develop DESPOT1 approach at ultra-low field. Approach: We acquired spoiled gradient recalled echo images across multiple flip angles at low field (64 mT) and fit the signal to estimate T1 using the DESPOT1 framework. Results: While challenged by limited SNR at low field, our results demonstrate the feasibility to measure T1 at low field from multiple flip angle images. Through additional optimization, such methods may allow low field systems to provide sensitive measures of brain tissue microstructure. Impact: Our results demonstrate the ability to perform T1 relaxation time mapping via DESPOT1 at ultra-low field for the first time. Improvements in the described approach could enable sensitive measurements of brain microstructure at ultra-low field.
Arterial Spin Labeling and Phase-Contrast MRI suggest impaired cerebral blood flow autoregulation in cerebral small vessel disease
Authors: Verstappen Damon , de Jong Joost , van Dinther Maud , van Schie Amaia Alvarez , van Oostenbrugge Robert , Staals Julie , Jansen Jacobus , Backes Walter , Voorter Paulien
Published: November 2024

Motivation: Largely unidentified pathophysiological mechanisms in cerebral small vessel disease (cSVD) hamper treatment development. One of the potentially affected functions is cerebral blood flow (CBF) autoregulation. Goal(s): Establish whether brain tissue perfusion is (in)dependent of macrovascular blood supply in cSVD patients. Approach: The relationship between internal carotid artery blood flow (blood supply), measured with phase-contrast MRI, and gray matter CBF (CBFGM), measured with arterial spin labeling, was determined separately for cSVD patients (n=41) and controls (n=18). Results: A significant, positive relationship between CBFGM and blood supply was found in cSVD patients, but not in controls, suggesting impaired autoregulation in cSVD. Impact: We revealed a positive relationship between internal carotid artery blood supply and the cerebral blood flow in gray matter in cerebral small vessel disease patients, but not in controls, which could indicate compromised autoregulatory capacity.
The Association of Aneurysm Wall Enhancement with Hemodynamic Relationships Between the Aneurysm Neck and Parent Artery
Authors: Li Yuxin , Zou Xia , Liu Xinzi , Liang Zhuofeng , Lin Anyi , Xue Jingyue , Xu Yongzhou , Wen Zhibo
Published: November 2024

Motivation: To investigate the relationship between the enhancement of intracranial cystic aneurysm walls and aneurysm morphology and hemodynamics. Goal(s): The study aimed to identify new imaging indicators for intracranial aneurysm enhancement. Approach: We conducted a retrospective collection of magnetic resonance images of aneurysm cases and grouped them based on aneurysm wall enhancement grades. Then the differences in morphological and hemodynamic parameters between groups were compared. Results: The study found that the maximum flow rate at the aneurysm neck and the ratio of the aneurysm neck to the maximum flow rate of the parent artery were statistically significant. Impact: This study investigated the hemodynamic relationship between aneurysms and the parent artery, and its potential impact on aneurysm wall enhancement. These results could offer valuable insights into the connection between aneurysm wall enhancement and hemodynamics.
MRI dynamic changes and mechanism of glymphatic system of hypertensive cerebral edema
Authors: Xia Yong , Jiang Yuanpeng , Qu Chunrong , Cheng Zhen , Gao Bo , Cheng Yongjun
Published: November 2024

Motivation: The pathogenesis of hypertensive cerebral edema remains elusiveGoal(s): To visualize and evaluate the transport function of the glymphatic system in the spontaneously hypertensive rats (SHR). Approach: We used the dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and immunofluorescence analysisResults: And we found that there was a significant change in the transport function of the glymphatic system and a decrease in the expression of aquaporin-4 (AQP4) around blood vessels. Impact: The combination of DCE-MRI and immunofluorescenc analysis can facilitate further exploration of mechanisms underlying brain edema in the spontaneously hypertensive animal models, thus aiding in the study of the glymphatic system.
Quantitative Hemodynamic Measurements of the Posterior Circulation in Moyamoya Disease Patients using Arterial Spin Labeling Dynamic MRA
Authors: Li Hongwei , Shao Xingfeng , Solcia Gustavo , Wang Sihui , Suzuki Yuriko , Wang Danny , Wang He , Chen Zhensen
Published: November 2024

