bluuebunny/crossref_metadata_2025 huggingface dataset:
Feasibility of thin-slice pituitary microadenoma MRI with super-resolution deep learning-constrained compressed sensing reconstruction
Authors: Zhang Meng , Ye Zheng , Lv Xinyang , Zhang Xiaoyong , Xia Chunchao , Li Zhenlin
Published: November 2024

Motivation: The spatial resolution of MRI is still limited to the detection of pituitary microadenomas. CSAISR framework can reduce noise and improve image resolution. Goal(s): To assess the image quality and pituitary microadenoma detection performance of the thin-slice MRI using CSAISR framework. Approach: In this work, 1.5mm-CSAISR, 1.5mm-CSAI, 1.5mm-CS and 3mm-CSAISR images were obtained. These 1.5mm images were evaluated subjectively and objectively, and the detection rate of 1.5mm-CSAISR and 3mm-CSAISR were compared. Results: Combined with subjective and objective evaluation, the image quality of 1.5mm-CSAISR images was the best. Meanwhile, the detection rate of 1.5mm-CSAISR reached 92.5%, which was significantly better than that of 3mm-CSAISR. Impact: The results suggest that thin-slice MRI combined with CSAISR framework can balance the relationship between noise reduction and spatial resolution improvement, increase the detection rate of pituitary microadenoma and is meaningful for the diagnosis, follow-up and localization of this disease.
CECNN-B1: Confidence-Enhanced CNN in B1 inhomogeneity Correction for Quantitative CEST MRI at 5 T
Authors: ZHANG Ruifen , WU Qiting , XIE Jiahui , WU Yin
Published: November 2024

Motivation: B1-correction is important in CEST MRI. Conventional correction methods require multiple B1 acquisitions, making them less practical in clinical adoptions. Goal(s): This study proposed a CNN-based model that can correct B1 inhomogeneity from a single B1 acquisition. Approach: A CECNN-B1 model incorporating B1 distribution was designed to enhance the confidence of CEST quantification, and its performance was evaluated on a creatine phantom at 5 T. Results: Substantial variation of B1 distribution was observed, resulting in inhomogeneous CEST map. After correction, the B1-induced artifact was effectively alleviated. The image quality of the corrected CEST map was superior to that calculated with conventional interpolation method. Impact: The proposed CECNN-B1 model enabled B1 inhomogeneity correction from single B1 acquisition. A creatine phantom study showed its superiority over the conventional interpolation methods requiring multiple B1 acquisitions, providing an efficient way for improved CEST MRI on high-field scanners.
Enhancing Ultra-Low-Dose PET/MRI Using Deep Learning Method for Improved Interpretation
Authors: Masood Anum , Drzezga Alexander , Elmenhorst Eva-Maria , Foerges Anna , Lange Denise , Hennecke Eva , Baur Diego , Kroll Tina , Neumaier Bernd , Aeschbach Daniel , Bauer Andreas , Landolt Hans-Peter , Elmenhorst David , Beer Simone
Published: November 2024

Motivation: We developed a deep learning model to enhance the image quality of ultra-low dose brain PET. Goal(s): Significantly reducing the injected dose not only minimizes radiation risk in subjects but also provides options for scanning protocols, and more follow-up studies. Approach: We proposed a 3D-Residual Attention U-Net model initially trained on whole-body [18F]FDG PET/MR images. We used transfer learning approach to fine-tune our proposed model on [18F]CPFPX PET/MRI inhouse dataset. Results: We achieved improved metrics compared to U-Net model with average PSNR of 28.02 (U-Net: 21.23), SSIM of 0.81 (U-Net: 0.53), CNR of 0.72 (U-Net: 0.61) and NMSE of 0.33 (U-Net: 0.67). Impact: Our model has potential to generate high-quality PET images from low-dose PET/MR, potentially contribute to implementation of kinetic modelling using PET/MR imaging. Our model is capable of enhancing both whole-body and brain datasets, making it valuable asset for diverse applications.
Deep Learning-Based Multistep Deformable Medical Image Registration for Multimodal Minimal-Invasive Image-Guided Intervention
Authors: Strittmatter Anika , Schad Lothar , Zöllner Frank
Published: November 2024

Motivation: We developed neural networks for deformable medical image registration using multiple steps and resolutions. Goal(s): To investigate how multiresolution networks impact registration results compared to monostep-monoresolution networks. Approach: The networks were trained unsupervised with Mutual Information and Gradient L2 loss. We compared them with a monoresolution-monostep network and the classical registration method SimpleElastix. We evaluated the multistep networks using a three-dimensional liver dataset with CT and T1-weighted MR scans. Results: Incorporating multiple steps and resolutions in the neural network yielded registration results with high spatial alignment and medically plausible transformations (minimal image folding) and fast registration times of less than half a second. Impact: Since the inclusion of multiple steps and resolutions within the neural network leads to improved registration results, multistep registration methods should be used whenever possible. Consequently, more work should be invested in developing multistep-multiresolution networks for multimodal medical image registration.
AA-VoxelMorph: A Weakly-supervised Learning Model for PET-MRI Registration via Adaptive Attention
Authors: Zhang Shaoze , Liu Yiwei , Wei Xingyue , Wang Rui , Liang Ziwei , Luo Jianwen , He Zuo-Xiang
Published: November 2024

