bluuebunny/crossref_metadata_2025 huggingface dataset:
Simultaneous 4D ASL angiography and perfusion MRI using time-encoded pCASL preparation with Stack of Spirals readout
Authors: van der Plas Merlijn , Koolstra Kirsten , Nagtegaal Martijn , Hartsema Emiel , Vaclavu Lena , Schmid Sophie , Petitclerc Leoni , Bornert Peter , van Osch Matthias
Published: August 2024

In this study, a framework was set up for the simultaneous acquisition of 4D MRA and perfusion ASL data while maintaining whole brain coverage. A Hadamard-8 preparation was combined with a 3D Stack of Spirals readout which resulted in 7 timepoints. By combining these two techniques we were able to obtain both angiography and perfusion from a single dataset. By improving the efficiency of the sampling scheme i.e. using variable density spirals, the total scan time could be reduced further while improving the SNR during the perfusion phase, albeit at the expense of the quality of the 4D MRA.
Oxygen Extraction Fraction mapping between normal cognition and mild cognitive impairment in an elderly cohort using quantitative BOLD
Authors: Le Linh , Wheeler Gregory , Fletcher Evan , Blockley Nicholas , Fan Audrey
Published: August 2024

Oxygen Extraction Fraction (OEF) is a valuable indicator of brain physiology influenced by cognitive impairment and vascular pathology in older adults. Our objective is to explore age-related OEF change for cognitively normal (CN) and Mild-Cognitive-Impairment (MCI) subjects. In this study, 56 elderly participants were recruited. Individuals with MCI resulted in higher OEF compared to CN participants, especially in the occipital lobe (p=0.007). In parietal lobe, we observed an inverse correlation between OEF and age (p=0.026) in CN and positive association between OEF and executive function (p=0.023) in MCI. The results showed an effect of aging and cognitive impairment on OEF
Rapid 3D SPirAl Respiratory and Cardiac Self-gated (SPARCS) Cine Imaging with a DEep learning-based rapid Spiral Image REconstruction (DESIRE)
Authors: Wang Xitong , Wang Junyu , Zhao Shen , Zhou Ruixi , Yang Yang , Salerno Michael
Published: August 2024

We developed a rapid 3D self-gated cardiac cine technique, using a variable-density undersampled randomized stack of spiral gradient echo sequence to perform cine evaluation of the whole left ventricle. Our proposed slice-by-slice deep learning-based imaging reconstruction technique for self-gated free-breathing 3D stack of spiral cardiac cine imaging can produce cine images with high temporal (40 ms) and spatial resolution (1.25x1.25x8 mm) within a 20s acquisition time with <1s deep learning inference time.
Database-Free Zero-Shot Deep Learning Reconstruction for Highly-Accelerated Free-Breathing Perfusion CMR
Authors: Demirel Omer , Zhang Chi , Yaman Burhaneddin , Kilic Toygan , Moeller Steen , Shenoy Chetan , Weingärtner Sebastian , Leiner Tim , Akçakaya Mehmet
Published: August 2024

Myocardial perfusion CMR is used to functionally assess coronary artery disease. However, its resolution and coverage remain limited and require rapid imaging. At high accelerations for whole-heart coverage and high spatio-temporal resolution, conventional reconstructions suffer from noise and aliasing artifacts. Physics-guided deep learning (PG-DL) reconstruction has shown improved image quality in fast MRI, but its application to perfusion CMR has been limited due to substantial differences in breathing and contrast uptakes among subjects. In this work, we tackle these challenges by adopting subject-specific self-supervised PG-DL that does not require a training database for simultaneous multi-slice accelerated myocardial perfusion CMR.
MRI reconstruction using DDPM with sparsely sampled k-space as guidance
Authors: Jiang Wei , Gao Yang , Sun Hongfu
Published: August 2024

Recovering MR images from partially acquired k-space data can reduce scan time, leading to significant applications. Deep learning approaches have been proposed to train neural networks using under-sampled and full-sampled image pairs in a supervised manner. However, these supervised models usually have poor generalizability when deployed to acquisitions different from the training datasets. Here, we propose an unsupervised Denoising Diffusion Probabilistic Model (DDPM) capable of not only generating random high-fidelity MR images but also reconstructing images corresponding to k-space data from arbitrary acquisition patterns.
Phantom Results of the ISMRM Joint RRSG–qMRSG Reproducibility Challenge on T1 mapping
Authors: Boudreau Mathieu , Karakuzu Agah , Cohen-Adad Julien , Carr Madeline , Doneva Mariya , Dual Seraina , Ennis Daniel , Ensworth Alex , Foias Alexandru , Fortier Véronique , Gilbert Guillaume , Grech-Sollars Matthew , Holloway Lois , Hu Siyuan , Jalnefjord Oscar , Koken Peter , Kolokotronis Anastasia , Kukran Simran , Lee Nam , Levesque Ives , Ma Dan , Maedler Burkhard , Maforo Nyasha , Moulin Kévin , Near Jamie , Rai Robba , Statton Ben , Stehning Christian , Wang Chenyang , Weiss Kilian , Zakariaei Niloufar , Zhang Shuo , Stikov Nikola
Published: August 2024

A collaborative reproducibility challenge was launched to explore if an imaging protocol independently-implemented at multiple centers can reliably measure T1 using inversion recovery in a standardized quantitative MRI phantom (ISMRM/NIST). A total of 19 submissions were accepted, totalling 41 phantom T1 mapping datasets. Errors relative to the temperature-corrected reference T1 values were under 10% for the range of values expected in the human brain in vivo. All submitted phantom data, code, pipelines, and scripts were shared on open platforms.
A Remote Sequence Streaming Interface for Open Innovation
Authors: Kluge Thomas , Forman Christoph
Published: August 2024

