bluuebunny/crossref_metadata_2025 huggingface dataset:
Rethinking Neurochemical Distributions in White Matter and Grey Matter: Considerations for Improved Magnetic Resonance Spectroscopy Measures
Authors: Leech Samantha , Bell Tiffany , Manske Sarah , Mullins Paul , Harris Ashley
Published: November 2024

Motivation: Current methods to quantify neurochemical concentrations in magnetic resonance spectroscopy (MRS) assume an equal distribution of neurochemicals between white matter (WM) and grey matter (GM), however, the implications of this assumption have not been explored. Goal(s): We aimed to improve the accuracy of MRS concentration measures by replacing assumptions with calculated values. Approach: We compared calculated concentration ratios of six neurochemicals in WM and GM with the assumed 1:1 ratio to determine the impact of assuming a 1:1 ratio on neurochemical concentrations. Results: The 1:1 ratio assumption yielded estimate errors of up to 55%. Impact: Implementation of our calculated white and grey matter neurochemical distributions will yield magnetic resonance spectroscopy concentration measures that are ~30-55% more accurate than conventional methods.
Short-Term Neurochemical Effects of Transcutaneous Vagus Nerve Stimulation using 7T Magnetic Resonance Spectroscopy
Authors: Chen Jessica , Conn Katelyn , Lan Zhou , Ridgewell Caitlin , Liao Huijun , Zhao Wufan , Heaton Kristin , Ritland Bradley , Neumeier William , Lin Alexander
Published: November 2024

Motivation: Transcutaneous auricular vagus nerve stimulation (taVNS) has shown therapeutic efficacy in treating conditions such as epilepsy and depression. Recently, its potential to improve cognitive performance in healthy adults has been explored. Goal(s): However, little is known about the neurochemical processes underlying performance enhancing outcomes. This study assessed immediate and residual effects of taVNS on neurochemical activity in brain regions associated with attention, vigilance, and stress. Approach: Magnetic resonance spectroscopy was used to non-invasively quantify neurochemical responses immediately after and 60 minutes post-stimulation compared with baseline. Results: Increased myo-inositol was observed in the posterior cingulate immediately after taVNS but not sustained for 60 minutes. Impact: Using magnetic resonance spectroscopy allowed for the evaluation of the short-term neurochemical response across multiple brain regions from transcutaneous auricular vagus nerve stimulation, which helps elucidate the mechanisms of the potential therapeutic and performance-improvement effects achieved from stimulation
Non-Water-Suppressed Semi-LASER Localization for MR Spectroscopy of the Human Skeletal Muscle at 3T
Authors: Sarma Manoj , Jani Mahrshi , Rodriguez Yeison , Zhang Bei , Henning Anke
Published: November 2024

Motivation: Due to the dependence on the type and orientation of the skeletal muscle, it is a challenge to perform 1H MRS to characterize the muscle features in this highly organized structure. Goal(s): The aim of this work was to implement a non-water-suppressed semi-LASER (sLASER) sequence to characterize up- and downfield metabolites in human skeletal muscle in vivo at 3T. Approach: This was achieved through optimizing the crusher scheme and phase cycling schemes in sLASER and combining it with metabolite cycled. Results: High quality spectra were obtained from different muscle fibers of 8 healthy volunteers to characterize both up- and downfield metabolites. Impact: We demonstrated the application of metabolite-cycled semi-LASER spectroscopy in human skeletal muscles at 3T. Up- and downfield parts of the spectrum were detected with high quality enabling the detection of important metabolites including carnosine providing unique insight into human physiology.
Weight loss predicts the modulation of dACC glutamate during inhibitory control that is specific to food-cues: Evidence from ¹H fMRS
Authors: Eichstaedt Jillian , Sochocki Rachel , Khatib Dalal , Miller Nicole , Diwadkar Vaibhav , Burghardt Paul , Rothberg Amy , Stanley Jeffrey
Published: November 2024

Motivation: The neurobiological mechanisms involved in poor weight loss maintenance are not understood. Goal(s): Our goal was to investigate neurobiological differences in glutamate modulation across different motor/inhibitory control responses after completion of a clinical weight loss program. Approach: 1H fMRS was conducted in a pilot sample who had completed a clinical weight loss program. Results: The presence of food cues as the stimuli influenced the glutamate modulation during inhibitory control responses. Impact: With further investigation, our preliminary evidence of a food-cue specific difference in glutamate modulation may fuel better treatment plans to support more successful maintenance of weight loss.
Enhancing 2D MRSI: Implementation of CHEmical-shift Adiabatic Pulses (CHEAP) at a 7T Philips platform using Pulseq
Authors: Nam Kyung Min , Roos Thomas , Weng Guodong , Klomp Dennis , Slotboom Johannes , Wijnen Jannie , Bhogal Alex
Published: November 2024

Motivation: Nuisance signal contamination and challenges associated with implementation involving advanced RF pulses and sequence hinder clinical adoption. Simplified sequence implementation and dissemination are crucial for community-driven advancement and vendor support. Goal(s): Our goal is to integrate CHEmical-shift selective Adiabatic refocusing Pulses(CHEAP) on the Philips platform via Pulseq open-source platform, creating advanced MRSI sequences that refine metabolite analysis by minimizing unwanted signals. Approach: Integrating chemically selective adiabatic 2-refocus pulses in Pulseq achieved optimal spectrum coverage, reducing interference from residual water and lipid signals. Results: Implementing the CHEAP sequence significantly mitigated interference from residual water and lipid signals, demonstrating its potential for advancing MRSI. Impact: The implementation of CHEAP sequence via Pulseq promises a standardized, shareable method, fostering collaboration and enabling precise metabolic studies. This advancement in MRS techniques may significantly improve reproducibility across sites and enhance capacity for metabolic profiling in health and disease.
Unlocking Myelin Mysteries: Could 31-Phosphorous Solid-State NMR be the key to quantitative MRI of myelin membrane morphology?
Authors: Knight Cariad-Arianna , Ensworth Alex , Laule Cornelia , MacKay Alex , Michal Carl
Published: November 2024