Motivation: Registered PET-MRI is better than single modality in diagnoses, and traditional algorithms are time-consuming and perform poorly in cross-modal registration. Goal(s): Improve registration efficiency and reduce registration time by improving traditional deep learning networks. Approach: We propose a weakly-supervised PET-MRI registration network based on a hybrid adaptive attention mechanism. Masks extracted from the fine-tuned large model is uesd to constrain the network. Results: We validate the proposed method on liver PET-MRI images. The experimental results show that the proposed method achieves a higher DICE value and shorter registration time than the other state-of-the-art registration algorithms. Impact: Our proposed new network can help doctors to complete the registration between PET and MRI and diagnose a disease in a short period of time.
Performance Metric for Assessment of Reconstructed Magnetic Resonance Image Phase
Authors: Dubljevic Natalia , Moore Stephen , Lauzon Michel , Souza Roberto , Frayne Richard
Published: November 2024

Motivation: Many deep learning (DL) reconstruction models do not assess the reconstructed phase despite its importance in certain imaging techniques. Goal(s): To develop a phase-specific metric and demonstrate its suitability for reconstruction assessment. Approach: We used our developed metric to assess and analyze DL and non-DL reconstruction results in an experiment investigating the effect of coil overlap on DL reconstruction methods. The phase metric results were compared to magnitude metric results. Results: The phase metric results were consistent with the magnitude metric results and provided useful insights into model performance. Impact: We propose and test a phase-specific metric that can be used to assess and further the development of complex-valued DL reconstruction methods. This metric would allow for DL reconstruction methods to be applied to MR imaging techniques such as phase contrast imaging.
Intracranial Vascular Segmentation in TOF-MRA Images Using Transfer Learning
Authors: Wu Yaping , Zheng Yijia , Lv Jiahui , Zheng Chao , Wang Meiyun , Ma Chune , Mao Xinsheng
Published: November 2024

Motivation: Addressing the challenge of segmenting cerebral vessels in TOF-MRA images, we explored transfer learning to overcome the need for large, annotated datasets. Goal(s): Assess the feasibility of using a refined CTA-based 3D CNN model for MRA vascular segmentation with a limited dataset. Approach: Implemented transfer learning on a ResU-Net3 model, initially trained on CTA scans, fine-tuned with a small MRA dataset. Results: Post-transfer learning, the model's DSC improved dramatically, indicating effective MRA vessel segmentation with limited data. Impact: This study benefits radiologists by streamlining the segmentation of cerebral vessels in MRA, reducing the workload associated with annotation. The method has the potential to be integrated into clinical workflows, enhancing the efficacy of vascular reconstruction in clinical settings.
Applying Deep Learning to Sodium MRI Reconstruction Using Anatomically-Guided Neural Networks
Authors: Kan Isaac , Schramm Georg , Qian Yongxian , Alivar Alaleh , Lui Yvonne , Boada Fernando
Published: November 2024

Motivation: Sodium Magnetic Resonance Imaging (23Na MRI) provides unique metabolic information but suffers from low signal-to-noise ratio (SNR). Iterative anatomically guided reconstructions (AGR) can improve SNR and resolution but are limited in practice by their long computational times. Goal(s): To address these limitations, we explore the use of neural networks to approximate the AGR sodium MRI reconstruction and reduce computational time. Approach: A U-Net convolutional neural network (CNN) was trained to approximate the AGR iterative reconstruction using data from normal human volunteers. Results: Our results indicate that the neural network implementation achieves comparable image quality while significantly reducing reconstruction time. Impact: The improved SNR accuracy and spatial resolution of the CNN AGR reconstructions make the use of Sodium MRI more feasible within the confines of a clinical examination.
Reliability of Deep Learning-based MR Image Reconstruction for Cortical Segmentation, Thickness and Intracortical Myelin Mapping
Authors: Kim Sang-Young , Kim Eunju , Hwang Jinwoo , Katoch Nitish , Park Chae Jung
Published: November 2024

Motivation: SmartSpeed AI, deep learning-based MR image reconstruction method can be used for scan acceleration, but its clinical applicability for studying brain volumetry and/or cortical myelin has not been investigated. Goal(s): This study was aimed to quantitatively evaluate the reliability for estimates of cortical thickness and myelin estimated from SmartSpeed AI reconstruction. Approach: Segmentation performance was evaluated using Dice coefficient and Hausdorff distance and the reliability of estimation for cortical thickness and myelin was assessed using intraclass correlation coefficient. Results: Comparable segmentation accuracy and reliable estimates of cortical thickness and myelin were obtained from relatively high acceleration factor with SmartSpeed AI reconstruction. Impact: SmartSpeed AI reconstruction enabled accurate cortical segmentation, and the reliable estimation of cortical thickness and intracortical myelin, suggesting the validity of its clinical applicability with reduced scan time.
Optimization of 3D PCASL perfusion SNR efficiency at 7T
Authors: Saib Gael , Patil Sunil , Koretsky Alan , Talagala S. Lalith
Published: November 2024

Motivation: 3D-PCASL is a well-established perfusion MRI technique at 3T. Nonetheless, its effectiveness is hampered by increased RF power deposition at 7T limiting the SNR efficiency and labeling duration. Goal(s): This work investigates the relationship between gray matter perfusion signal and labeling duration aiming to optimize 3D-PCASL SNR efficiency while taking into consideration the increased RF power constraints of 7T MRI. Approach: SNR efficiency was measured at various labeling durations maintaining scan time to ~6 min and SAR to ~90% of first-level mode limit at 7T. Results: Optimal SNR efficiency was obtained at labeling duration of ~3 s. Impact: The result of this work provides optimal labeling duration for 7T 3D PCASL studies with maximum SNR per unit time taking into account first-level mode RF power limits.
Leveraging ASL Perfusion to Unravel Neural Activation in the Olfactory Bulb: A Proof of Concept
Authors: Zhao Sichen Ludwig , Taso Manuel , Tisdall M , Gottfried Jay , Detre John
Published: November 2024