Motivation: Hemodynamic information in the posterior circulation of Moyamoya disease (MMD) patient is crucial and may hint collateral flow. Goal(s): To assess feasibility of hemodynamic quantification for the posterior circulation of MMD patients based on ASL dynamic MRA. Approach: The model-free aBF quantification method based on ASL dynamic MRA, CFD simulation and 3D phase contrast (PC) were compared in the posterior circulation and the circle of Willis. Results: As compared to CFD, aBF demonstrated better an agreement with PC results and had the potential for quantifying distal vessels. However, its accuracy may be influenced by the choice of AIF. Impact: This study provides preliminary evidence regarding the feasibility of using ASL dynamic MRA to quantify the flow hemodynamics in Moyamoya disease patients.
Cerebrovascular Flow and White Matter Microstructural Integrity in the Presence of Amyloid and Tau Biomarkers
Authors: Spahic Alma , Roberts Grant , Peret Anthony , Rivera-Rivera Leonardo , Dean Douglas , Johnson Kevin , Eisenmenger Laura , Wieben Oliver
Published: November 2024

Motivation: This study is driven by the need to understand the intricate relationship between cerebrovascular disease (CVD) and Alzheimer's disease (AD) pathology. Goal(s): In this study, we aim to investigate the correlations between CVD markers, AD biomarkers and white matter (WM) microstructure. Approach: To achieve these goals, we used 4D flow MRI and neurite orientation dispersion and density imaging (NODDI) and utilized statistical models to examine the relationships between vascular flow and WM neurite density index. Results: Our results identified significant correlations between AD biomarkers, WM integrity and cerebrovascular flow in specific vessels. Impact: Our findings motivate further investigations into the intricate relationship between cerebrovascular disease and Alzheimer's disease (AD) pathology. Better understanding of this relationship may improve early AD detection and therapeutic strategies.
Simulating pulsatile flow: towards understanding MRI sequences targeting microvascular fluid-structure interactions
Authors: Rundfeldt Hans Christian , Báez-Yáñez Mario Gilberto , Zwanenburg Jaco , Petridou Natalia
Published: November 2024

Motivation: MRI is increasingly used to investigate vascular pathology in neurodegenerative disease. The origin of the MRI signal, however, often remains elusive. This study models pulsatile blood flow aiming to characterize vascular pulsation’s impact on MRI signal formation in advanced sequences. Goal(s): To create a realistic model of pulsatile blood flow in microvasculature. Approach: Static and pulsatile flow are simulated in a simplified and a more realistic synthetically generated vessel model using 1D approximations of governing equations. Results: Pulsatile flow simulations in simplified networks with realistic inflow wave forms yield realistic deformation characteristics. Realistic pressure and flow distributions are achieved in extensive microvascular networks. Impact: By providing realistic voxel-scale vascular simulation models capable of quantifying physiological parameters such as CBV and vessel deformation due to pulsatility, contribution of microvascular pulsations to MRI signal and cerebral waste clearance may be explored in the future.
Comparison of Time Efficiency Between CT and MRI Multimodal Scan for Patients with Acute Ischemic Stroke in the Late Window
Authors: Jia Rongrong , Gao Yanjun , Bai Weixian , Lv Hairong , Wei Xiaocheng
Published: November 2024