Open innovation and reproducible research have become increasingly important in recent years. Furthermore, software version dependent application development introduces significant efforts to developers and researchers in maintaining different versions of applications for a variety of scanner software releases. In this work, we propose a software-version and programming language independent interface for data acquisition on Siemens Healthcare MAGNETOM scanners. This is achieved by generic interfaces and streaming protocols. A wide range of development and deployment opportunities is enabled with a web-API. The use of the interface is exemplarily shown with a prototype FLASH sequence and demonstrated with a phantom scan.
Developing an Open Access Brain Metastasis Database: Yale Brain Metastasis Database
Authors: Ramakrishnan Divya , Jekel Leon , Sala Matthew , Kaur Manpreet , Janas Anastasia , Petersen Gabriel , Bousabarah Khaled , Lin MingDe , Merkaj Sara , von Reppert Marc , Aboian Mariam
Published: August 2024

While there are many machine learning (ML) algorithms for brain metastasis (BM) detection and segmentation, very few have been validated on external datasets. There is a critical need for open access BM datasets for development and validation of more robust algorithms. Here, we present the Yale Brain Metastasis database of 290 patients with annotated segmentations of BM on T1 post-gadolinium and associated survival information. A subset of 228 patients have FLAIR segmentations, clinical features, and qualitative imaging features. Open access of this database will greatly aid in the development and validation of new AI algorithms for BM detection and segmentation.
Systematic evaluation of the robustness of open-source networks for MRI multicoil reconstruction
Authors: Fujita Naoto , Yokosawa Suguru , Shirai Toru , Terada Yasuhiko
Published: August 2024

Deep neural networks (DNNs) for MRI reconstruction often require large datasets for training, but in clinical settings, the domains of datasets are diverse, and the degree to which deep neural networks are the robustness of DNNs to domain differences between training and testing datasets has been an open question. Here, we evaluated the robustness of four open-source multicoil networks to differences in the domain. We found that model-based networks exhibit higher robustness than data-driven networks and that robustness varies across network architectures, even within model-based networks. Our results provide insight into what network architectures are effective for generalization performance.
Unique brain stiffness and damping ratio patterns associated with morphological phenotypes of normal pressure hydrocephalus
Authors: Karki Pragalv , Murphy Matthew , Cogswell Petrice , Senjem Matthew , Graff-Radford Jonathan , Jack Clifford , Ehman Richard , Huston John
Published: August 2024

Normal pressure hydrocephalus (NPH) is a brain disorder that is often misdiagnosed as Alzheimer’s or Parkinson’s disease due to overlapping symptoms. However, unlike such neurodegenerative diseases, NPH can be surgically treated by shunt placement with a success rate of around 80%, depending on phenotype. Therefore, correct diagnosis and categorization of NPH is important. As a step towards that goal, we demonstrate that analyzing the different morphologic phenotypes of NPH using magnetic resonance elastography can help establish unique viscoelastic signatures associated with each phenotype providing distinguishing biomarkers.
An ultra-fast dementia diagnosis MRI protocol enabled by Wave-CAIPI
Authors: Chughtai Haroon , Thomas David , Rosa-Grilo Miguel , Mulroy Eoin , Beament Millie , Coath Will , Prosser Lloyd , Malone Ian , Alexander Danny , Barkhof Frederik , Mummery Catherine , Fox Nick , Parker Geoff
Published: August 2024

Structural brain imaging is vital in the diagnostic pathway for cognitive disorders and dementias, including the identification of Alzheimer’s disease. MRI is the recommended modality for this but is often not used due to long scan times and lower availability relative to CT. Reduced scan times are needed to enable more widespread adoption of MRI as a first-line modality for cognitive disorders. Our ultra-fast protocol enabled by Wave-CAIPI shows promise in reducing the diagnostic scan time for dementias from around 18 minutes to under 6 minutes whilst retaining clinical utility across several contrasts.
Accelerated multi-shot diffusion MRI using deep learning denoising
Authors: Alus Or , El Homsi Maria , Rodriguez Lee , Mazaheri Yousef , Kee Youngwook , Petkovska Iva , Otazo Ricardo
Published: August 2024

Multi-shot EPI is commonly used to compensate for geometric distortions and increase spatial resolution in body diffusion MRI, with a price tag of longer scan times. This work presents an alternative technique to k-space undersampling to accelerate the acquisition, which is based on reducing the number of repetitions at high b-value and denoising the resulting images using a convolutional neural network. The proposed deep learning denoising technique is demonstrated to accelerate the acquisition of multi-shot diffusion MRI acquisition of patients with rectal cancer and reduce the scan time beyond the duration of a single-shot diffusion MRI acquisition.
The value of diffusion-relaxation correlation spectrum imaging in differential diagnosis of early and advanced squamous cervical carcinoma
Authors: Gao Yihe , Xue Ke , Dai Yongming , Ye Jing
Published: August 2024

Accurately staging squamous cell carcinoma (SCC) is of vital importance for determining the treatment plan, prognosis and outcome. Diffusion-relaxation correlation spectrum imaging (DR-CSI) can jointly encode diffusion and relaxation information and resolve information of tissue compartments and heterogeneity at a sub-voxel level. In this study we investigated the feasibility of DR-CSI in characterizing tissue microenvironment and staging SCC. The DR-CSI technique can non-invasively provide information on microscopic tissue compartments within voxels, reflecting intra-tissue heterogeneity. The diagnostic performance of DR-CSI in identifying early and advanced SCC was significantly superior to conventional ADC and T2.
Classifying patients with chronic mild traumatic brain injury: a rs-fMRI study and support vector machine analysis
Authors: Vedaei Faezeh , Mashhadi Najmeh , Zabrecky George , Monti Daniel , Navarreto Emily , Hriso Chloe , Wintering Nancy , Newberg Andrew B. , Mohamed Feroze B.
Published: August 2024