Motivation: The olfactory bulbs (OBs) play a key role in the detection and processing of olfactory information. However, research on human OB function has been limited due to their challenging location for conventional T2*-weighted BOLD fMRI. Goal(s): To explore ASL as an alternative to BOLD for OB functional MRI. Approach: We utilized ASL with a 16-shot Stack-of-Spirals 3D TSE readout to obtain short-TE functional imaging. We quantified blood flow and subsequently assessed neural activation in the OB using a blocked design olfactory paradigm. Results: This study quantifies OB blood flow for the first time and demonstrates neural activation in the OB during odor delivery. Impact: This work highlights ASL's potential for fMRI, especially in challenging areas with high susceptibility and low signal-to-noise ratio, making it a viable option for studying olfactory-related regions where BOLD fMRI faces difficulties.
Renal perfusion quantification using multi delay Pseudo-continuous Arterial Spin Labeling: Comparison of results at 1.5T and 3T field strengths
Authors: Garcia-Ruiz Leyre , Echeverria-Chasco Rebeca , Aramendia-Vidaurreta Veronica , Mora-Gutiérrez Jose María , Garcia-Fernandez Nuria , Vidorreta Marta , Bastarrika Gorka , Fernández-Seara María A.
Published: November 2024

Motivation: Pseudo-continuous Arterial Spin Labeling (PCASL) MRI is a promising technique for assessing renal perfusion. Previous studies have been carried out at 1.5T and 3T field strengths, but a comparison of data acquired at both field strengths has never been performed. Goal(s): The aim of this work was to assess the effects of field strength on the measured perfusion signal and to evaluate the reproducibility of renal PCASL at both fieldsApproach: Healthy volunteers underwent repeated scans on both scanners, spaced a week apart. Results: The results showed excellent reproducibility and minimal differences in RBF, ATT and T1 values between visits and field strengths. Impact: The similarity in the study results across both field strengths underscore the potential for expanding PCASL-based renal perfusion assessment, increasing the technique accessibility, and widening diagnostic capabilities.
Multi-PLD ASL avoids up to 30% CBF underestimation in above half subjects without compromising test-retest reliability compared to single-PLD
Authors: Liu Zixuan , Shou Qinyang , Jann Kay , Wang Danny , Shao Xingfeng
Published: November 2024

Motivation: Long arterial transit time (ATT) may cause underestimation of CBF in single-delay arterial spin labeling (ASL) quantification.Goal(s): Our goal is to evaluate the accuracy and reliability between single- and multi-delay ASL acquisition. Approach: 28 subjects underwent test-retest scans ~1 week apart. Voxel-wise and regional CBF/ATT values were quantified to evaluate the test-retest reliability. ATT values obtained from 5-PLD data were used to estimate quantification errors for CBF estimated from single-delay (PLD=2s) ASL. Results: Overall, single-delay may cause an average of 4% to 30% of CBF underestimation in 56% regions. 5-delay is a solid solution to evaluate ATT and CBF. Impact: While single-delay ASL results in slightly higher test-retest reliability, the underestimation of CBF may compromise the quantification accuracy. Since ATT variation is more common in elderly and patients, multi-delay ASL with model-fitting analysis is expected to outperform single-delay ASL.
Quantitative T2 of Long Post-Labeling-Delay (PLD) ASL Signal as a Reporter of Extravascular Microenvironment
Authors: Wang Zihan , Sankaralayam Dinil , Ganji Sandeep , Hu Zhiyi , Shi Wen , Jiang Dengrong , Lu Hanzhang
Published: November 2024

Motivation: Quantitative T2 values of ASL spins may then inform their local microenvironment. Goal(s): In this study, we aim to conduct a technical development to demonstrate the feasibility of measuring ASL T2 at long post-labeling-delay (PLD), at a time when the spins have fully exited the vasculature. Approach: A protocol comprised of Pseudo-Continuous Arterial Spin Labeling (PCASL) module followed by optimized background suppression pulses and 2D multiple-spin-echo (MSE) EPI readout was used to collect T2-weighted images at different post labeling delay (PLD). Results: We estimated T2 of ASL difference signals at long PLDs and also find age related changes in T2 values. Impact: Our result suggests that ASL spins may be used as a reporting probe to assessment the microvascular environment of the brain in health and diseases.
Arterial deceleration time (ADT) mapping using velocity-selective arterial spin labeling
Authors: Guo Jia
Published: November 2024

Motivation: It is challenging to evaluate the health of vasculature at the arteriole/capillary level. Goal(s): To develop an approach to measuring a physiological parameter, arterial deceleration time (ADT), which may provide useful information at the arteriole/capillary level. Approach: Two sets of arterial transit time (ATT) measurement using VSASL with different Vcut in labeling but the same Vcut in imaging were carried out in one subject to measure ATT and ADT. Results: The preliminary data confirmed the ATT-insensitivity of VSASL and measured an ADT from 4cm/s to 2cm/s of less than 100 ms. Impact: This feasibility study measured a new physiological parameter: arterial deceleration time – the time for the arterial blood to decelerate from V1 to V2 (V1>V2), which may have potential value in evaluating microvascular health and clinical applications.
Reduced B0/B1+ sensitivity in velocity-selective inversion ASL using adiabatic refocusing pulses
Authors: Bolar Divya , Barnes Ryan , Chen Conan , Liu Thomas , Han Fei , Pfeuffer Josef , Wong Eric
Published: November 2024