Motivation: Improved sensitivity to changes in myelin membranes may be achieved by phosphorous (31P) solid-state NMR (ssNMR), enabling more direct evaluation of neurodegeneration. Goal(s): To demonstrate how ssNMR proton cross-polarization (CP) may be more sensitive to membrane morphology than 31P alone. Approach: Using porcine neural tissue, we conducted a series of 31P ssNMR experiments that characterize the myelin phospholipid involvement in 31P-CP and CP’s sensitivity to variations in membrane composition, orientation, and dynamics. Results: The CP signal is highly sensitive to the amount and orientation of myelin between grey and white matter samples across neural regions, and may better detect changes in membrane structure. Impact: Improved sensitivity to subtle variations in myelin membrane morphology using the 31P ssNMR method of CP has the potential for in vivo MRI use, and could lead to earlier diagnosis, as well as enhanced disease and treatment monitoring.
Improving reconstruction of multiband real-time MRI
Authors: Watson Isaac , Zamboni Elisa , McStravick James , Mitchell David , Trefzer Martin , Sebald Angelika , Kennerley Aneurin
Published: November 2024

Motivation: Static sequences cannot capture movement and current real-time MRI sequences cannot provide volumetric coverage. Our motivation is too bridge this gap. Goal(s): This work aims to extend a standard real-time MRI sequence to use multiband excitation to improve real-time volumetric coverage for diagnostic purposes. Approach: Development of a radial sequence incorporating multiband excitation. A compressed sensing reconstruction algorithm is also implemented. Phantom and in-vivo images are acquired and analysed to assess image quality and reconstruction times. Results: We present both phantom and in-vivo images acquired using this approach. The proposed reconstruction approach results in a higher SNR compared to conventional reconstruction. Impact: We present improvements in real-time MRI by adding multiband excitation, allowing for the dynamic imaging of complex movements across a volume. This will improve the monitoring and diagnostic capabilities of real-time MRI, for example in maxillofacial surgery or orthopaedics.
Characterizing laryngeal dynamics during voicing and breathing with real-time multi-slice variational manifold learning
Authors: Rusho Rushdi Z. , Hoffman Matthew , Apfelbach Christopher , Alam Wahidul , Oya Hiroyuki , Howard Matthew , Meyer David , Jacob Mathews , Lingala Sajan
Published: November 2024

Motivation: Visualizing the movement of laryngeal muscles during voicing and breathing is important for understanding the mechanics of speech production. Goal(s): The goal is to improve our understanding of speech production by visualizing gross-vocal fold movements due to laryngeal muscle contractions. Approach: We proposed a multi-slice non-gated spiral sparse sampling strategy, and variational manifold based reconstruction scheme for dynamic laryngeal MR imaging at high spatio-temporal resolution. Results: Our proposed method can capture gross vocal fold motions (e.g., adduction, abduction, elongation, shortening) during various voicing and breathing at both high spatial (1.5 mm2) and high temporal resolutions (36 ms/frame). Impact: Otolaryngologists, voice and neuroscientists interested in larynx physiology and disorders pertaining to breathing and phonation.
MULTI-PHASE SPATIAL RECONSTRUCTION METHOD FOR ACCELERATED DYNAMIC CONTRAST-ENHANCED MRI
Authors: Mertens Alexander , Cheng Hai-Ling
Published: November 2024

Motivation: Dynamic contrast-enhanced (DCE) MRI requires both high spatial and high temporal resolution for accurate quantification and delineation. In practice, temporal resolution is often traded for spatial resolution and volume coverage. Goal(s): To develop a reconstruction method that offers both high spatial and temporal resolution. Approach: We describe a two-stage reconstruction technique that consistently produces high-temporal, high-spatial resolution estimates of the ground truth data and is more accurate than current state-of-the-art methods. Results: The proposed method achieves high quality reconstruction with as few as one acquired spoke per frame when radial sampling is used. Impact: The theoretical and practical success of the spatial subspace method over temporal subspace methods encourages further research on the topic. Furthermore, reconstruction at 1 spoke per frame enables larger imaging volumes, thus more capable imaging tools.
A hybrid PCA acceleration method for rapid real-time 2D target tracking
Authors: Wright Mark , Han Gawon , Yun Jihyun , Yip Eugene , Fallone Gino , Wachowicz Keith
Published: November 2024

Motivation: Most standard MR sequences are too slow for real-time applications. Accelerated acquisitions are one method to achieve the required frame rates. Neural-network reconstruction and parallel imaging are two methods that can achieve the necessary frame rates but are computationally expensive or require extensive coil arrays. Goal(s): To develop an accelerated reconstruction method suitable for real-time applications which is computationally inexpensive and simple to implement. Approach: Our method was tested retrospectively on lung, liver, and prostate patients for image quality and auto-contourability using a range of metrics. Results: Image quality and contourability improved over similar methods while maintaining good reconstruction times for real-time applications. Impact: An accelerated PCA-based reconstruction method was developed suitable for real-time applications, and in particular, target tracking. It has improved image quality and auto-contourability compared to similar methods while still maintaining simplicity in its implementation (low-cost computing, single coil arrays).
High-resolution MR neurography of the wrist with diffusion-sensitized driven-equilibrium with deep learning constrained Compressed SENSE
Authors: Hamatani Yutaka , Nagao Michinobu , Yoneyama Masami , Goto Yasuhiro , Shiina Isao , Morita Yasuyuki , Kodaira Kazuo , Ogawa Takumi , Kato Mana , Shinya Sudo , Sakai Shuji
Published: November 2024