Machine learning classification of patients with chronic mild traumatic brain injury using rs-fMRI metrics
Effect of iterative denoising in automated white matter hyperintensities segmentation using accelerated FLAIR sequences
Authors: Corredor-Jerez Ricardo , Carrière Mathilde , Chaptal Thierry , Maréchal Bénédicte , Kober Tobias , Ayrignac Xavier , de Champfleur Nicolas Menjot , Troalen Thomas , Le Bars Emmanuelle
Published: August 2024

Acquisition time in brain MR protocols can be reduced using acceleration techniques like CAIPIRINHA. The inherent increase of noise due to their undersampling schemes can be mitigated with additional processing methods. Iterative denoising has shown good performance filtering images in k-space while preserving image details. This work evaluates the impact of iterative denoising on automated segmentation of white matter hyperintensities using CAIPIRINHA 3D FLAIR and compressed sensing 3D MPRAGE. Reliable segmentations were generated across different denoising levels (45% to 85%); small structures presented lower detection rates with stronger denoising (≥75%). These findings are revelant for designing optimized brain imaging protocols.
fMRI with whole-brain coverage, 75ms temporal resolution and high SNR by combining HiHi reshuffling and Multiband imaging
Authors: Schmidt Tim , Vannesjo Johanna , Sommer Stefan , Nagy Zoltan
Published: August 2024

By combining an fMRI data shuffling method and multiband accelerated acquisition, we were able to measure the hemodynamic response in the primary motor and visual cortices with 75ms temporal resolution while maintaining high SNR. With the addition of multiband imaging, we were able to achieve whole-brain coverage in a feasible scan time, and use appropriate event-related stimulus paradigm to map the BOLD response in the primary motor and visual cortices in a combined experiment.
Optimized fMRI preprocessing pipeline enables robust functional connectivity analysis of mouse brain at laminar level
Authors: Zhu Wei , Zhang Guangle , Zhu Xiao-Hong , Chen Wei
Published: August 2024

High-resolution BOLD fMRI has become an essential tool for studying neural circuit and hemodynamic changes at mesoscopic scale. Nevertheless, it is more prone to the poor sensitivity and non-neural signal contamination as the spatial resolution increases. Group-level analysis also imposes new requirements on the subject alignment accuracy. To deal with these challenges, we developed a fMRI preprocessing pipeline featured in random matrix theory-based PCA denoising, one-time image voxel shift correction, and enhanced subject-level alignment. We applied this pipeline to the high-resolution mouse resting state fMRI and achieved high-quality hierarchical connectomes from large brain regions to thin cortical layers.
Detecting the role of hippocampal subfields in pattern separation and completion task using 7T object memory task fMRI
Authors: Yang Zhengshi , Zhuang Xiaowei , Lowe Mark , Dietmar Cordes
Published: August 2024

In this study, we have established a reliable hippocampal subfield segmentation and activation analysis pipeline to probe the role of hippocampal subfields in pattern separation task by using 7T fMRI data, which could be able to identify the role of subfield dysfunction in cognitive impairment in prodromal AD.
Frequency and Phase Correction of GABA-edited MR Spectroscopy using Complex-valued Convolutional Neural Network
Authors: Bugler Hanna , Berto Rodrigo , Souza Roberto , Harris Ashley
Published: August 2024

Edited Magnetic Resonance Spectroscopy (Edited-MRS) is important for the quantification of ɣ-amino butyric acid (GABA) in vivo. However, during acquisition, data may suffer phase and frequency shifts, which affects the quality of the output spectrum. Frequency and phase correction (FPC) is necessary to account for these shifts, and deep learning models have obtained recent success in this task. Still, current methods do not take into consideration that MRS data is complex-valued. We propose a complex-valued convolutional neural network model for FPC. Our results showed that our model compares favorably against two recently proposed deep learning methods.
Differential MRS results in a clinical multisite study depending on site harmonization and analysis approach
Authors: La Parker , Bell Tiffany , Craig William , Doan Quynh , Beauchamp Miriam , Zemek Roger , Yeates Keith , Harris Ashley
Published: August 2024

The use of multiple sites and scanners is commonly used to increase sample size; however, this introduces unwanted variance into spectroscopy data. In this work we compare statistical co-variate techniques in combination with ComBat harmonization using data from a concussion study. We show traditional statistical methods of accounting for multi-site variance (using covariates to control for site and vendor) could result in erroneous interpretation of clinical data. Our data supports the use of ComBat harmonization and suggests how to best control for multisite data statistically.
Glutamate and its relation to cortical thickness and perfusion across the cingulate cortex
Authors: Archibald Jessica , MacMillan Erin , Zölch Niklaus , Kramer John
Published: August 2024

Investigating the relationships between morphological, functional, and metabolic changes in the brain can help reveal subtle changes caused by disease. To establish an understanding of the relationship between brain glutamate levels with cortical thickness (CT) and cerebral blood flow (CBF) in the healthy brain, magnetic resonance spectroscopy (MRS), arterial spin labeled MRI and anatomical MRI were obtained from 4 sub-divisions of the cingulate cortex. A linear mixed effects model revealed that glutamate was significantly correlated with CT (p=0.05, R2=0.30) but not with CSF (p=0.21, R2=0.17) within healthy participants. These results will help inform studies of disease or therapeutic interventions.
Characterization of small bowel strictures in Crohn’s disease with multiparametric MRI
Authors: Altinmakas Emre , Sharma Himanshu , Bane Octavia , Abboud Ghadi , Menys Alex , Hao Yansheng , Colombel Jean-Frederic , Harpaz Noam , Taouli Bachir
Published: August 2024