Motivation: Fourier transform velocity selective inversion (FT-VSI) ASL is sensitive to B0/B1+ inhomogeneities, which can lead to pronounced artifacts in CBF imaging. Goal(s): Reduce B0/B1+ sensitivity and improve performance of FT-VSI by utilizing adiabatic refocusing pulses in lieu of composite refocusing pulses. Approach: Adiabatic hyperbolic secant (sech) pulses with MLEV-8 phase modulation were integrated into the FT-VSI train, replacing the composite pulses. Simulations and phantom acquisitions were performed to evaluate B0/B1+sensitivity and subtraction fidelity. Human CBF data were acquired to compare image quality, tSNR, and artifacts. Results: Adiabatic refocusing markedly improved FT-VSI robustness to B0/B1+ inhomogeneities. CBF maps showed improved tSNR, image quality, and artifact reduction. Impact: A novel VSASL labeling train that uses adiabatic refocusing pulses for velocity selective inversion is introduced and found to dramatically enhance performance by improving accuracy, increasing tSNR, and reducing artifacts in human CBF imaging.
In Vivo Reproducibility of tNOE of Human Brain Using Tx/Rx Coil at 3T
Authors: Benyard Blake , Kumar Dushyant , Wilson Neil , Reddy Ravinder
Published: November 2024

Motivation: Nuclear Overhauser Effect (NOE) is based on dipolar cross relaxation mechanism that enables the indirect detection of aliphatic protons via the water proton signal. This work focuses on determining the reproducibility of the transient-Nuclear-Overhauser-Effect (tNOE) of the healthy human brain at 3 Tesla. Goal(s): To establish reproducibility of tNOE in the brain regions of healthy subjects at 3T. Approach: We scanned three healthy subjects multiple times to determine inter-day reproducibility of tNOE on 3T. Results: The inter-subject coefficient of variations (CoV) of tNOE from GM and WM were 5.12% and 3.97%, respectively. The intra-subject tNOE CoV range for GM and WM was 0.24%-4.0%. Impact: This work will facilitate the use of tNOE at 3T to investigate macromolecular (lipid and proteins) derangements in different diseases with significantly reduced scan time and improved specificity compared to steady state NOE.
Selective imaging of myelin based on phase transition using a 3D adiabatic inversion recovery prepared ultrashort echo time (3D IR-UTE) sequence
Authors: Athertya Jiyo , Malhi Bhavsimran Singh , Jang Hyungseok , Sedaghat Sam , Shin Soo Hyun , Chang Eric , Ma Yajun , Du Jiang
Published: November 2024

Motivation: There is a need for selective myelin imaging with minimal contamination from long-T2 water components. Goal(s): To develop a new contrast mechanism for direct visualization of myelin using a three-dimensional adiabatic inversion recovery prepared ultrashort echo time (3D IR-UTE) sequence. Approach: We employed the long-T2 signal phase transition in 3D dual-echo IR-UTE imaging to find the optimal inversion time (TI) necessary to null water signals for selective myelin imaging in a clinical 3T scanner. Results: Myelin signal could be selectively detected by 3D IR-UTE sequence based on long-T2 phase transition and optimal TI for both ex vivo and in vivo brains. Impact: The 3D IR-UTE sequence allows direct imaging of myelin, which is important for accurate diagnosis and assessment of multiple sclerosis (MS), Alzheimer’s disease (AD), and other neurological diseases.
Imaging the relationship of axon diameter and myelination in macaque and human brain
Authors: Gong Ting , Maffei Chiara , Dann Evan , Lee Hong-Hsi , Lee Hansol , Huang Susie , Haber Suzanne , Yendiki Anastasia
Published: November 2024

Motivation: Axon diameter and myelination are essential for conduction of action potentials and therefore related to brain function. However, the relationships between them in white matter (WM) across different species are not well understood. Goal(s): To investigate the relationship between axon diameter and myelination in human and macaque brain WM. Approach: We estimate axon diameter and myelin water fraction (MWF), and derive fiber g-ratio, using macaque and human brain data acquired on a preclinical scanner. Results: Microstructure parameters exhibit consistent patterns across WM tracts and species. Regions with smaller axons tend to have higher packing density and MWF; fiber g-ratio is relatively stable. Impact: The weak correlations between dMRI measures and MWF suggest they can provide complementary information about fiber morphology. The regional variations of these microstructure measures will be baseline for investigating changes in abnormal tissue conditions such as demyelination and axonal loss.
Disrupted structural brain networks and structural-functional decoupling in adolescent major depressive disorder
Authors: Wu Baolin , Gong Qiyong
Published: November 2024

Motivation: Previous studies have demonstrated brain abnormalities in adolescents with major depressive disorder (MDD); however, how the topological organization of brain networks is affected is still unclear. Goal(s): This study aimed to investigate the white matter (WM) structural topology in adolescent MDD. Approach: The topological metrics of WM structural brain networks were analyzed using graph theory method. Results: Adolescent MDD patients showed disrupted topological organization at the global, rich-club and modular levels, exhibited abnormal nodal centralities in multiple regions, and had a decrease in the coupling between structural and functional connectivity. Impact: Our findings suggest widespread disruption of structural brain networks and structural-functional decoupling in adolescent MDD, which may provide new insights into the neurobiology of adolescent MDD.
Brain Connectivity and Intelligence: A Comprehensive Magnetic Resonance Imaging Analysis Illuminating the Neural Underpinnings
Authors: Guo Sen , Cui Yuxin , Sun Zhe , Kamagata Koji , Uchida Wataru , Kikuta Junko , Takabayashi Kaito , Shimoji Keigo , Chen Hongkai , Mahemuti Zaimire , Zou Rui , Saito Yuya , Tuerxun Rukeye , Wada Akihiko , Aoki Shigeki
Published: November 2024

