bluuebunny/crossref_metadata_2025 huggingface dataset:
Transfer learning for non-parametric prediction of joint distributions of g-ratios and axon diameters from MRI
Authors: Kung Gustavo Chau Loo , Weber Emmanuelle , Batra Ankita , Ni Lijun , Zeineh Michael , Knowles Juliet , McNab Jennifer
Published: November 2024

Motivation: Machine learning approaches are an alternative to conventional biophysical model fitting used to generate MRI microstructural maps, but the lack of paired MRI-histology data complicates end-to-end training of these models. Goal(s): Develop a nonparametric deep learning based prediction of joint distributions of g-ratios and axon diameters from multimodal MRI data. Approach: Histology-based synthetic MRI data was used to pretrain a conditioned normalizing flow model. Transfer learning was then performed on limited paired MRI-histology data. Results: The joint distribution shows good visual agreement with actual samples and the distances between the marginal probabilities and their respective samples exhibit a Jensen-Shannon distance smaller than 0.22. Impact: We present an optimized model to obtain non-parametric joint distributions of g-ratios and axon diameters from multimodal MRI from limited experimental data. The approach can easily be adapted to other microstructural modeling tasks.
The Effect of Deep Learning on Radiomic Imaging Features: A Phantom Study
Authors: Peake Edward , Duarte Joao , Priest Andrew , Graves Martin
Published: November 2024

Motivation: To investigate the effect of a deep learning reconstruction algorithm on radiomic image features. Goal(s): To assess the effect of AIRTM Recon Deep Learning (ARDL), a commercial AI reconstruction algorithm, on radiomic features in a set of phantoms. Approach: A set of radiomic phantoms were constructed and used to acquire images with different numbers of signal averages and ARDL levels. Effects were evaluated through intraclass correlation coefficient (ICC) measures. Results: Radiomic features maintain excellent ICC values (>0.9) at a constant SNR with ARDL Low, but ICC values decrease with higher ARDL levelsImpact: This research highlights how deep learning image reconstruction can alter radiomic features and could help define a subset of stable features. The level of deep learning reconstruction applied is shown to have significant impact, even at constant SNR.
Model-based frequency-and-phase correction of 1H-MRS data with 2D linear-combination modeling
Authors: Simicic Dunja , Zöllner Helge , Davies-Jenkins Christopher , Oeltzschner Georg
Published: November 2024

Motivation: Retrospective frequency-and-phase correction (FPC) methods like spectral registration struggle at low SNR. Goal(s): To develop model-based correction FPC with simultaneous 2D fitting of all transients. To compare its performance to conventional FPC. Approach: Inclusion of all transients (without prior FPC) into a 2D linear-combination model with frequency and phase parameters for each transient. Comparison with conventional approach (spectral registration, averaging and 1D modeling). Outcome measures: frequency/phase/amplitude ground truth error & standard deviation, amplitude CRLB. Results: Model-based FPC is feasible and retrieves frequency/phase variations with high fidelity. At low SNR, frequency and metabolite amplitude estimation is more accurate and precise. Impact: Direct integration of frequency-and-phase correction into 2D linear-combination modeling is feasible and has great potential to improve metabolite level estimation for conventional and dynamic MRS data, especially for low-SNR conditions, e.g., long TEs, strong diffusion weighting, etc.
Self-Supervised SUper-Resolution ASL Enhancement based on Conditional Diffusion Models (SURED)
Authors: Xu Yunzhi , Shi Liangchen , Zheng Jiaxin , Li Jiaxin , Zeng Yu , Dai Weiying , Alsop David , Zhao Li
Published: November 2024