Small bowel stricture (SBS) is the one of the most common complications of Crohn’s Disease (CD) and it usually requires surgical management. Imaging diagnosis and characterization of SBS is essential for proper patient management, however this may be challenging with conventional MRI techniques. In current study, we investigate the utility of advance MRI techniques namely multiparametric MRI (mpMRI) including diffusion, perfusion, and motility in the characterization and tissue composition in CD-related SBS. Our preliminary results suggest that mpMRI parameters are promising tools to characterize SBS and may be helpful in the stratification of patients for medical treatment vs surgery.
In vivo Quantitative evaluation of MR Imaging: Irisin Activates Brown Adipose Tissue and Improves Systemic Metabolic Disorders in Obese Mice
Authors: Dai Jingyue , Zhao Yufei , Peng Xingui
Published: August 2024

Irisin is an exercise-induced myokine which can induce white adipose tissue browning; however, its impact on BAT remains unclear. Here, we employed MR chemical shift-selective imaging to quantify fat fraction, and to verify these MR-derived data by that from laboratory analysis to investigate the effects of irisin on BAT and systemic metabolism in obese mice. Irisin reduced the fat fraction and increased the UCP1 protein expression of BAT. Besides, irisin helped obese mice to loss weigh and improve systemic metabolic disorders. These results suggests that irisin can reactivate brown adipose tissue and improve systemic metabolic disorders in obese mice.
IRIS-DL: A Deep Learning Software Tool for Identifying Genetic Mutations in Gliomas and Meningiomas
Authors: Bas Abdullah , Sacli-Bilmez Banu , Buz-Yalug Buse , Sumer Esra , Azamat Sena , Hatay Gokce , Danyeli Ayca , Can Ozge , Ozduman Koray , Dincer Alp , Ozturk-Isik Esin
Published: August 2024

Intelligent Radiological Imaging Systems (IRIS)-DL is a deep learning software tool that includes libraries for segmenting tumor regions and identifying several genetic mutations in gliomas and meningiomas. The tool has three modules, which are “Model Library”, “Trainer”, and “Plotter”. In the “Model Library”, the users could run pre-trained models on their local data. The “Trainer” module is for creating custom AI (conventional machine learning, artificial neural networks, and deep learning) models on the user data. Lastly, “Plotter” module is for data visualization and explorative data analysis.
Predicting Post-Stereotactic Radiotherapy Magnetic Resonance Images: A Proof-of-Concept Study in Breast Cancer Metastases to the Brain
Authors: Pandey Shraddha , Kutuk Tugce , Mills Matthew , Abdalah Mahmoud , Stringfield Olya , Latifi Kujtim , Moreno Wilfrido , Ahmed Kamran , Raghunand Natarajan
Published: August 2024

Stereotactic radiosurgery (SRS) can provide effective local control of breast cancer metastases to the brain while limiting damage to surrounding healthy tissues. Knowledge-based algorithms have been reported that can alleviate the manual aspects of radiation dose planning, but these do not currently provide voxel-level dose prescriptions that are optimized for tumor control and avoidance of radionecrosis and associated toxicity. On the assumption that a voxelwise relationship exists between pre-SRS MR images, the RT dose map, and the resulting post-SRS MR images, we have investigated a deep learning framework to predict the latter from the former two.
Variable-flip-angle 3D spiral-in-out TSE/SPACE using echo-reordering and concomitant gradient compensation at 0.55 T
Authors: Wang Zhixing , Ramasawmy Rajiv , Javed Ahsan , Mugler John , Meyer Craig , Campbell-Washburn Adrienne
Published: August 2024

This study describes an alternative approach to Cartesian SPACE for 1 mm3 isotropic whole brain T2-weighted imaging on a high performance 0.55T scanner. In this technique, the Cartesian readouts were replaced by interleaved, rotated spiral-in-out trajectories, combined with a variable-flip-angle refocusing, echo-reordering, and concomitant gradient compensation. Parallel imaging (PI) and compressed sensing (CS) were utilized for further acceleration. Compared to 3D-Cartesian SPACE, this method can be leveraged to mitigate the lower SNR of 0.55T via the improved SNR efficiency of prolonged spiral trajectory sampling.
S-EBM: generalising event-based modelling of disease progression for simultaneous events
Authors: Parker Christopher , Oxtoby Neil , Alexander Daniel , Zhang Hui
Published: August 2024

S-EBM generalises the event-based model (EBM) of disease progression for simultaneous events. In synthetic data, S-EBM can tell the difference between simultaneous and non-simultaneous events under a range of experimental conditions. In comparison to conventional EBM, S-EBM avoids artificial serial orderings, permitting more accurate and parsimonious descriptions of disease progression. When applied to real Alzheimer’s disease biomarker data, S-EBM estimates a sequence containing neurologically plausible simultaneous events which more closely explain the data than conventional EBM. S-EBM may be applied to recover novel patterns of disease progression thereby informing our understanding of disease evolution.
Impacts of bone on the worst-case configuration for orthopedic implants under 1.5T and 3T MRI
Authors: Yang xiaolin , Zheng Jianfeng , Guo Ran , Kainz Wolfgang , Chen Ji
Published: August 2024

Due to the differences in both electrical and thermal properties, the RF-induced heating from implantable devices under MRI can have different behaviors inside muscle-like tissue and bone. A locally modified ASTM phantom with bone tissue was developed in the study. Simulations were used in the study to determine the worst-case heating configurations in the original and the modified ASTM phantoms. Based on our study, it was observed that the worst-case heating configuration can be altered when the devices are implanted in/near bone tissues. Consequently, additional in-vivo modeling would be required to understand the clinically relevant RF-induced heating.
Whole brain Layer-fMRI on the NexGen 7T scanner with high performance gradients and 64-channel receiver array.
Authors: Beckett Alexander , Huber Renzo , Ma Samantha , Häkkinen Suvi , Gunamony Shajan , Feinberg David
Published: August 2024