Motivation: To the best of our knowledge, no study in neuroscience has been conducted based on high intelligence (HI) and low intelligence (LI) groups young healthy adults. Goal(s): This study aims to explore the effect of brain network structures on the intelligence quotient and brain efficiency of healthy adults. Approach: We focused on the white matter’s topological traits, which demonstrate network performance and structural connectivity differences between the HI and LI groups. Results: We found structural differences in connections between specific nodes in the HI and LI groups. Intelligence correlated positively with network efficiency and negatively with path length. Impact: This study innovatively explores the relationship between brain network structures and intelligence in healthy young adults. This study revealed that higher intelligence is associated with efficient brain networks and greater resilience in targeted network disturbances.
A Deep Learning model for segmentation of the core components mediating the Papez circuit in brain 7T-MRI
Authors: Peng Yunsong , Zhen Zhiming , Tian Chong , Wang Rongpin , Liu Chen
Published: November 2024

Motivation: The core components mediating the Papez circuit are important targets or localization indicators for the clinical development of deep brain stimulation (DBS) in treating Alzheimer's disease (AD), refractory epilepsy, and other diseases. Goal(s): Exploring deep learning model for segmentation of the core components mediating the Papez circuit in brain 7T-MRIApproach: Fifty-five healthy volunteers were enrolled, and SegResVAE Net was used to segment core components. Results: The SegResVAE Net exhibited the best Dice scores for the mammillary body, mammillothalamic tract, and the average of all components. Impact: Segmentation of the core components mediating the Papez circuit would be beneficial for treating Alzheimer's disease (AD), refractory epilepsy, and other diseases.
Segmentation of MRI White Matter Hyperintensities for longitudinal studies in CADASIL
Authors: Lebenberg Jessica , Saichi Mohamed , Chabriat Hugues
Published: November 2024

Motivation: In CADASIL, the relationship between white matter hyperintensities (WMH) changes and disease progression remains unknown. Goal(s): To estimate WMH changes, accurate segmentations are mandatory but remain challenging to obtain automatically because of patients MRI variability and heterogeneous contrast between WMH and normal appearing white matter. We aim to decrease correction time required to reach accurate results. Approach: We used an incremental learning approach with a supervised algorithm from baseline segmentation data up to the final follow-up data to improve results and reduce manual correction time. Results: A significant improvement of segmentation sensitivity and a reduction of manual correction time were obtained. Impact: To accurately investigate the WMH progression in CADASIL, we propose an individual-based incremental training approach, with repeated learning of the segmentation algorithm from subsequent corrected data obtained along follow-up, to reduce the final manual correction time as much as possible.
Dysfunction of glymphatic system in recovered COVID-19 patients: A diffusion tensor image analysis along the perivascular space (DTI-ALPS)
Authors: Wu Lin , Zhang Zhi , Dai Jiankun , Li Meng , Zhou Fuqing
Published: November 2024

Motivation: Central nervous related sequelae are often reported in recovered COVID-19 patients. It’s unclear whether recovered patients have glymphatic impairment and clinical correlation. Goal(s): Assessing glymphatic function in recovered patients and the clinical correlation. Approach: 61 recovered patients and 38 healthy controls were enrolled. Glymphatic function was assessed using DTI-ALPS and compared between recovered and HC subjects. Results: The impairment of glymphatic function was observed in recovered patients and was more obvious in older patients. It was correlated with sleep quality and anxiety status in a subgroup of recovered patients. Impact: Our study firstly provided evidence of glymphatic function impairment in recovered COVID-19 patients.
Insights into Neurological Long COVID Syndrome with Quantitative Susceptibility Mapping in Basal Ganglia
Authors: Ying Jia , Huang Chuan , Sekendiz Zennur , Fontana Ashley , Clouston Sean , Kritikos Minos , Luft Benjamin
Published: November 2024

Motivation: The etiology of persistent and enigmatic neurological symptoms in post-acute sequelae of COVID-19 (PASC) remains unclear. Goal(s): To utilize quantitative susceptibility mapping (QSM) to compare iron levels in the basal ganglia of individuals with neurological symptoms of PASC, recovered COVID-19 patients, and uninfected controls, thereby identifying potential biomarkers for the condition. Approach: QSM imaging was performed on three distinct cohorts: PASC, post-COVID non-PASC, and uninfected subjectsResults: Elevated QSM values, indicative of iron accumulation, in the putamen and caudate nucleus were observed in PASC patients. Impact: The study aims to offer a novel imaging perspective on PASC, potentially leading to a better understanding of its neurological aspects and aiding in the development of targeted therapies for affected patients.
MR-guided automatic whole-brain segmentation via deep learning technology based on integrated PET/MRI system
Authors: Li Wenbo , Huang Zhenxing , Wu Yaping , Zhao Wenjie , Yang Yongfeng , Zheng Hairong , Liang Dong , Wang Meiyun , Hu Zhanli
Published: November 2024