Motivation: Arterial spin labeling (ASL) MRI is a non-invasive technique used for measuring perfusion. However, the resolution of ASL is limited by its low SNR. Goal(s): to propose an ASL super-resolution method based on a self-supervised training strategy and the conditional diffusion model. Approach: Synthetic high resolution ASL images were generated by utilizing paired T1w images and low-resolution ASL images. A modified conditional diffusion model was trained to simultaneously achieve resolution enhancement and denoising. The proposed model was tested on simulated and volunteer images. Results: The proposed network demonstrates superior enhanced image details, improved SNR, and preserved original contrast in conventional low-resolution ASL images. Impact: The proposed method enhanced the ASL images without requiring the high-resolution ASL for training. It enables super-resolution ASL images from 4 minutes scans to approach those acquired in 17min.
ZOOM and enhance: ZOnally magnified Oblique Multi-slice for cardiac DTI with ultra-strong gradients
Authors: Mueller Lars , Afzali Maryam , Coveney Sam , Döring André , Fasano Fabrizio , Evans John , Teh Irvin , Dall'Armellina Erica , Szczepankiewicz Filip , Jones Derek , Schneider Jurgen
Published: November 2024

Motivation: Cardiac diffusion tensor imaging (cDTI) with echo-planar imaging (EPI) requires long readouts to avoid aliasing artefacts if 2D-selective rf-pulses are not available. These prolong the echo time (TE) and increasing sensitivity to off-resonance artefacts. Goal(s): The reduction of the excited and refocused field of view in the phase direction to reduce TE and sensitivity to image artefacts in cDTI. Approach: We combine ZOnally-magnified Oblique Multi-slice (ZOOM) EPI (i.e. tilting the slice orientation of the refocussing rf-pulse) with ultra-strong gradients. Results: We were able to reduce TE (from ~70 ms to 59 ms) in cDTI considerably by reducing the FoV and using strong gradients. Impact: We reduced the echo time in cDTI with ultra-strong gradients which will allow us to use more advanced diffusion acquisitions (higher b-values and/or different gradient waveforms) in the heart in vivo.
Dealing with Gradient Nonlinearities for High-Performance Gradient Diffusion MRI - Application to the Human Connectome Project (HCP) Dataset
Authors: Irfanoglu M. Okan , Beyh Ahmad , Thai Anh , Pierpaoli Carlo , Dell'Acqua Flavio
Published: November 2024

Motivation: Correction of gradient nonlinearity effects on diffusion-sensitization is important. However, not all software packages and/or diffusion models can incorporate spatially-varying bvals/bvecs information. Goal(s): Determine how significant nonlinearities are in the Human Connectome Project(HCP) dataset and investigate the feasibility of a spherical harmonics (SH)-based signal regeneration technique to directly incorporate nonlinearity effects and eliminate the need for additional information sharing. Approach: FA, MD and angular error maps from 200 subjects were compared using different formats of correction. Results: Nonlinearities are significant on HCP-dMRI data. The proposed SH-based signal regeneration approach allows the use of spatially invariant diffusion gradient tables with substantially reduced residual error. Impact: Diffusion models and software not designed to incorporate spatially-varying bvals/bvecs can now take advantage of gradient nonlinearity correction. A reprocessed version of HCP dMRI dataset will be made publicly available in this format, along with the conventional gradient deviation tensors.
DTI at four directions: an application at ultra-low field
Authors: Ametepe Joshua , Gholam James , Planchuelo-Gómez Álvaro , Padormo Francesco , Beltrachini Leandro , Cercignani Mara , Jones Derek
Published: November 2024

Motivation: The project aimed to tackle extended DTI scan durations, worsened by low SNR at low fields, striving to boost efficiency while preserving results' accuracy at lower SNR levels. Goal(s): The study sought to create an ML-based approach to shorten DT-MRI scans while ensuring reliable tensor estimation despite low SNR challenges at ULF. Approach: ML models, trained on synthetic data, predicted diffusivities and principal eigenvectors from four diffusion-weighted images, factoring in simulated noise and gradient rotations for noise and motion. Results: The models estimated diffusivities and fibre orientations with fewer data, showing promise for ULF tractography. Suggesting shorter DTI scans are possible at ULF. Impact: Our results are relevant to clinicians and researchers using low-field MRI, potentially enabling faster DT-MRI scans, opening avenues for efficient DTI in challenging settings, and making fibre mapping more accessible with reduced acquisition scan times.
3D Whole-heart T1-weighted Imaging in a Two-minute Free-breathing Scan for Radiofrequency Ablation Lesion Assessment
Authors: Patel Jaykumar , Krahn Philippa , Escartin Terenz , Sheagren Calder , Biswas Labonny , Barry Jen , Larsen Melissa , Wright Graham
Published: November 2024