Motivation: The matter of time efficiency for both CT and MRI remains a topic of ongoing debate. Goal(s): To provide an objective comparison of time efficiency between CT multimodal and MRI multimodal scans in patients with acute ischemic stroke in the late time window. Approach: Efficiency in terms of time was compared between AIS patients who underwent CT scans and those who underwent MRI scans. Results: The evaluation time for the MRI multimodal scan was found to be shorter than that of CT. MRI scans were recommended for use during the night shift. Furthermore, more experienced technicians had shorter scanning times. Impact: Implementing this approach can streamline the scanning procedure for radiology departments when dealing with acute stroke patients, aid in the training of technicians, and recommend a preference for MRI scans during nighttime shifts.
Amygdala Subregion Atrophy: Early Indicator of Cognitive Impairment in Cerebral Small Vessel Disease
Authors: Cheng Zhenyu , Li Meng , Li Jing , Chen Yiwen , Liang Pengcheng , Wang Na , Zhang Xinyue , Liang Changhu , Li Xianglin , Guo Lingfei
Published: November 2024

Motivation: The motivation behind this study was to delve deeper into the amygdala subregion changes in Cerebral Small Vessel Disease (CSVD) and how these changes correlate with cognitive impairment. Goal(s): The primary goal was to determine whether specific amygdala subregions could serve as early indicator for cognitive impairment in CSVD, thereby aiding in early diagnosis and intervention. Approach: Combined 3T MRI neuroimaging with cognitive assessments and focused statistical evaluation of amygdala subregionsResults: Our study revealed significant volume reductions in specific amygdala subregions among CSVD group, with pronounced atrophy observed in the left cortical nucleus. Impact: This research introduces a novel indicator that utilizes neuroimaging techniques for the early prediction of CSVD progression and associated cognitive impairment, which could significantly enhance the precision of diagnostics and inform effective management strategies for CSVD.
LSA branches in symptomatic and asymptomatic unilateral MCA stenosis patients at 7T
Authors: Li Runze , Lv Jinhao , Duan Qi , Qu Jianxun , Li Chenxi , Duan Caohui , Bian Xiangbing , Lou Xin
Published: November 2024

Motivation: The annual stroke risk of symptomatic middle cerebral artery (MCA) stenosis patients is more than four times that of asymptomatic patients, and doctors need to identify these two types for personalized treatmentGoal(s): We aim to investigate whether there is a difference in the number of stems and branches of the Lenticulostriate Artery (LSA) between symptomatic and asymptomatic unilateral MCAstenosis patients at 7TApproach: Count the number of LSA stems and branches in the patient's cerebral hemisphere using 7T TOF MRAResults: :We found that the number of LSA branches varies between symptomatic and asymptomatic patientsImpact: LSA may serve as an imaging indicator for early diagnosis and personalized treatment of different types of MCA stenosis patients.
Evaluation of altered vascular function in patients with unilateral middle cerebral artery stenosis at 7T-MRI
Authors: Wang Xinyu , Lyu Jinhao , Qu Jianxun , Lou Xin
Published: November 2024

Motivation: It is important to understand the mechanisms underlying middle cerebral artery stenosis and detect altered vascular function prior to the alteration in brain parenchyma. 7T MRI, benefited from much increased signal-to-noise ratioGoal(s): Evaluate vascular function in the affected and healthy middle cerebral arteries of patients with unilateral middle cerebral arterial stenosis. Approach: 7T-MRI to assess blood flow velocities and pulsatility indices (PI). Results: Flow velocity and PI in the middle cerebral artery were lower on the affected side than on the healthy side. These markers will facilitate mechanistic and interventional studies of middle cerebral artery stenosis. Impact: Flow velocity and PI were lower on the affected side than on the healthy side in patients with middle cerebral artery stenosis. These findings may improve the understanding of middle cerebral artery stenosis pathogenesis.
Reshaping of the lenticulostriate artery in middle cerebral artery occlusion
Authors: Lyu Jinhao , Duan Qi , Li Zhixin , Bian Xiangbing , Qu Jianxun , Zhang Zihao , Lou Xin
Published: November 2024