Motivation: The median and ulnar nerves are fine anatomical structures, and these small nerves could not be adequately visualized on conventional MR neurography. Goal(s): The purpose of this study is to realize high-resolution MR neurography for the diagnosis of peripheral neuropathy of the median and ulnar nerves. Approach: We performed visualization of the detailed anatomy of the median and ulnar nerves by high-resolution diffusion sensitized-driven equilibrium (DSDE) with deep learning constrained Compressed SENSE (CS-AI) with deep learning constrained Compressed SENSE (CS-AI). Results: MR neurography with DSDE with CS-AI could clearly visualize the median and ulnar nerves at high resolution within a feasible scan time. Impact: High-resolution MR neurography with DSDE with CS-AI clearly visualizes the entire median and ulnar nerves, including the small common palmar digital nerves. This may be a useful technique in the diagnosis of peripheral neuropathy of the wrist.
Visualizing the Vagus Nerve in Cadavers with High-Resolution MRI
Authors: Herzka Daniel , Nuzov Noa , Sali Goksel , Reimers Jasper , Lunasco Leina , Brunsman Brandon , Markley Michael , Pelot Nicole , Flask Chris , Crofton Andrew , Shoffstal Andrew , Blitz Ari
Published: November 2024

Motivation: Clinical visualization of the vagus nerve is challenging due to nerve diameter and tortuosity, as well as variation between subjects, making implementation of stimulation therapies difficult. Goal(s): Determine feasibility of bilateral vagus visualization in embalmed cadavers using high-resolution imaging. Approach: Thirteen cadavers were imaged at 0.5-0.6 mm isotropic resolution using 3D constructive interference in steady state (CISS) and volumetric interpolated breath-hold sequence (VIBE). Scanning covered the extent of the vagus from the skull base to the abdomen and imaging was correlated to gross dissection. Results: The vagus nerve was visualized bilaterally most clearly with CISS and 0.5mm uninterpolated spatial resolution. Impact: MRI visualization of the vagus, cranial nerve X, could be used to direct future interventions including potential percutaneous MRI-guided procedures. Cadaveric imaging can help optimize acquisitions for future in vivo translation.
Improved Region-Optimized Virtual Coils for Cartesian Acquisition Geometries
Authors: Chan Chin-Cheng , Nguyen Christopher , Haldar Justin
Published: November 2024

Motivation: ROVir (Region-Optimized Virtual coils) is a technique that constructs MRI virtual coils in a way that seeks to simultaneously maximize the amount of information captured by the smallest number of virtual coils (coil compression/dimensionality reduction) while also suppressing signal from undesired spatial regions (avoiding aliasing/leakage artifacts). Although ROVir generally performs well, its performance can sometimes be limited by coil geometry. Goal(s): To improve the performance of ROVir. Approach: We exploit the structure of Cartesian imaging, calculating distinct ROVir weights for each position along the fully-sampled readout. Results: The proposed approach enables substantially better dimensionality reduction and signal suppression performance. Impact: The proposed approach provides substantially better signal suppression and coil compression for Cartesian acquisitions, alleviating burdens on data acquisition (reducing the need for sequence-based signal suppression and enabling reduced-FOV imaging) and reducing the computational complexity of image reconstruction.
MR-blob: Coordinate-Transformed Blobs for Parallel MRI Reconstruction
Authors: Singh Imraj , Kereta Željko , Denker Alexander , Barbano Riccardo , Jin Bangti , Thielemans Kris , Arridge Simon
Published: November 2024

Motivation: Reducing the number of parameters needed to represent and reconstruct parallel MRI measurements. Goal(s): Reconstruct parallel MRI measurements with coordinate-transformed Gaussian functions (blobs) where the forward model is formulated directly. We term this MR-blob. Approach: MR-blob directly represents parallel MRI measurements; where coil sensitivities are modelled as isotropic Gaussians and the image is represented by coordinate-transformed blobs. Results: Noisy, undersampled parallel MRI simulations of Shepp-Logan phantom are reconstructed with a pixelised image, a coordinate-transformed blob-based image, and MR-blob; all with total variation regularisation. Quality measures are shown to be consistent across methods and regularisation strengths. Impact: Parameter-efficient image representations have the potential to reduce computational burden. This work defines parallel MRI forward model for coordinate-transformed blobs. This includes auto-calibrating coil sensitivities that re-scale and translate to fit the parallel MRI measurements.
ECS-induced seizure enhances neurometabolic activity but doesn’t affect astrocytic activity in mouse brain
Authors: Sarawagi Ajay , Patel Anant
Published: November 2024