Motivation: MRI-guided arrhythmia interventions require fast, high-resolution 3D images for comprehensive intraprocedural assessment of radio-frequency ablation lesion size and depth. Goal(s): Demonstrate similar volumetric lesion quantification between ground-truth respiratory-navigated Cartesian imaging and a novel free-breathing 3D cone-trajectory sequence. Approach: 4 healthy Yorkshire swine were ablated inside the MRI scanner, with 12 lesions prescribed in total. Volumetric analysis was performed for both Cartesian and 3D cones imaging. Results: Non-contrast T1-weighted 3D cones imaging with a highly accelerated scan time less than two minutes demonstrated similar lesion volumes to the slower 3D Cartesian sequence. Impact: Currently, non-contrast ablation lesion assessment in patients requires 5-10 minutes, depending on breathing patterns. The free-breathing 3D cone trajectory sequence presented here is a time-efficient method for lesion characterization that is feasible for intraprocedural applications.
Visual stimulus-evoked blood velocity responses at far upstream branches of the Posterior Cerebral Artery measured with phase-contrast fMRA
Authors: Hu Zhangxuan , Proulx Sebastien , Gomez Daniel , Varadarajan Divya , Bollmann Saskia , Tan Can Ozan , Gokcal Elif , Gurol M. Edip , Polimeni Jonathan
Published: November 2024

Motivation: Neuronal activity induces vasodilation in local arterioles that propagates to upstream large arteries, but the furthest detectable arterial dilation from the site of neuronal activity remains unknown. Goal(s): Detecting blood velocity responses at far upstream branches of the Posterior Cerebral Artery induced by a visual stimulus. Approach: In this study, a functional phase-contrast MRA technique was combined with a commonly used block-design stimulation paradigm to detect blood velocity responses. Results: About 10–20% velocity increases at the P2 segments of the Posterior Cerebral Artery were robustly observed. Impact: We demonstrate that neuronal activity-induced velocity response can propagate to large feeding arteries 6–7 cm from the visual cortex. The spatial and temporal properties of this propagation are important for understanding neurovascular coupling, autoregulation, and human fMRI.
Functional MRI of the nose
Authors: Ponticorvo Sara , Paasonen Jaakko , Stenroos Petteri , Paasonen Ekaterina , Filip Pavel , Rothman Douglas , Auerbach Edward , Garwood Michael , Metzger Gregory , Gröhn Olli , Michaeli Shalom , Mangia Silvia
Published: November 2024

Motivation: Standard fMRI techniques are unable to image the nasal cavity due to strong susceptibility artefacts. Goal(s): Our goal is to exploit ultrashort or zero echo time imaging to study functional connectivity of the nose. Approach: Resting-state fMRI was performed on 5 humans at 7T and 1 mouse at 9.4T. Independent component analysis (ICA) was performed, and ICA signals were analyzed within the context of other physiological signals. Results: Highly reproducible nose networks were observed in humans. The signal of one network strongly correlated with the autonomic nervous system activity. A pronounced nose network was also observed in the mouse. Impact: Ultrashort and zero echo time fMRI enables unprecedented performance for detecting functional nose networks providing the means to study nose activity and system-wide connections between central and peripheral nervous systems not currently possible with standard fMRI for the first time.
Lung infection alters perivascular aquaporin-4 in rat model of Alzheimer’s Disease detected by filter exchange imaging (FEXI)
Authors: Ohene Yolanda , Harris William , Powell Elizabeth , Smethers Katherine , Luka Nadim , South Kieron , Berks Michael , Lawrence Catherine , Parker Geoff , Parkes Laura , Boutin Hervé , Dickie Ben
Published: November 2024