Laminar-resolved fMRI has the potential to capture directional information flow within and between cortical areas to inform network neuroscience. However, common layer-fMRI imaging protocols are constrained by: limited slab coverage commonly used 0.8mm resolution barely resolves individual layer groups. In this abstract, we use the NexGen 7T scanner to develop a whole-brain functional imaging protocol at 0.6mm resolutions. The aim was to identify and mitigate challenges in protocol optimization of 3D-EPI VASO: maintaining CBV-weighting with long acquisition windows via segmentation across IR, residual EPI ghosts residual fat ghosts We show whole-brain 0.64mm CBV-based connectivity maps covering the entire neocortex.
Enhancing fluid signal in driven-equilibrium short-tau inversion-recovery (STIR) imaging with short TR times.
Authors: von Deuster Constantin , Nanz Daniel
Published: August 2024

Fluid-sensitive Turbo-Spin-Echo (TSE) imaging in combination with Short-Tau Inversion Recovery for fat suppression (STIR) is commonly employed in musculoskeletal MRI. A Driven Equilibrium (DE) module following the imaging train can be used to reduce the Repetition Time (TR) without significant SNR loss. However, due to the STIR preparation, a conventional DE element rotates the fluid magnetization onto the negative z-axis instead of the positive z-axis, which delays longitudinal relaxation and attenuates the fluid signal. We implemented a modified DE module that increased fluid-signal intensity in STIR-TSE images of the spine.
Metabolic changes in hippocampus in preclinical model due to chronic noise exposure: An in-vivo 1H Magnetic Resonance Spectroscopy study.
Authors: Chauhan Nisha , Kumaran S Senthil , Singh Himanshu
Published: August 2024

Occupational and environmental noise contributes to major public health issues. The chronic high level noise exposure may cause stress and depression and also alterations in Neurometabolites. In-vivo 1H MRS and Open-Field Test are carried out to elucidate the changes in brain metabolite and its effect on behavior due to chronic noise exposure in a preclinical model. A significant increase in Glutamate and N- Acetylaspartate metabolite was observed in hippocampus which is an important target to study the effects of stress. Reduced total movement time, movement distance and mean velocity were observed in the noise exposed group.
Prominent role of cerebellar communication in schizophrenia reflected by resting state fMRI connectivity
Authors: Ramkiran Shukti , Rajkumar Ravichandran , Brambilla Claudia Régio , Orth Linda , Sbaihat Hasan , Kaulen Nicolas , Mauler Jörg , Veselinović Tanja , Khudeish Nibal , Tellmann Lutz , Langen Karl-Josef , Lerche Christoph , Shah N. Jon , Neuner Irene
Published: August 2024

Schizophrenia is a complex neuropsychiatric disorder, the pathophysiology of which is unclear. Several studies have shown the involvement of altered brain communication. Several new voxel-level connectivity measures such as radial similarity, radial correlation, inter-hemispheric connectivity and local correlation have enabled a deeper understanding of alterations in communication. Our results reveal the prominent role of the cerebellum in schizophrenia by investigating voxel-level connectivity in resting state fMRI data.
Disrupted Associations between the Transcriptome Profile, Brain Structure and Function, and Cognitive Ability in Schizophrenia
Authors: Zhao Shu-wan , Yin Hong , Cui Long-biao
Published: August 2024

Cognitive dysfunction is considered to be one of the core symptom dimensions of schizophrenia. To deconstruct the underlying etiological behind cognitive deficits from a more integrated and multidimensional perspective, we established a trans-scale dataset at the level of the individual corresponding to each participant and control, containing blood-sample transcriptome profile, neuroimaging endophenotypes, and cognitive ability. Multivariate correlation analysis and mediation analysis demonstrated that the gene-brain-cognition associations were exsisted in healthy controls, whereas disrupted in patients with schizophrenia. These results provide a preliminary clue in comprehending the misaligned relationship between the microscale biological pathways and macroscopic phenotypes in schizophrenia.
QSM in rats with minimal hepatic encephalopathy: does iron overload aggravate cognitive impairment by promoting neuroinflammation?
Authors: Yang Xuhong , Dang Pei , Zheng Jiarui , Li Min , Lv Ruirui , Wang Xiaodong
Published: August 2024

In this study, we used the QSM technique to quantitatively evaluate the levels of brain iron in the hippocampus of MHE rats and by evaluating the neuroinflammatory response to explore the relationship between iron with neuroinflammation and cognition. It is expected to provide important insights into the occurrence of MHE disease symptoms and the related biological mechanisms behind them. Meanwhile, our findings also suggested that an abnormal susceptibility values in certain brain areas identified using the QSM may be a potential biomarker to reflect the severity of cognitive impairment and to monitor its progression in cirrhotic patients with MHE.
Dynamic T2* Relaxometry of Hyperpolarized [1-13C]pyruvate MRI in the Human Brain and Kidneys
Authors: Liu Xiaoxi , Cui Di , Xu Duan , Bok Robert , Wang Zhen , Vigneron Daniel , Larson Peder , Gordon Jeremy
Published: August 2024