Motivation: Electroconvulsive shock (ECS) therapy is an established treatment for drug-resistant depression. However, the status of neurometabolic activity during ECS-induced seizure is not clear. Goal(s): This study was carried out to measure neurometabolic activity during ECS-induced Seizure. Approach: 1H-[13C]-NMR spectroscopy was used in conjunction with intravenous [1,6-13C2]glucose or [2-13C]acetate infusion to measure neuronal and astroglial metabolic activity, respectively. Results: The neurometabolic activity was increased while astroglial activity remained unchanged during ECS-induced seizure. Moreover, the anaerobic glucose consumption was highly increased, suggesting the dominance of glycolysis during the seizure state. Impact: The assessment of metabolic activity of Glutamatergic, GABA’ergic neurons, and astrocytes during ECS-induced seizures, provides valuable information about glucose consumption during enhanced energy demand, and the interplay of different cell types in the brain.
Changes in GABA and Glutamate levels with peripheral and central visual stimulation using functional magnetic resonance spectroscopy at 7T
Authors: Saucedo Andres , Guo Fanhua , Pappas Ioannis , Wang Danny
Published: November 2024

Motivation: Changes in GABA and Glutamate associated with positive/negative BOLD activation in the visual cortex due to central and peripheral stimulation of the visual field are measured with functional MRS. Goal(s): How does stimulation of the central visual field affects concentrations of excitatory and inhibitory neurotransmitters and could these changes be consistent with negative and positive BOLD activation? Approach: An fMRS paradigm was applied on four volunteers to measure average changes in GABA and Glu due to alternating periods of central and peripheral visual stimulation, using a non-edited semi-LASER sequence. Results: GABA decreased on average with peripheral stimulation while Glutamate showed an inconsistent trend. Impact: Indirect effects of negative and positive BOLD activation can be explored with functional MRS by measuring changes of GABA (inhibitory) and Glutamate (excitatory) neurotransmitters in the visual cortex resulting from central and peripheral stimulation of the visual field.
Cerebral Artery Segmentation with Limited Data: Using Hierarchical Transformers
Authors: Tian Song , Huang Sicong , Song Zhixuan , Xie Jingyu , Jiang Shanshan , Zhang Tianwei , Zhang Wenjing , Lui Su
Published: November 2024

Motivation: Transformer networks have demonstrated their effectiveness in both large-scale natural language processing and 2D image analysis tasks. However, their potential in 3D medical image analysis, particularly on small training datasets, remains unexplored. Goal(s): Leveraging transformer-based models to attain highly accurate cerebral artery segmentation in 3D TOF-MRA images. Approach: SwinUNETR with a hard example mining loss function to perform cerebral artery segmentation on the public dataset CAS2023. Results: We obtained an average Dice score of 0.844 and 0.889 for the stenosis area, a normalized Hausdorff distance of 0.888 and 0.8444 for the stenosis area, along with a weighted Dice and Hausdorff score of 0.867. Impact: We used fewer than 100 cases to train a transformer model for artery segmentation, indicating that transformers have the potential to replace CNNs in the processing of 3D TOF-MRA medical images, even with a small training dataset.
Multi Complex-valued Spatio-temporal Fusion Networks for Robust MRF Reconstruction
Authors: Ding Tianyi , Gao Yang , Xiong Zhuang , Cloos Martijn , Sun Hongfu
Published: November 2024

Motivation: Motivated by the prevalent use of basic deep learning architectures in MRF image reconstruction, and their heavy reliance on conventional dictionary matching methods for ground truth or paired in vivo acquisitions, we sought to innovate. Goal(s): Our specific intent was to determine whether novel architectures could surpass traditional ones in MRF reconstruction. Approach: To this end, we introduced the MRF-Mixer, blending complex-valued MLP with U-Net, and the more advanced MRF-TransMixer, integrating complex-valued MLP, Transformer, and U-Net. Results: Leveraging our purely simulated training dataset, we methodically assessed their performance, endeavoring to highlight advancements with potential to transform MRF reconstruction in practice. Impact: The MRF-Mixer and MRF-TransMixer offer enhanced MRF image reconstruction, potentially boosting diagnostic accuracy for clinicians. This advancement could lead to safer imaging for patients and motivate researchers to further explore SOTA network architecture applications in MRF.
Deep Learning Classification of Muscular Dystrophy from MR Images using Swin Transformer
Authors: Taccogna Maria , Rizzo Giovanna , D'Angelo Maria , Peruzzo Denis , Mastropietro Alfonso
Published: November 2024

Motivation: This study aims to improve the accuracy of muscular dystrophy (MD) diagnosis by applying AI and multiparametric MRI to distinguish subtypes with similar muscle involvement patterns. Goal(s): The primary goal is to develop a Swin Transformer (SwinT) AI-based classification approach for BMD, LGMD2, and healthy subjects using muscle MR images and identify the optimal MRI contrast for accurate classification. Approach: In a retrospective study, we utilized SwinT and VGG19 AI models with various MRI contrasts in a 10-fold cross-validation setup. Results: SwinT outperformed VGG19, with the Fat Fraction contrast delivering the highest accuracy of 89.3%±4.9%, highlighting the potential for more accurate MD diagnosis. Impact: This work could improve muscular dystrophy diagnosis, offering clinicians a more objective and accurate tool. Patients may benefit from earlier and more precise interventions, while scientists can explore novel research avenues in AI-driven medical diagnostics, ultimately reducing healthcare disparities.
CycleGAN-based Degradation Enhancement for Unpaired Super-Resolution of Anisotropic Medical Images
Authors: Pascale Michele , Muthurangu Vivek , Steeden Jennifer
Published: November 2024