Motivation: Territory supplied by lenticulostriate arteries includes deep subcortical grey matter nucleus which pose vast influence on neurological function. Goal(s): To elaborate the modification of lenticulostriate arteries when middle cerebral artery occluded. Approach: Patients with MCA occlusion were performed 7T time-of-flight MR angiography with voxel size of isotropic 0.3mm. Results: Origination of LAS were modified to originated from the proximal end of occluded segment in 64.3%. While their morphometrical features were identical with the normal contralateral side LSA. Impact: The finding may be helpful for the protection of LSA in endovascular procedure, and provide critical knowledge about the angiogenesis of middle cerebral artery. Future large sample size 7T MRI study is warranted.
Experimental catheter validation of stenotic pressure drops with vWERP using 4D Flow MRI
Authors: Ahmar Ali , Winter Patrick , Grundmann Sven , Romig Swantje , Bruschewski Martin , Marlevi David , Schnell Susanne
Published: November 2024

Motivation: 4D flow MRI is a promising diagnosis tool for studies on intracranial atherosclerosis. Since pressure drops across stenoses could be used for diagnosis, a robust non-invasive technique for regional pressure variations is of great value. Goal(s): To validate stenotic pressure drops estimated using Virtual Work Energy Relative Pressure (vWERP) while taking into account spatial resolution dependency and turbulence. Approach: Relative pressure was estimated at multiple locations in two different stenoses using vWERP and compared with gold-standad catheter measurements. Additionally, tubulent kinetic energy (TKE) was determined. Results: vWERP provided robust estimates of relative pressure and demonstrated excellent agreement with catheter measurements despite high TKE. Impact: This abstract demonstrates a versatile setup to estimate relative pressure in stenoses at pulsatile flow conditions. The influence of spatial resolution and turbulences were investigated. The results may help to implement non-invasive pressure estimations to the clinical routine.
Correlation between imaging features of lenticulostriate artery and cognitive impairment in patients with cerebral small vessel disease
Authors: zhang yukun , miao yanwei
Published: November 2024

Motivation: The association between cognitive impairment and lenticulostriate arteries (LSAs) in patients with CSVD remains uncertain. Goal(s): This study aims to investigate the relationship between the imaging characteristics of lenticulostriate arteries (LSAs) and cognitive function in patients with sporadic CSVD. Approach: Forty-seven CSVD patients and 28 healthy people were prospectively recruited. According to the MoCA score and years of education, they were divided into the Non-VCI group and the VCI group . Results: The decrease in the number of LSA was an independent risk factor for VCI in CSVD patients . The number and total length of LSA were significantly correlated with MMSE and MoCA score. Impact: LSAs is associated with overall cognitive dysfunction in CSVD patients, and the number of LSAS is an independent risk factor for VCI in CSVD patients.
Associations between small vessel function and progressive white matter injury in CADASIL using advanced 7T MRI
Authors: Pham Stanley , van den Brink Hilde , Vlegels Naomi , Kopczak Anna , Gesierich Benno , de Luca Alberto , Dieleman Nikki , Zwanenburg Jaco , Duering Marco , Biessels Geert Jan , Siero Jeroen
Published: November 2024

Motivation: Impaired blood flow velocity and pulsatility on 7T MRI are associated with white matter injury in cerebral small vessel disease (cSVD) cross-sectionally, but it is unclear whether it relates to injury progression. Goal(s): To assess if small vessel dysfunction is associated with markers of progressive white matter injury in monogenic cSVD. Approach: Small vessel function was measured on 7T MRI in monogenic cSVD. White matter injury (white matter hyperintensities, peak-skeletonized mean diffusivity) was measured with 3T MRI on baseline and two-year follow-up. Results: There were no associations between small vessel dysfunction and white matter injury progression in monogenic cSVD. Impact: Whole-brain white matter injury progression cannot be explained by small vessel function. Local white matter injury progression might be linked with small vessel function and should be assessed in the future with voxelwise studies.
Cartilage and Meniscus Segmentation for Knee MRIs: 2D, 3D and Foundational Models
Authors: Nunes Bruno Astuto Arouche , Zhang Xuzhe , Gomez Laura Carretero , Sundaran Deepthi , Dholakia Jignesh , Sánchez Eugenia , Padrón Mario , Fung Maggie , Soni Ravi , Gopal Avinash , Bhatia Parminder
Published: November 2024