We present dynamic T2* measurements for HP [1-13C]pyruvate and metabolites in a healthy human brain volunteer and two RCC patients at 3T. The T2* of pyruvate was shown to vary during the acquisition, whereas the T2* of lactate and bicarbonate was constant through time and across organs. The T2* of lactate was constant at gray matter (30.1±5.9ms), white matter (33.8±7.6ms), healthy kidney (38.94±6.9ms) and tumor (33.27±6.4ms), and the T2* of bicarbonate over whole-brain (109.5±12.8ms) and kidney (64.6±15.8ms). These relaxometry measurements will be useful for future sequence optimization and can be included in kinetic modeling to harmonize data across different TEs.
Free-Breathing, Confounder-Corrected, 3D T1 Mapping of the Liver through Simultaneous Estimation of T1, PDFF, R2* and B1+
Authors: Muslu Yavuz , Cashen Ty , Mandava Sagar , Hernando Diego , Reeder Scott
Published: August 2024

T1 relaxation is emerging as a biomarker for the diagnosis and staging of chronic liver disease. Modified Look-Locker Inversion Recovery (MOLLI) is widely used in clinical practice for abdominal T1 mapping; however, current methods are not corrected for fat and B1 inhomogeneities as confounding factors and fail to provide reliable T1 measurements. In this work, we propose a novel, free-breathing, confounder-corrected T1 mapping method over the entire liver, by combining inversion recovery and chemical shift encoding imaging for simultaneous estimation of T1, PDFF, R2* and B1+.
Accelerated T2 Mapping with GS-Assisted Deep Translation of T1W Image Prior
Authors: Liu Ruihao , Li Yudu , Guo Rong , Zhao Yibo , Meng Ziyu , Zhuang Huixiang , Wang Tianyao , Li Yao , Du Yiping , Liang Zhi-Pei
Published: August 2024

Deep learning (DL)-based methods have shown great potential for accelerating T2W imaging by using image prior generated from a companion T1W image. However, quantitative T2 mapping requires multiple T2W images acquired with multi-TE, creating practical problem for the use of DL for accelerated T2 mapping due to insufficient multi-TE training data. This work addresses this problem by using a generalized series model to map a T2W DL prior for one TE to multiple TEs, enabling effective use of T1W image prior for high-quality T2 mapping from sparsely sampled data. The method has been validated using experimental data, producing impressive results.
Deep Learning Based Algorithm to Identify Large Vessel Stenosis and Occlusion on Contrast Agent-free Magnetic Resonance Imaging
Authors: Wu Di , Sun Mengzhou , Li Yi , Liang Xiaoyun , Huang Feng , Zhu Wenzhen
Published: August 2024

Large vessel occlusion detection based on clinical scales is of low sensitivity and that based on CTA needs contrast agent exposure. This study aims to develop a deep learning (DL) algorithm for detecting intracranial large vessel steno-occlusion on contrast agent-free MR techniques including DWI and ASL. The accuracy of the DL algorithm was 88.2% with a sensitivity of 88.0%, comparable to CTA-based DL algorithms with sensitivity ranging from 67% to 94%. The MR-based DL algorithm is feasible to accurately detect intracranial large vessel steno-occlusion without intervention, radiation exposure and contrast agent, which could optimize stroke workflow and guide clinical decision-making.
Do Breast Edema and Shrinkage pattern Provide Additional Evaluation Value for Treatment Response and Recurrence in Luminal Breast Cancer
Authors: Sun Shiyun , Gu Yajia , You Chao
Published: August 2024

This study aims to develop an accurate and convenient prediction model for luminal breast cancer based on conventional MRI. First, we evaluated conventional MRI features and their changes during NAC (pre, early and post) and identified stable imaging markers that predicted multiple treatment responses and prognosis. Second, we comprehensively explored the shrinkage pattern, breast edema and changes during NAC. We found that both of them can provide added value to traditional MRI features. Finally, we constructed a combined model with multi-parameter MRI features and clinicopathological information, which showed good prediction performance in treatment response and prognosis of luminal breast cancer.
Diagnostic performance of DCE-MRI Parameters in Distinguishing Tumor Deposits from Metastatic Lymph Nodes in Rectal Cancer
Authors: Xu Qiaoyu , Sun Hongliang , Xu Yanyan , Wang Juan , Zhang Tongyin
Published: August 2024

This study aimed to investigate the usefulness of diffusion-weighted imaging (DWI) derived apparent diffusion coefficient (ADC) values and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) derived semi-quantitative parameters in distinguishing TD from MLN before surgical resection in RC patients. TD has some specific morphological features, including relatively larger size, lower short- to long-axis ratio, irregular shape and ill-defined border on T2-weighted MR images in rectal cancer. The combination of ADC values and parameters of DCE-MRI can help to improve the diagnostic efficiency of TD in rectal cancer.
Diffusion-relaxation correlation spectroscopic imaging for evaluating response to neoadjuvant therapy in locally advanced rectal cancer
Authors: Zhao Xixi , Hu Wentao , Hu Honglei , Chen Chuyao , Dai Yongming , Xu Yikai
Published: August 2024

Different treatment strategies should be applied to locally advanced rectal cancer (LARC) patients achieving different response after neoadjuvant chemoradiotherapy (nCRT). The purpose of this study was to determine the diagnostic performance of diffusion-relaxation correlation spectroscopic imaging (DR-CSI) in discriminating the pathological complete response (pCR) to nCRT in LARC. Compared to non-pCR group, pCR group showed significantly decreased average DR-CSI VD. ROC curve analysis indicated that the VD has better diagnostic performance in distinguishing pCR from non-pCR patients than traditional ADC and T2 value.
Pelvic organs prolapse recurrence after surgery using static and dynamic MRI
Authors: Zhang Cheng , Zhao Yujiao , You Cong , Shen Zhiwei , Shen Wen
Published: August 2024