Motivation: 3D medical images are often acquired with anisotropic volumes to reduce scan times. Super-resolution reconstruction to recover features in the low-resolution direction would improve visualisation and clinical accuracy. Goal(s): To train an unpaired super-resolution network for anisotropic 3D MRI and CT images. Approach: We propose that it is possible to leverage disjoint patches from the high-resolution (in-plane) data to increase the resolution of the low-resolution (through-plane) slices. Results: We demonstrate that our proposed modified CycleGAN architecture, performs better than the standard CycleGAN for super-resolution of MRI and CT data. Impact: Unpaired super-resolution reconstruction of anisotropic 3D medical images, enables accurate recovery of features in the low-resolution direction of MRI and CT data.
Compressed sense acquisition with artificial intelligence based denoising: evaluation in ultra-high field high resolution resting-state fMRI
Authors: Anteraper Sheeba , Dimitrov Ivan , Peeters Johannes , Geraedts Tom , Prins Wim , Henning Anke
Published: November 2024

Motivation: Compressed Sense (CS) acquisition in combination with novel deep learning-based reconstructions has been shown as a viable acceleration technique that brings about additional artificial intelligence (AI) based denoising. Goal(s): Here, we investigate the impact of CS-AI acceleration and denoising on high-resolution resting-state (rs)-fMRI analysis. Approach: CS was performed, and different reconstruction methods were compared: (i) conventional CS, (ii) CS with moderate SmartSpeed AI based denoising and (iii) CS with strong SmartSpeed AI based denoising. Results: Our preliminary results indicate that the underlying reconstruction CS nets do not introduce “artificial” noise or bias and are capable of generating the expected neuronal networks. Impact: Increasing the rs-fMRI resolution, without sacrificing fidelity in functional connectivity maps, via the application of CS-AI, will lead to higher confidence in human brain mapping, thus reducing the number of participants needed to detect differences between healthy and clinical populations.
Stacked Deep U-Net with Hybrid Assisted Priors for Through-Plane Super-Resolution in Brain MRI
Authors: Choi Yoonseok , Al-masni Mohammed , Kim Dong-Hyun
Published: November 2024

Motivation: The motivation behind this study is to alleviate discomfort during clinical exams by improving through-plane resolution. Goal(s): Our objective is to develop a deep learning-based super-resolution approach for low-resolution T2 images, reducing patient discomfort and improving diagnosis accuracy. Approach: We develop a deep learning framework that combines information from T1 and T2 scans, enabling the generation of high-quality images in the through-plane direction. Results: The proposed approach successfully enhances through-plane super-resolution in brian MRI, resulting in superior image quality. This improvement has the potential to improve diagnostic accuracy and alleviate patient discomfort during clinical exams. Impact: This study presents a novel deep learning framework that improves through-plane Super-Resolution in brain MRIs, thus enhancing diagnostic accuracy and reducing patient discomfort during routine health checks.
Application and Assessment of Deep Learning to Routine 2D T2 FLEX Spine Imaging at 1.5T
Authors: Milshteyn Eugene , Chulsky Semyon , Shaikh Ibraheem , Maclellan Christopher , Soman Salil
Published: November 2024

Motivation: Deep learning reconstruction in spine imaging has become widely available, but diagnostic quality and quantitative measurements still need to be verified in the clinical setting. Goal(s): Our goal was to validate application of deep learning to 2D FLEX spine imaging at 1.5T via IQ assessment and noise characterization. Approach: DL and conventionally reconstructed images were assessed by three radiologists. Noise characteristics were evaluated by calculation of total variation and number of detected edges. Fat fraction was also calculated. Results: The radiologists preferred DL in majority of cases (79.5%), with noise noticeably lower in DL images. The fat fraction measurements were very similar. Impact: The application of DL to routine 2D FLEX imaging in the spine provides enhanced diagnostic quality and decreased noise without sacrificing quantitative fidelity. Therefore, the application of DL can be confidently used at 1.5T in the clinic for patient care.
Edge Map Reinforced Two Stage Super-Resolution GAN with Attention-Based DenseNet Generator for Knee MR Images
Authors: Nasim Muhammad Adnan , Touheed Marva , Najeeb Faisal , Omer Hammad
Published: November 2024

Motivation: Super-resolution is challenging especially in medical imaging where recovering fine structures from low-resolution images is essential for accurate diagnosis. Goal(s): Our goal is to achieve super-resolution for the low-resolution knee MR images. Approach: We propose an edge map reinforced super-resolution GAN with attention-based DenseNet generator to enhance the knee structures in two stages: (1) Generating an enhanced edge map for the low-resolution input image; (2) Generating super-resolution output by using the enhanced edge map generated in the first stage and low-resolution input images. Results: The results show that the proposed method provides better output than the contemporary super-resolution GAN method. Impact: This work presents a robust edge map reinforced super-resolution framework for knee MRI by using an attention-based DenseNet generator in super-resolution GAN. The proposed method generates MR images with clear edges and facilitates accurate medical assessment for knee related disorders.
Fast CUBE imaging of pediatric pituitary with deep learning reconstruction algorithm at 3T
Authors: Cui Ruxin , Wang Qidong , Wen Qingqing , Ding Xia , Dou Weiqiang
Published: November 2024

Motivation: 3D CUBE imaging of pediatric pituitary is time-consuming, and thus presents difficulties for children with limited patience and cooperation. A vendor-provided deep learning reconstruction (DLR) algorithm, proposed for high image SNR, may allow for MR imaging with shortened scan time. Goal(s): Explore if DLR allowed for rapid CUBE imaging in pediatric pituitary while maintaining the image quality and precise measurement of pituitary height. Approach: The imaging quality, scan time, and pituitary height measured were compared between DLR-CUBE and conventional CUBE. Results: Relative to conventional CUBE, DLR-CUBE showed improved SNR, comparable image quality, accurate measurement of pituitary height, and only half the scan time. Impact: DLR-CUBE can dramatically shorten the acquisition time while maintaining the image quality and accurate measurement for pituitary height, demonstrating the potential of DLR-CUBE in clinical examinations of pediatric pituitary.
Automatic AI based Intensity harmonization of MRI prostate images.
Authors: Crespo Eduardo
Published: November 2024