Motivation: Segmentation of brain tissues plays a significant role in quantifying and visualizing anatomical structures based on PET/MRI systems. Goal(s): However, most of the current methods are based on unimodal MRI but rarely combine structural and functional dual-modality information. Approach: In this paper, we proposed a dual-modality segmentation framework to achieve automatic and accurate segmentation for the whole brain. Results: The numerical experimental results demonstrate that the proposed method can incorporate multimodal information with the efficient and accurate segmentation performance achieved, allowing for better visualization and quantification results. Impact: We proposed a novel dual-modality whole-brain segmentation method based on PET and MR images that is beniificial to enrich the network features. Additionally, our method has reduced the segmentation time and could be implemented with other multimodal data.
CPU-based real time cardiac MRI segmentation using lightweight neural network and knowledge distillation
Authors: Cui Yijun , Meyer Craig , Feng Xue
Published: November 2024

Motivation: Cardiac MRI plays an important role in diagnosis and prognosis of cardiovascular disease. Ideally, clinicians wants to get real-time segmentation using the existing CPU device but is challenging due to high computation burden of current neural networks. Goal(s): We aim to develop a lightweight network to accelerate cardiac MRI segmentation on a CPU device while maintaining the accuracy. Approach: We used layer-wish knowledge distillation to improve the accuracy of the lightweight network. Results: Our results showed that the accuracy of the lightweight model can satisfy the real-time segmentation on CPU devices and achieve the same level of accuracy as the complex model. Impact: This research provides a way to significantly reduce the running time of neural network on CPU device while maintaining accuracy using knowledge distillation. It facilitates the deployment of neural network in clinical practice by eliminating the need for additional hardware.
Automatic spine station identification from surface coil sensitivity maps of MR imaging using deep learning
Authors: Shao Muhan , Manickam Kavitha , Gui Dawei , Bhushan Chitresh , Shanbhag Dattesh
Published: November 2024

Motivation: In spine scanning with MRI, multiple localizer scans are acquired to manually set the stations. 3D surface coil sensitivity maps, with low-resolution but large FOV, which are acquired as part of the prescan can potentially be used to automatically determine the station boundaries. Goal(s): Utilize the existing information in the MRI scanner to automatically predict the location of spine stations and thereby accelerate the workflow. Approach: Use a deep learning framework to automatically identify the stations of the spine anatomy from the coil sensitivity maps. Results: The deep learning model shows good localization of spine stations with mean centroid errors less than 15mm. Impact: Spine stations can be identified from large FOV, low-resolution surface coil sensitivity maps in MRIs using our deep learning framework, which can be used for fast and automatic spine anatomical planning and imaging.
Acquisition of Ktrans perfusion parameter maps from DCE-MRI using a deep learning approach
Authors: Zeng Daohui , Du Mu , Liu Yubao , Yao Bingyu , Huang Junhui , Yang Long , Li Xuanle , Li Ye , Liang Dong , Liu Xin , Zheng Hairong , Hu Zhanli , Zhang Na
Published: November 2024

Motivation: Breast cancer has become the leading cancer worldwide. Hemodynamic features obtained from breast DCE-MRI perfusion maps can accurately quantify tumor pathophysiology. However, traditional estimation of perfusion parameter maps requires significant computational resources and time. Goal(s): To investigate whether deep learning techniques can synthesize Ktrans perfusion parameter maps from contrast-enhanced MRI. Approach: A pix2pix-based cGAN architecture was proposed to generate breast Ktrans perfusion maps. Results: The Ktrans values of the tumor regions in the synthetic and real Ktrans maps show a strong correlation. Two experienced radiologists could not distinguish between real and synthetic Ktrans maps. Impact: This study presents a novel feasible approach for synthesizing Ktrans perfusion maps, which enables rapid generation of high-quality and low-noise perfusion maps, thereby facilitating more effective application of these maps in clinical practice by physicians.
Automated Deep Learning-based Stiffness Quantification in Magnetic Resonance Elastography of the Liver
Authors: Atamaniuk Vitaliy , Wcisło Mikołaj , Pozaruk Andrii , Hańczyk Łukasz , Obrzut Marzanna , Obrzut Bogdan , Gutkowski Krzysztof , Cholewa Marian
Published: November 2024

Motivation: The assessment of liver MRE exams is time-consuming, as is the reconstruction process performed by the scanner. Goal(s): Our objective was to automate the reconstruction and evaluation of stiffness maps, allowing for the calculation of liver stiffness based solely on MRE data, all accomplished within a matter of seconds. Approach: To achieve this, we developed a U-Net-based model combination that takes both magnitude and phase MRE images as input. This model generates stiffness maps and corresponding ROIs while also estimating stiffness within the ROI. Results: The proposed model successfully and accurately estimated liver stiffness, reducing the entire process to a few seconds. Impact: The proposed model can effectively assess liver stiffness using MRE data, substantially decreasing image reconstruction and analysis time to just a few seconds - a crucial advancement for clinical applications.
Deep Learning-Based Quasi-Automatic Tool for Regional Quantitative 17-Segment Analysis of Myocardial Fibrosis
Authors: Al-Haidri Walid , Levchuk Anatoliy , Zotov Nikita , Fokin Vladimir , Ryzhkov Anton , Efimtsev Alexander , Bendahan David , Brui Ekaterina
Published: November 2024

Motivation: Despite the significance of regional myocardial analysis in clinical practice it's performed manually, which is a time-consuming task. Therefore, automation of myocardium regional analysis is a relevant task. Goal(s): The goal of this work is to develop a tool for myocardium regional quantitative analysis automation Approach: A trained neural network segment myocardium and fibrosis. The segmented myocardium undergoes additional segmentation into 17 segments using mathematical algorithm. The fibrosis volume in each segment is measured. Results: U-Net achieved median DSC 0.75 for fibrosis and 0.85 myocardium. The fibrosis regional detection accuracy of our algorithm 0.71 according to F-score. Our algorithm speed is about 30s/patient. Impact: Our tool allows to speed up and improve the accuracy of myocardium regional analysis.
Advancing Neoadjuvant Chemotherapy Response Prediction through Deep Learning-Enabled Retrospective Quanfication of Pharmacokinetics
Authors: Wu Chaowei , Wang Lixia , Wang Nan , Pandol Stephen , Christodoulou Anthony , Xie Yibin , Li Debiao
Published: November 2024