As high as 54% is the anatomical recurrence rate following hysterectomy with anterior and posterior vaginal wall restoration for pelvic organ prolapse (POP). The levator ani muscle (LAM) is the primary pelvic floor support system. Using static and dynamic MRI, we investigated the association between the morphology and function of LAM and the postoperative recurrence of POP. Risk factors for the recurrence of POP included the thickness and injury of the LAM at rest, the H line, the M line, and the size of the levator hiatus at rest and strain, as well as the variation of the levator hiatus during the Valsalva maneuver.
Double Pulsed Field Gradient Diffusion MRI of skeletal muscle is sensitive to muscle microstructure
Authors: Berry David , Galinsky Vitaly , Hutchinson Elizabeth , Ward Samuel , Frank Lawrence
Published: August 2024

Single pulsed field gradient (sPFG) diffusion MRI (dMRI) is a commonly researched tool to monitor changes in muscle microstructure associated with injury. Double pulsed field gradient (dPFG) dMRI has been used to characterize microstructure of neural tissues, but is not commonly applied to skeletal muscle. This study demonstrates both in silico and in vivo applications of dPFG dMRI to assess changes in muscle microstructure that are related to muscle function (e.g. fiber diameter). Additionally, a novel method for analyzing dPFG data called Diffusion Tensor Subspace Imaging (DiTSI) demonstrates high sensitivity to quantifiable biomarkers of muscle microstructure.
A flat fully-populated MR-guided focused ultrasound phased array body system
Authors: Jones Ryan , Huang Yuexi , Lucht Benjamin , Gunaseelan Samuel , Portelli Tyler , David Elizabeth , Hynynen Kullervo
Published: August 2024

Existing commercial body MR-guided focused ultrasound (MRgFUS) systems have limited electronic steering ranges and rely on mechanical translation/rotation to ablate large tissue volumes. Here, we describe a flat fully-populated MRgFUS phased array body system with increased electronic steering capabilities. Following extensive benchtop and pre-clinical testing, the device was evaluated in a pilot clinical trial of MRgFUS for the treatment of uterine fibroids, which demonstrated the feasibilty and safety of the approach. The technology is extensible, stackable, and modular, allowing custom MRgFUS device development tailored to any indication.
Creating in vivo conformal thermal treatments using a multisource LITT device combined with real time multi-slice MR-thermometry
Authors: Desclides Manon , Ozenne Valéry , Machinet Guillaume , Pierre Christophe , Chemouny Stéphane , Quesson Bruno
Published: August 2024

Percutaneous clinical ablation devices usually create a heating pattern with an ellipsoid shape. Output power and duration of application are the only degrees of freedom, which does not allow creating conformal ablation. To overcome this limitation, we designed a Laser Interstitial Thermal Therapy (LITT) device allowing creating various heating patterns. We present here in vivo results in pig muscle monitored by real-time 2D multi-slice MR-thermometry.
Quasi-Diffusion: A Model Of Signal Attenuation In Diffusion Magnetic Resonance Imaging Of Brain Tissue
Authors: Barrick Thomas , Ingo Carson , Howe Franklyn
Published: August 2024

We show the quasi-diffusion model describes the dMRI signal from low b-value (stretched exponential) to high b-value (power law) regimes via a single, parsimonious function of two independent parameters. We identify new tissue contrast via a signal inflection point indicating the transition to the localisation regime. Quasi-Diffusion Imaging (QDI) parameter estimates converge to stable values as maximum b-value is increased, suggesting quasi-diffusion is a valid model for brain tissue dMRI signal decay. Accuracy of QDI parameters computed from 4 b-values of a 12 b-value acquisition indicate QDI measures may be derived from data acquired in clinically feasible times.
Simultaneous T2/T2* quantification for oxygen extraction fraction estimation using GE-SE EPIK: A preliminary study in brain tumor patients
Authors: Küppers Fabian , Yun Seong Dae , Lohmann Philipp , Filss Christian , Stoffels Gabriele , Langen Karl-Josef , Shah Nadim
Published: August 2024

The versatile information provided by simultaneous multi-contrast GE/SE acquisitions keeps interest in this area high. This work extends the previously published 10-echo GE-SE EPIK to investigate brain tumor patients. Within this scope, a single-slice 12-second acquisition provides quantification of T2 and T2* with application to oxygen extraction fraction (OEF) information. Data from four tumor patients were acquired, revealing increased T2/T2* values and increased OEF in regions with variable amino acid uptake in O-(2-[18F]fluoroethyl)-L-tyrosine (FET) PET.
Effects of SNR on T2 and T2* estimates from a cardiac gradient-echo spin-echo (GESE) echo planar sequence
Authors: Huang Qi , Mendes Jason , Adluru Ganesh , DiBella Edward
Published: August 2024

The bias and precision of T2 and T2* estimates for a five-echo GESE model were studied using CRLB and Monte Carlo simulations. Different SNR cases were implemented by varying the standard deviation of added noise or the T2/T2* ratio. An in-vivo study with high SNRs for all EPI readouts was performed to validate the simulation.
Detection of Age-related Cerebral Microvessel Tortuosity and Its Association with Dilated Perivascular Space Using USPIO-enhanced 7T MRI
Authors: Sun Zhe , Li Chenyang , Haacke E. Mark , Ge Yulin
Published: August 2024

Microvessel tortuosity, which is lack detection on clinical imaging, may have direct detrimental effects on capillary flow and nutrient supply which is critical in maintaining neural functions. A high-resolution USPIO-enhanced 7T MRI demonstrated corkscrew appearing medullary arterioles (about 50 µm) in white matter with some enclosed in dilated perivascular space (PVS). The number of tortuous arterioles increased with aging and more tortuous vessels with dilated PVS were found in elderly people (P<0.05). The dynamic interactions between vulnerable tortuous arteries and PVS dilation may be the basis of age-related hypoperfusion, white matter pathology, and waste clearance dysfunction in the elderly.
4D-Flow and DCE-MRI Study on the Wall Enhancement of Unruptured Intracranial Aneurysms
Authors: Fu Qichang , Zhang Yong , Zhu Chengcheng , Guan Sheng , Cheng Jingliang
Published: August 2024