Motivation: In multi-centric, multi-scanner datasets differences between images caused by different acquisition protocols and reconstruction techniques are found. These differences introduce biases when developing AI-based solutions that limit their generalization. Goal(s): To develop an algorithm to harmonize intensities on MR medical images independently of the source and artifacts that may be introducing a bias. Approach: Use of the MRI frequency domain to synthetically generate realistic intensity variations simulating differences in acquisition protocols. Image-to-image CNN-based solution to reconstruct any image to a reference dataset. Results: Harmonization of prostate T2w MRI showed a qualitative harmonization of the images and an improvement in AI-based segmentation task. Impact: This methodology helps the harmonization of medical MRI images, enhancing accuracy and efficiency in MRI AI based task. By standardizing image quality and reducing variations, this innovation ensures consistent interpretations across healthcare institutions, improving collaboration among medical and AI professionals.
Zero-Shot lung segmentation of Phase-Resolved Functional Lung (PREFUL) MRI from adults and children with pulmonary diseases
Authors: Zubke Maximilian , Mueller Robin , Wernz Marius , Speth Milan , Klimeš Filip , Voskrebenzev Andreas , Wacker Frank , Vogel-Claussen Jens
Published: November 2024

Motivation: PREFUL MRI relies on accurate lung segmentation. Automating lung segmentation using supervised machine learning requires the laborious creation of training data. Therefore, an alternative independent of availability and peculiarities of training data may be useful. Goal(s): Investigate feasibility and limits of lung segmentation in PREFUL MRI across different vendors, acquisition parameters, age groups and pulmonary diseases without training data. Approach: Segment Anything Model (SAM) using different point grids and seedpoint-based prompts was evaluated in overall 14 different configurations. Results: Comparison with ground truth showed median Dice Similarity Coefficient (DSC) of 0.82 without training data. Impact: Lung segmentation of PREFUL MRI of child and adult patients with different pulmonary diseases appears feasible without training data. The construction of supervised trained segmentation models may be not mandatory for projects when a median DSC of 0.82 is sufficient.
Image quality assessment model for 3D ASL in children
Authors: Lin Ruoge , Yang Qing , Dolui Sudipto , Zeng Yu , He Yong , Zhang Hongxi , Zhao Li
Published: November 2024

Motivation: Arterial spin labeling (ASL) is vulnerable to motion and off-resonance, which may result in unstable image quality, particularly in children. Goal(s): To propose an automatic image quality assessment model for 3D ASL in children. Approach: The proposed model was trained and validated on 51 3D ASL scans from children, and was compared to a previously developed reference method. The performance was evaluated using AUC and 5-fold cross-validation tests. Results: The proposed model yielded 8%-11% higher AUC, accuracy, and F1 score compared to the reference method. Impact: The proposed model for assessing image quality in children's ASL may offer a valuable tool for clinics and studies on brain development with a large cohort.
Off-resonance effects on infant pCASL
Authors: Zhang Zhenyu , Ding Yushuang , Lin Ruoge , Dai Weiying , Alsop David , Zhang Hongxi , Zhao Li
Published: November 2024

Motivation: The image quality of ASL in infants is variable. Goal(s): To investigate the labeling efficiency in infant pCASL. Approach: Labeling efficiency was simulated using the Bloch equation. Prospective scans were performed on 11 subjects, with pCASL at 60mm and 90mm below the AC-PC line, MRA, and B0 map. Results: Blood flow velocity has negligible effects on the labeling efficiency at the on-resonance frequency. The combined effects of off-resonance and the high blood flow velocity are the primary cause of the variable image quality. pCASL images at 60mm showed improved SNR in 8 /11 infants, compared to that of 90mm. Impact: This study examined the factors contributing to variable ASL images in infant and provided a preliminary solution by selecting the labeling plane.
Whole-Brain SNR-Efficient Pseudo-Continuous Arterial Spin Labeling at 7T
Authors: Woods Joseph , Ji Yang , Li Hongwei , Hess Aaron , Okell Thomas
Published: November 2024

Motivation: The high SAR burden of PCASL at 7T typically requires long TRs and short label durations, reducing SNR efficiency and perfusion measurement accuracy. Goal(s): Our goal was to maximize the SNR efficiency of PCASL at 7T. Approach: We optimized the PCASL B1+/gradient parameters and background suppression pulses to maximize SNR efficiency (i.e., balancing labeling efficiency and SAR), and utilized VERSE and dynamic B0 shimming to reduce SAR. Results: Compared to optimizing the PCASL parameters for maximum labeling efficiency, our max-SNR efficiency optimized parameters increased SNR efficiency by 38% and reduced TRs by 41% in vivo. Impact: The improved SNR efficiency of our optimized 7T PCASL parameters increases the advantages of ultra-high field perfusion measurements, enabling robust and high-SNR perfusion measurement in clinically viable scan times.
Machine Learning Based Approach for Partial Volume Corrected Cerebral Blood Flow and Arterial Transit Time Mapping
Authors: Jung Youngkyoo , Kim Donghoon , Lipford Megan , He Hongjian , Ivanovic Vladimir , Lockhart Samuel , Whitlow Christopher , Craft Suzanne
Published: November 2024