Motivation: While multiphasic contrast-enhanced MRI has propelled noninvasive pCR prediction in breast cancer, its limited temporal resolution restricts quantitative analysis, affecting generalizability and interpretability. Goal(s): To enhance pCR prediction, we integrated retrospective pharmacokinetic quantification by addressing the temporal resolution limit using deep learning. Approach: We incorporated a novel retrospective pharmacokinetic quantification approach into our pCR prediction model to better capture the tumor microenvironment's pharmacokinetic indicators. Results: Our approach improved predictive accuracy in external test datasets, demonstrating the method's superior performance and broader applicability. Impact: Deep-learning pharmacokinetic quantification enhances the accuracy and applicability of pCR prediction using multiphasic DCE-MRI, offering the potential for precise pre-treatment evaluation that could streamline NAC targeting and minimize initiation delays for breast cancer patients unlikely to respond to standard treatments.
Efficient Multi-modality MRI Fusion Based on Superpixel Method for Semi-supervised Brain Tumor Segmentation
Authors: Deng Yifan , Xiao Sa , Chen Zhen , Wang Cheng , Zhou Xin
Published: November 2024

Motivation: The multi-modal images can provide complementary information to improve automatic MRI segmentation performance. However, most multi-modal methods require long training times due to the complex network structures and the large amounts of multi-modal images involved. Furthermore, obtaining numerous labeled data is time-consuming and laborious. Goal(s): To achieve efficient and high-quality segmentation using only a few labeled data. Approach: We propose an efficient training-free multi-modal fusion strategy based on superpixel method for semi-supervised brain tumor segmentation. Results: The experiments on BraTS18 dataset show that our method results in superior overall performance, and can greatly reduce the time costs of doctors. Impact: The strategy of using superpixel method to accelerate the network training process can assist in the timely diagnosis and treatment of diseases in the clinic, and provides a new idea to simplify multi-modal information fusion.
Prompt guided multi-organ segmentation of the total body
Authors: Wen Meiyuan , Gao Yunlong , Wu Yaping , Huang Zhenxing , Li Wenbo , Zhao Wenjie , Yang Yongfeng , Zheng Hairong , Liang Dong , Wang Meiyun , Hu Zhanli
Published: November 2024

Motivation: Numerous studies have made significant strides in the field of medical image segmentation. However, most studies have focused on specific localized regions rather than addressing the challenge of unified segmentation across the entire human body. Goal(s): To improve the efficiency and accuracy of disease diagnosis and treatment, continuous advancements in multi-organ segmentation brings great advantages. Approach: In this paper, we present a prompt guided multi-organ segmentation model on total-body images, which can be adapted for CT, PET and MRI modalities. Results: Our extensive experiments demonstrate the superior performance of our model in accurately segmenting 21 organs. Impact: Our research leverages the power of prompts to tackle the challenge of multi-organ segmentation. It has potentially wide applications in the fields of CT, MRI and PET, enabling the simultaneous segmentation of multiple organs and images from diverse modalities.
Diffusion-T2 relaxation correlation spectroscopic imaging at 3T versus 5T: in vivo human brain application
Authors: Xu Junqi , Wang Qianfeng , Zhu Jiayu , Hu Junpu , Zhang Xijing , Yuan Jianmin , Li Hao , He Wang
Published: November 2024

Motivation: Motivated by the promise of higher SNR and gradient performance at ultra-high magnetic fields (5T), we conducted in-vivo experiments of diffusion-relaxation correlation spectroscopic imaging (DR-CSI) to tap into its prospects at 5T. Goal(s): This study aims to assess the feasibility and performance of DR-CSI at 5T for brain tissue measurement and to explore its 5T applications compared to 3T. Approach: We have acquired two-dimensional diffusion-relaxation data and resolved the 2D spectra and spatial maps at both field strengths for comparative analysis. Results: Results at 5T showed clearer differentiation of brain tissues and improved sensitivity to deep brain nuclei. Impact: At 5T, diffusion-relaxation correlation spectroscopic imaging excels in estimating deep brain nuclei, promising for future applications in infarcts and neurodegenerative conditions.
DTI Denoising by Directional Filtering of k-space Inspired by the Pattern of Gradient Influence
Authors: Esmaeilzadeh Khashayar , Keyvanfard Farzaneh , Moghaddam Abbas Nasiraei
Published: November 2024

Motivation: DTI suffers from intrinsic low SNR compared to conventional MRI, making denoising crucial. Goal(s): Our study aims to introduce a novel filtration method based on an estimated pattern of areas most directionally affected in the spatial frequency domain. Approach: A pattern was suggested through DTI theory and observations of simulation data. This was then used to propose a filter for noise reduction. The application of this filter was quantitatively and qualitatively evaluated on simulated MR and DTI images, considering added noise. Results: The results showed not only the method’s increased robustness to noise but also a clearer representation of white matter tracts. Impact: By integrating the fundamental concepts of diffusion within the white matter into our filter design, we present a promising approach to denoising DTI data, potentially yielding more reliable and biologically meaningful results, and benefiting researchers investigating brain connectivity.
Exploring the precision of robust modelling methods for diffusion-weighted MRI
Authors: Sairanen Viljami , Andersson Jesper
Published: November 2024