This study used 4D-flow-MRI, dynamic contrast-enhanced MRI, and vessel wall MRI to explore the relationship between wall shear stress (WSS) and Ktrans and aneurysm wall enhancement (AWE) to understand the functional changes in the unruptured intracranial aneurysms (UIAs). Seventy-eight patients were enrolled, including 96 UIAs. All patients completed examinations based on a 3.0T Siemens Prisma magnetic resonance. WSS with the AWE group was lower, and Ktrans with the AWE group were higher than those without AWE. The wall enhancement index (WEI) was a quantitative index of AWE. WEI had a negative correlation with WSS and a positive correlation with Ktrans.
Abbreviated MRI with Second Shot Arterial Phase for HCC Evaluation: Modified Version of LI-RADS and Recall Reduction Strategy
Authors: Kim Jeong Woo , Lee Chang Hee
Published: August 2024

A modified version of LI-RADS was devised for abbreviated MRI (AMRI) with second shot arterial phase (SSAP) by referring to CEUS LI-RADS. The modified LI-RADS scores using AMRI with SSAP showed a high concordance rate with the conventional LI-RADS score using full-protocol MRI. The recall rate significantly decreased when the HCC surveillance and diagnosis strategy was changed from strategy 1 (AMRI without SSAP; surveillance then recall test) to strategy 2 (AMRI with SSAP; simultaneous surveillance and diagnosis).
Preoperative Prediction of CK19 Expression and Early Recurrence of Hepatocellular Carcinoma using T1 mapping on Gadoxetic acid-enhanced MRI
Authors: Zhao Yue , Guan Xiuhong , Xu Yongzhou , Jiang Xinqing , Yang Ruimeng
Published: August 2024

Cytokeratin 19 (CK19) is well acknowledged as a progenitor cell marker and tumor stem cell marker that plays an important role in promoting the malignant property of HCC. If the preoperative CK19 expression status in HCC can be accurately predicted noninvasively, it may provide important information for clinical decision-making. T1 mapping is useful for preoperative prediction of CK19 expression and early recurrence of HCC. The clinical-quantitative model combining alpha-fetoprotein and quantitative features showed good performance and robustness in predicting CK19 expression. T1 relaxation time on hepatobiliary phase was an independent predictor of CK19 expression and recurrence-free survival.
Corona Enhancement Combined with Microvascular Invasion for Identification / Prognosis of Macrotrabecular-Massive Hepatocellular Carcinoma
Authors: Yang Lili , Wang Meng , Zhu Yanyan , Zhang Jiahui , Chen Feng
Published: August 2024

The macrotrabecular-massive (MTM) subtype of hepatocellular carcinoma (HCC) is aggressive and associated with an unfavorable prognosis. This study aimed to characterize MTM-HCC features based on contrast‑enhanced MRI and to evaluate the prognosis of imaging characteristics combined with pathology for predicting early recurrence and overall survival after surgery. This retrospective study included 123 and 59 patients with HCC that underwent preoperative contrast‑enhanced MRI and surgery, respectively. The multivariate analysis and multiple Cox regression analysis identified that corona enhancement combined with MVI risk was significantly associated with poor outcomes after surgery.
Patient-specific computational model of interstitial fluid flow in pancreatic cancer based on DCE- and DW-MRI
Authors: Salavati Hooman , Ceelen Wim , Debbaut Charlotte , Lecluyse Clarisse , Pullens Pim
Published: August 2024

The abnormal interstitial fluid flow (IFF) in solid tumors characterized by the elevated interstitial fluid pressure (IFP) is one of the major barriers for treating solid tumors. Here, we implemented DCE-MRI and IVIM DW-MRI in a clinical study to upgrade the existing basic mathematical framework of IFF to a patient-specific model. The results were compared with the basic model, which showed a noticeable difference regarding the prediction of the heterogeneity of IFF.
Automated Processing and Segmentation of Abdominal Structures Using a Hybrid Attention-Convolutional Neural Network Model
Authors: Basty Nicolas , Srinivasan Ramprakash , Thanaj Marjola , Sorokin Elena , Cule Madeleine , Thomas E Louise , Bell Jimmy , Whitcher Brandon
Published: August 2024

Automated image processing and organ segmentation are critical to the quantitative analysis of population-scale imaging studies. We have implemented an end-to-end pipeline for neck-to-knee Dixon MRI data based on the UK Biobank abdominal protocol. Bias-field correction, blending across series boundaries, and fat-water swap correction are performed in the preprocessing steps. A hybrid attention-convolutional neural network model segments multiple abdominal organs, major bones, along with adipose and muscle tissue. The application of neural network models, to both swap detection and segmentation, produces a computationally-efficient pipeline that scales to accommodate tens of thousands of datasets.
Accelerated MRI Reconstruction using Adaptive Diffusion Probabilistic Networks
Authors: Güngör Alper , Dar Salman Ul Hassan , Öztürk Şaban , Korkmaz Yilmaz , Elmas Gokberk , Ozbey Muzaffer , Çukur Tolga
Published: August 2024

Learning-based MRI reconstruction is commonly performed using non-adaptive models with frozen weights during inference. Non-adaptive conditional models poorly generalize across variable imaging operators, whereas non-adaptive unconditional models poorly generalize across variations in the image distribution. Here, we introduce a novel adaptive method, AdaDiff, that trains an unconditional diffusion prior for high-fidelity image generations and adapts the prior during inference for improved generalization. AdaDiff outperforms state-of-the-art baselines both visually and quantitatively.