Motivation: Morphometric assessment of cartilage(e.g.,thickness), through MRI yields accurate measurements on the progression of Osteoarthritis(OA). Such quantitative measurements require image segmentation techniques. Recent developments in Visual Foundational Models(VFM) bring opportunities to increasing generality and robustness. Goal(s): What improvements can VFM-based approaches bring to automatic segmentation of knee 3DMRIs, and how it compares to traditional convolution networks(CNNs)?Approach: Trained 2DVFM, 3DCNN, and a modified 3DVFM on 500MRI volumes. Evaluated qualitative and quantitatively on external datasets. Results: The proposed 3D-VFM, demonstrates a slight advantage on quantitative morphological assessment, but strongly outperforms others when qualitatively assessed by radiologists, presenting a promising direction and better generalization. Impact: By leveraging Visual Foundational Models (VFM) in the morphometric assessment of cartilage through 3D MRIs, our research demonstrates significant promise in enhancing the accuracy and generalization of knee segmentation to be applied to osteoarthritis progression measurements.
Uncertainty-Aware Anatomical Brain Parcellation using Diffusion MRI
Authors: Li Chenjun , Wu Ye , Zhang Le , Li Qiannuo , Wang Shuyue , Yao Shun , Cho Kang Ik Kevin , Seitz-Holland Johanna , Ning Lipeng , Legarreta Jon , Rathi Yogesh , Westin Carl-Fredrik , O'Donnell Lauren , Pasternak Ofer , Zhang Fan
Published: November 2024

Motivation: While anatomical brain parcellation has long been performed using anatomical MRI and atlas-based approaches, deep learning methods together with diffusion MRI techniques can improve parcellation performance and interpretation of prediction uncertainty. Goal(s): Our goal is to design an uncertainty-aware deep learning network to utilize multiple diffusion MRI parameters for accurate brain parcellation while enabling voxel-level uncertainty estimation. Approach: We include five evidential deep learning subnetworks and perform an evidence-based ensemble for parcellation prediction and uncertainty estimation. Results: The results demonstrate our method’s superior parcellation performance over several state-of-the-art methods, its promising results in unseen patient scans, and potential applications in brain abnormality detection. Impact: The proposed approach enables improved accuracy in brain parcellation from diffusion MRI, facilitating the understanding of the human brain in health and disease. It may also serve as an effective tool for brain abnormality detection, fostering inquiries into uncertainty-quantified diagnostics.
Uncertainty-guided task-specific multi-parametric MR image fusion for brain tissue segmentation and quantification
Authors: Li Cheng , Huang Weijian , Osman Yousuf , Xiao Taohui , Han Hua , Zheng Hairong , Wang Shanshan
Published: November 2024

Motivation: Existing techniques for multi-parametric MR imaging-based brain tissue segmentation typically employ a generic feature combination strategy without incorporating task-specific guidance, making it challenging to ensure effective fusion. Goal(s): In this work, we aim to develop a task-specific multi-parametric MR image fusion framework to enhance the brain tissue segmentation and quantification accuracy. Approach: During preliminary experiments, we have identified a close correlation between prediction uncertainties and prediction errors. Therefore, we propose an uncertainty-guided task-specific multi-parametric MR image fusion framework to enhance fusion efficiency and decrease prediction uncertainty. Results: Experiments on the iSeg-2019 dataset demonstrate that the proposed method achieves better results than existing techniques. Impact: The outcome of this work has the potential to be utilized in clinical practice to help physicians better monitor brain development and diagnose brain diseases. Meanwhile, the framework can be extended to diverse fields where multi-modal image fusion is required.
Neural Network-based MR Elastography Wave Inversion Using Physics-based Simulations and Uncertainty Quantification
Authors: Bustin Héloïse , Meyer Tom , Jordan Jakob , Reiter Rolf , Walczak Lars , Tzschätzsch Heiko , Sack Ingolf , Hennemuth Anja
Published: November 2024