Motivation: Clinical research with infants, subject motion can cause many subjects being excluded from analyses due to large parts of their data is corrupted by outliers. While robust modelling methods can mitigate this problem, how they affect dMRI model estimate precision is not well known. Goal(s): We demonstrate how dMRI model precision can be evaluated with two robust modelling strategies. Approach: We used white-matter simulation to compare multi-tensor model precision between 1) Gaussian Process outlier replacements and ordinary model estimator to 2) robustly weighted model estimation. Results: Model precision estimation is possible with both robust approaches, but outlier replacement can cause inflated precision estimates. Impact: Our aim is to enable larger sample sizes for clinical dMRI research by decreasing the need to exclude subject due to subject motion. Additionally, we provide new robust tools to evaluate the precision of dMRI model estimates.
Pointwise analysis of tract microstructure using geometric deep learning for language performance prediction
Authors: Chen Yuqian , Zekelman Leo , Zhang Chaoyi , Xue Tengfei , Song Yang , Makris Nikos , Rathi Yogesh , Golby Alexandra , Cai Weidong , Zhang Fan , O’Donnell Lauren
Published: November 2024

Motivation: The prediction of cognitive performance scores using diffusion MRI tractography enables the study of relationships between brain structure and function. Goal(s): Our goal is to achieve accurate prediction of cognition and identify critical brain regions for prediction. Approach: We propose a geometric deep-learning framework for language score prediction. It utilizes a point cloud representation of fiber tracts for detailed spatial and microstructure information and incorporates a novel regression loss to utilize the continuity of language scores. Results: Our method outperforms comparison methods with state-of-the-art representations of fiber tracts and identifies predictive language-related brain regions. Impact: Our proposed novel geometric deep learning framework using a point cloud representation of fiber tracts can be applied to various tractography-based prediction tasks to improve performance and provide a probe to explore relationships between brain structure and function.
Assessing the potential of 7T and high-performance gradients for high-resolution R2* mapping in deep gray matter
Authors: Mattern Hendrik , Tung Yi-Hang , Lüsebrink Falk , Speck Oliver
Published: November 2024

Motivation: While conventional 7T systems provide increased SNR, their gradient systems lack the performance to acquire high-resolution GRE data with very fast sampling/many echoes. Goal(s): To leverage a 7T with high-performance gradients to acquire more echoes than on conventional 7T systems and assess the potential for high-resolution R2* mapping. Approach: Multi-echo GRE data with varying number of echoes was acquired at a 7T Terra.X Impulse Edition and 7T Plus. Conventional logarithmic-linear and advanced stretched exponential fits were performed. Results: Sampling more echoes enables fitting advanced models which yield lower R2* standard deviation and root-mean-squared-errors. Impact: Combining 7T with high-performance gradients enables high spatial and temporal resolution multi-echo GRE data. Hence, R2* mapping can be performed with novel multi-parametric fits, enabling new avenues in biophysical modeling as well as signal denoising and decomposition algorithms.
Automatic detection and measurement of WM lesions in MS patients using MR-STAT and a self-supervised bivariate Gaussian probabilistic model
Authors: Schilder Martin , Mandija Stefano , Jacobs Sarah , Kleinloog Jordi , Liu Hanna , van der Heide Oscar , Keil Vera , Vonken Evert-Jan , Dankbaar Jan Willem , Hendrikse Jeroen , van den Berg Cornelis , van der Kolk Anja , Sbrizzi Alessandro
Published: November 2024

Motivation: Radiology workflow around multiple sclerosis (MS) patients is time-consuming. Goal(s): To automatically count and measure individual white matter anomalies in MS patients from a five-minute Magnetic Resonance Spin TomogrAphy in Time-domain (MR-STAT) scan. Approach: We imaged ten healthy volunteers (HV) and six MS patients using a five-minute MR-STAT protocol. Resulting quantitative data from seven HVs was fit to a multivariate Gaussian probabilistic model. The model was tested on three HVs and six MS patients. Results: Automatic anomaly detection was moderately accurate in MS patients. No anomalies were found in HVs. These results underline the potential for a shorter acquisition with automatic outlier detection. Impact: MRI protocols for MS patients are lengthy and the assessing the images is a time-consuming task for the radiologist. We combine a fast (five-minute) MR-STAT relaxometry scan with a data-driven, automatic outlier detection strategy to potentially accelerate the clinical workflow.
Chemical exchange saturation transfer for detecting altered APT in arterial stenosis at 7T
Authors: Wang Xinyu , Lyu Jinhao , Qu Jianxun , Liebig Patrick , Lou Xin
Published: November 2024

Motivation: The incidence of middle cerebral arterial (MCA) stenosis increases yearly, molecular biomarkers for this condition are needed. Amide proton transfer imaging is a potential imaging marker. Goal(s): To investigate the APT changes in MCA stenosis patients. Approach: We quantitatively evaluated both the affected and healthy middle cerebral arterial blood-supplying brain regions in eight patients with unilateral middle cerebral arterial stenosis using CEST-APT at 7T. Results: The CEST values of the affected MCA-supplied brain regions were higher than those of the healthy MCA-supplied brain regions, suggesting metabolic alterations in patients with MCA stenosis. Impact: CEST values for vascularized brain regions can be quantitatively measured using 7T-CEST and can provide new markers for mechanistic and intervention studies in patients with MCA stenosis. This can contribute to a better understanding of the pathogenesis of MCA stenosis.