Motivation: To reduce a scan time of multi-PLD PCASL imaging using a convolutional neural network (CNN) for robust estimation of partial volume (PV)-corrected ATT and CBF maps. Goal(s): To develop a CNN to predict PV-corrected ATT and CBF from fewer PLDs, ensuring minimal accuracy loss. Approach: Trained and validated a CNN on multi-PLD ASL data from 48 subjects, comparing its performance with a standard method. Results: The CNN achieved low mean average errors, suggesting reduced PLD count does not significantly affect ATT and CBF estimation accuracy. Impact: The study’s CNN reduces MRI scan times while accurately estimating brain hemodynamic parameters, such as cerebral blood flow and arterial transit time, enhancing patient comfort and diagnostic efficiency, potentially transforming cerebrovascular disease monitoring and advancing AI integration in medical imaging.
Reproducibility and sensitivity of BOLD-DSC Perfusion MRI within a single session and across multiple imaging sessions
Authors: LE THUY THI , IM GEUN HO , LEE CHAN HEE , KIM SEONG-GI
Published: November 2024

Motivation: Transient hypoxia-induced BOLD-DSC perfusion imaging approach can noninvasively map cerebral blood flow (CBF) and cerebral blood volume (CBV). However, the reproducibility of this technique has not been previously assessed. Goal(s): We aimed to determine the reproducibility of BOLD-DSC measurements within a single session and across multiple sessions. Approach: The reproducibility of trial-wise BOLD-DSC measurements within each session was assessed for each animal during week 1, while the reproducibility of weekly BOLD-DSC measurements was evaluated across all animals from week 1 to week 4. Results: We found that reproducibility and sensitivity of hypoxia-induced BOLD changes were consistently high in both single and multiple sessions. Impact: The hypoxic challenge induces highly sensitive and reproducible BOLD responses across trials within a single session and consistently across multiple sessions, enabling the longitudinal and repetitive mapping of cerebral perfusion with easily implementable whole-brain BOLD-DSC MRI in mice.
White Matter Damage and Myelin Loss in Rare PolyG Diseases: A 3T MRI Pilot Study
Authors: Hsieh Justin , Hartono Septian , Chan Ling Ling , Laule Cornelia , Ng Adeline Su Lyn , Tan Eng King , Tan Jayne Yi , Lim Tchoyoson CC
Published: November 2024

Motivation: Literature suggests that white matter (WM) may be specifically affected in polyG diseases, a novel class of genetic neurodegenerative diseases including fragile X-associated tremor/ataxia syndrome (FXTAS) and neuronal intranuclear inclusion disease (NIID). Goal(s): To use advanced MRI to characterize WM in polyG disease. Approach: 3T myelin water imaging and diffusion tensor imaging in participants with FXTAS and NIID. Results: FXTAS and NIID demonstrated diffuse cerebral WM damage and myelin loss. Middle cerebellar peduncle (MCP) changes were seen in FXTAS, but not in NIID. Diffusion hyperintense foci in the MCP (FXTAS) and frontal WM (FXTAS and NIID) matched foci of highest dysmyelination. Impact: Fragile X-associated tremor/ataxia syndrome and neuronal intranuclear inclusion disease show diffuse cerebral white matter abnormalities and myelin damage, and spatially differential changes in the frontal white matter and middle cerebellar peduncle.
The involvement of the cerebellum in structural connectome changes in episodic migraine without aura
Authors: Matoso Ana , Fouto Ana , Esteves Inês , Ruiz-Tagle Amparo , Caetano Gina , Silva Nuno , Vilela Pedro , Gil-Gouveia Raquel , Nunes Rita , Figueiredo Patrícia
Published: November 2024

Motivation: While the pathophysiology of migraine remains incompletely understood, several studies reported connectivity disruptions across large-scale brain networks. Goal(s): To study changes in the structural connectome of migraine patients including cortical and subcortical regions as well as the cerebellum, often disregarded. Approach: We performed tractography on diffusion MRI data and applied graph theory metrics to study connectome changes in episodic migraine patients and their healthy controls, using two different whole-brain parcellations. Results: Patients show increased global efficiency and decreased characteristic path length, as well as increased connectivity of cerebellar regions with a greater node degree in the posterior lobe of the cerebellum. Impact: This study sheds light on the importance of including regions other than the cortex in the structural connectome studies of migraine. Indeed, the cerebellum seems to play an important role in migraine, presenting increased connectivity with other regions.
Multimodal MRI Evaluation of Brain Plasticity Following 8 Weeks of Structured Functional and Strength Training
Authors: Abdullah Osama , Enriquez Natascha , Paterson Haidee , Naranjo Jorge , Qadi Ameen , Rokers Bas
Published: November 2024

Motivation: The impact of longer exercise durations and its correlation with comprehensive behavioral changes, such as improvements in strength and working memory, remains relatively underexplored. Goal(s): In this study, we explore the impact of an 8-week strength training program on brain function and structure through multimodal MRI. Approach: We recruited 21 participants, who had been leading a sedentary lifestyle before enrolling. Utilizing a Siemens Prisma scanner and Human Connectome Project protocols, we obtained multimodal MRI data, covering anatomical scans, diffusion imaging, resting state fMRI, and ASL scans. Results: Preliminary findings highlight the importance of microstructural measurements in detecting exercise-induced brain changes. Impact: This research highlights the potential for using multimodal MRI to characterize exercise-induced white matter plasticity in the brain, particularly in motor-related areas.
Deriving and characterizing fiber tract specific anisotropic R2 from DTI
Authors: Raja Rajikha , Pang Yuxi , Reddick Wilburn
Published: November 2024