Motivation: In Magnetic Resonance Elastography (MRE), accurate reconstruction of stiffness maps is essential for medical diagnosis. Traditional inversion techniques are limited by noise, discretization and/or low wavenumbers. Goal(s): We aim to overcome these limitations using a neural network-based wave inversion (ElastoNet) with integrated uncertainty quantification ensuring reliable predictions with high detail resolution. Approach: We trained ElastoNet on simulated wave patches. For inference, we combined all 3 motion encoding directions as input and used evidential deep learning as an uncertainty quantification method. Results: ElastoNet achieves a substantial improvement in detail resolution compared to current neural network approaches and shows promising results in the low-frequency domain. Impact: Our MR elastography neural network-based wave inversion is a promising method for enhanced accuracy and reliability in tissue property characterization. It effectively addresses challenges in reconstruction of stiffness maps, expanding the potential of MR elastography for medical diagnosis.
Instance-level explanations in multiple sclerosis lesion segmentation: a novel localized saliency map
Authors: Spagnolo Federico , Molchanova Nataliia , Schaer Roger , Ocampo-Pineda Mario , Cuadra Meritxell Bach , Melie-Garcia Lester , Granziera Cristina , Andrearczyk Vincent , Depeursinge Adrien
Published: November 2024

Motivation: The use of AI in clinical routine is often jeopardized by its lack of transparency. Explainable methods would help both clinicians and developers to identify model bias and interpret the automatic outputs. Goal(s): We propose an explainable method providing insights into the decision process of an MS lesion segmentation network. Approach: We adapt SmoothGrad to perform instance-level explanations and apply it to a U-Net, whose inputs are FLAIR and MPRAGE from 10 MS patients. Results: Our saliency maps provide local-level information on the network's decisions. Predictions of the U-Net rely predominantly on lesions' voxel intensities in FLAIR and the amount of perilesional volume. Impact: These results cast some light on the decision mechanisms of deep learning networks performing semantic segmentation. The acquired new knowledge can be an important step to facilitate AI integration into clinical practice.
Prospective Quality Metric Assessment of SyntheticCT Images via a Learnable Framework
Authors: Kaushik Sandeep , Cozzini Cristina , Wiesinger Florian , Goud Ponnam Mahendhar , Menze Bjoern , Shanbhag Dattesh
Published: November 2024

Motivation: Prospective quality assessment of synthetic CT images by predicting an accuracy metric. Such a score can be an indication of confidence of model prediction or be used as a feedback for performance of the model. Goal(s): Prediction of mean absolute value of synthetic CT image without a reference CT imageApproach: A deep learning framework which is trained to predict MAE metric of a given image. Results: The proposed QMetNet model learns to predict the MAE metric on unseen data in a reliable manner without a reference image. Impact: This novel framework makes it possible to train models to predict a choice of metrics as suitable for different applications. It could be a potential solution to provide confidence of prediction of a model to ease adoption of AI solutions.
Code-Aware Transformation from NAC PET to AC PET, MRI, or CT Imaging
Authors: Jin Yuxi , Li Qingneng , Zhou Chao , Li Zhihua , Chen Zixiang , Huang Zhenxing , Zhang Na , Zhang Xu , Fan Wei , Yuan Jianmin , He Qiang , Zhang Weiguang , Zheng Hairong , Liang Dong , Hu Zhanli
Published: November 2024