Motivation: The orientation dependence of transverse relaxation R2 in the human brain white matter could be exploited as a potential myelin specific biomarker for assessing demyelinating pathologies. Goal(s): To investigate the feasibility of characterizing fiber tract specific anisotropic R2 solely based on DTI. Approach: A high-resolution public domain Connectome DTI dataset was used for the demonstration. Two major fiber tracts, corpus callosum and corticospinal tract, were segmented, and corresponding orientation dependent R2 profiles were quantified based on a recently developed model. Results: Results suggested that anisotropic R2 could be extracted effectively from DTI, facilitating easy access to a potential myelin specific biomarker. Impact: In this work, we demonstrated that a single T2-weighted image (i.e., b=0) effectively separates anisotropic R2 from its isotropic counterpart offering an efficient alternative to conventional lengthy R2 mapping method of incorporating in-vivo axon fiber orientation information from DTI.
Assessing Visual Pathway White Matter Degeneration in Primary Open-angle Glaucoma Using Diffusion Spectrum Imaging
Authors: Guo Linying , Tang Zuohua
Published: November 2024

Motivation: The underlying structural pathophysiology of visual white matter degeneration in primary open-angle glaucoma (POAG) remains incompletely understood. Goal(s): To characterize macro- and micro-structural abnormalities in the optic tract and optic radiation of POAG by multiple morphology and diffusion metrics derived from diffusion spectrum imaging(DSI)Approach: DSI was performed on 34 bilateral POAG patients and 25 healthy controls (HCs), and multiple morphology and diffusion metrics were calculated and compared. Results: Significant differences emerged in morphology and diffusion metrics between POAG patients and HCs. Impact: Our demonstration of multiple morphology and diffusion changes in POAG utilizing DS suggests the macro- and micro-structural white matter abnormalities, which could be used for assessing POAG degenetation
Fixel-based analysis of multi-shell diffusion MRI reveals white matter changes after long-duration spaceflight
Authors: Jeurissen Ben , Jillings Steven , Giraldo Diana , Van Ombergen Angelique , Tomilovskaya Elena , Pechenkova Ekaterina , Rukavishnikov Ilya , Petrovichev Victor , Sijbers Jan , Eulenburg Peter zu , Wuyts Floris
Published: November 2024

Motivation: The effects of spaceflight on the central nervous system, and in particular the brain’s white matter (WM), are poorly understood. Goal(s): To gain knowledge about the effect of long-duration spaceflight on the brain's WM. Approach: We performed the first fixel-based analysis of diffusion MRI scans of 18 cosmonauts before and after long-duration spaceflight. Results: We show widespread changes in the WM after spaceflight, which are predominantly macroscopic rather than microscopic. Moreover, we detect a net increase in the amount of WM fibers in the left superior and left middle cerebellar peduncles, providing evidence for neuroplasticity in the brain induced by long-duration spaceflight. Impact: Better understanding and monitoring of the effect of space flight on the brain is crucial to ensure the health of space crews and their performance during long-duration space missions.
The association between cerebral dirty-appearing white matter and progression of small vessel disease in community-dwelling older adults
Authors: Eiling Ingmar , Sigurdsson Sigurdur , Keller Jasmin , Verweg Laura , Launer Lenore , van Osch Matthias , Gudnason Vilmundur , de Bresser Jeroen
Published: November 2024

Motivation: Dirty-appearing white matter (DAWM) might be an early marker of cerebral small vessel disease (SVD) progression. Goal(s): To investigate whether dirty-appearing white matter (DAWM) on brain MRI is related to long-term progression of SVD markers in a cohort of community-dwelling older adults. Approach: We visually rated DAWM volume fraction on baseline FLAIR images (n=2506) and related this to progression of multiple SVD MRI markers at the 5.2-year follow-up. Results: Global and occipital DAWM is associated with an increase in global white matter hyperintensity (WMH) volume at follow-up. This suggests that DAWM is an early marker of SVD, preceding WMH. Impact: The presence of DAWM on MRI might help in early risk stratification of older adults at risk for cognitive decline due to SVD, and in that way allow for selection of individuals for preventive lifestyle interventions and treatment trials.
Associations Between Microstructure, White Matter Hyperintensity Severity, and Cognitive Impairment: An Intravoxel Incoherent Motion Study
Authors: Lin Huihua , Dai Xiaomin , Su Jiawei , Yang Shengsheng , Zheng Yonghong , Ma Mingping , Yu Shun , Song Yang
Published: November 2024

Motivation: It is still unclear what the mechanism of the effect of white matter hyperintensities (WMHs) and cognitive impairment is. Goal(s): To use intravoxel incoherent motion (IVIM) to evaluate the microvascular and parenchymal microstructural changes of WMHs and to investigate their association with cognitive impairment. Approach: The IVIM parameters were compared between patients and controls, different ROIs, and different WMH severity. Additionally, a multivariate linear regression analysis was conducted to explore the factors influencing cognitive scores. Results: WMH patients had higher parenchymal diffusivity than the control group. Furthermore, increased parenchymal diffusivity was associated with an increased risk of cognitive decline. Impact: The IVIM technique has the potential to provide a quantitative marker of parenchymal diffusivity for assessing the severity of WMH and may be useful in quantifying cognitive dysfunction in patients with WMH.