Motivation: In radiation therapy with PET images, CT and MR images are used for precise targeting, but acquiring them is expensive, time-consuming and increases radiation risk. Goal(s): Developing a deep learning model capable of dynamically switching to a specified mode enhances flexibility beyond traditional one-to-one cross-modal conversion methods. Approach: We developed a deep learning model with dynamic modality translation capabilities by the incorporation of switch layers within the decoder module. Results: The evaluations showed that our model excels at converting non-attenuation corrected PET images to attenuation corrected PET, MR, or CT images, making it easier to obtain additional modality images for radiation therapy. Impact: Dynamic conversion from NAC PET to desired modalities like AC PET, CT, or MRI on demand is more efficient, saving on data storage and processing, and offers customized imaging for specific clinical needs, enhancing workflow efficiency.
Evaluation of generative models for synthetic CT images using SINGHA, a new spectrally informed metric
Authors: Ravano Veronica , Elwakil Adham , Yu Thomas , Hilbert Tom , Maréchal Bénédicte , Richiardi Jonas , Thiran Jean-Philippe , Mourad Charbel , Margain Paul , Favre Julien , Kober Tobias , Omoumi Patrick , Sommer Stefan
Published: November 2024

Motivation: Synthetic CT (sCT) based on MRI could improve the characterization of bone pathology by estimating bone mineral density and providing a high level of structural details. However, evaluating the performance of sCT is challenging in both respects. Goal(s): To propose an evaluation framework for sCT that quantifies accuracy both in terms of image intensity and depiction of structural details. Approach: We propose the new frequency-based metric SINGHA that captures the sharpness difference between images. Results: SINGHA was complementary to standard metrics and captured differences in high frequency content, thereby contributing to a more comprehensive evaluation of sCT images. Impact: Using the newly introduced Spectrally-INformed Grading of High-frequency Attributes (SINGHA) in conjunction with standard intensity-based metrics enables to simultaneously evaluate synthetic CT accuracy in terms of bone mineral density and sharpness.
Zero-Dose PET Synthesis for Patients with Glioblastoma by Mixture-of-Experts-Based TransUNet
Authors: Lan Ella
Published: November 2024

Motivation: Through the utilization of MRI images: T1, T1c, ASL, and T2-FLAIR, the PET image can be synthesized without requiring the patients to face radiation exposure. Goal(s): To synthesize high-quality FDG-PET images by multi-contrast MRI, using TransUNet with mixture-of-experts (MoE) in order to ensemble both local and global feature maps. Approach: TransUNet was utilized as the backbone to synthesize PET from multi-contrast MRI. A mixture-of-experts (MoE) was designed to assign a weight map to each layer's feature. Results: Multi-contrast MRIs can be used to synthesize FDG-PET images for GBM cases by the proposed MoE-based TransUNet model, and incorporating MoE in TransUNet yields solid results. Impact: To synthesize high-quality FDG-PET images by multi-contrast MR via deep learning, this zero-dose project can be transformative in today’s modern field of medicine, It would not require the patients to face radiation exposure while improving the accessibility of PET information.
Improved Deep Learning MR Image Enhancement with Synthetic Images
Authors: Zhou Zechen , Chamberlain Ryan
Published: November 2024

Motivation: Deep learning (DL) based image enhancement requires paired data for supervised training. But separately acquired data pairs may encounter spatial mis-alignment that limits the model performance. Goal(s): Incorporate synthetic data into the training set to address the mis-alignment issue and improve the quality and diversity of the training set. Approach: Develop and validate the diffusion based image degrader to synthesize low quality images. Compare the performance of DL models trained with/without synthetic data. Results: DL models trained with synthetic data can achieve similar performance compared to training with acquired pairs. Additional synthetic data can improve DL image enhancement. Impact: Synthetic data allows building more diverse training sets to achieve multi-task DL models. How much faster the DL model can support and whether it can control the quality of output to meet different clinical preferences is worth further investigation.