Motivation: Pneumonia is more prevalent in Alzheimer’s Disease (AD) patients than in healthy elderly people, which may be due to blood-brain barrier (BBB) vulnerability. Goal(s): We assess whether filter exchange imaging (FEXI) can be used to understand the comorbid mechanisms occurring at the BBB with pneumonia and AD. Approach: We apply the FEXI technique to a TgF344-AD rat model of AD with induced Streptococcus pneumoniae lung infection. Results: FEXI detects significantly higher BBB water exchange in infected rats, with greater increase in the AD group, which significantly correlates to upregulation of hippocampus aquaporin-4 water channels, demonstrating the sensitivity of non-invasive FEXI to BBB alterations. Impact: This work could be translated to a clinical study using filter exchange imaging to assess whether Alzheimer’s Disease patients suffering with pneumonia also exhibit worse blood-brain barrier alterations than patients without pneumonia and healthy elderly people.
A Dynamic PVA Cryogel based Cardiac Phantom for 3D Temperature Validation of Radiofrequency Ablation
Authors: Brink Wyger , Doest Shanne , Rocchi Maria , Fresiello Libera , Boers Tim , Dagnino Giulio , Götte Marco
Published: November 2024

Motivation: MRI-guided radiofrequency ablation of cardiac arrhythmias holds promise to monitor the lesion formation process, however lacks validation tools. Goal(s): To develop a phantom for quantitative validation of catheter ablation procedures. Approach: In this study, a phantom composed of polyvinyl alcohol (PVA) with temperature sensitive properties has been developed for validation of the attained ablation temperature. Results: Post-ablation T1 maps were acquired and converted into temperature maps, based on the characterization of the reference phantoms. Impact: This work demonstrates a validation phantom for temperature validation after radiofrequency ablation procedures. This can offer an effective tool for MR methods development as well as procedural training, shortening procedural times and improving patient outcomes.
An Anthropomorphic Cardiac Phantom based on MR-Visible Additive Manufacturing for Development and Training of Interventional CMR Procedures
Authors: Nuvoli Sara , Sablé Quentin , Dagnino Giulio , Götte Marco , Kundrat Dennis , Brink Wyger
Published: November 2024

Motivation: Both technological development as well as procedural training of interventional cardiac MR (iCMR) procedures require anatomically realistic models. Animal studies are undesirable and becoming virtually impossible due to legal and ethical restrictions. Goal(s): To develop a realistic cardiac phantom that can be 3D printed using standard MR-visible support material. Approach: Material properties were determined and an anatomically realistic cardiac model was developed and constructed using 3D printing. Imaging features were assessed and an MR-guided cardiac intervention was simulated experimentally. Results: A simple and realistic MR-visible cardiac phantom has been presented for technological development and training purposes, to improve MR-guided cardiac interventions. Impact: The proposed phantom facilitates the development, testing and validation of novel technologies to improve MR-guided cardiac interventions, can be used for procedural training purposes, and may ultimately contribute to the improvement of clinical outcome.
Deep Learning-based Voxel-wise Estimation of Vessel Size Distribution from MR Gradient Echo Sampling of the Free Induction Decay and Spin Echo
Authors: Semmineh Natenael , Guha Indranil , Boxerman Jerrold , Quarles C.
Published: November 2024

Motivation: DSC-MRI is vital for diagnosing brain pathologies. Our goal is to harness GESFIDE MR signal evolution through deep learning (DL) to estimate vessel size distribution (VSD), which would allow us to explore deeper into the complexities of tumor vascular microstructure and other pathologies. Goal(s): Our objective is to assess the capabilities of GESFIDE in providing voxel-wise VSD estimate. Approach: We simulated GESFIDE signals with the FPFDM method. A DL network, VSD estimator (VSDE), was trained to estimate VSDs. Results: Our validation demonstrates GESFIDE's promise in assessing VSD as a distinct contrast mechanism, offering insights into tumor microstructure and pathologies. Impact: Our study reveals GESFIDE's potential for VSD estimation. Leveraging this unique contrast mechanism allows in-depth exploration of tumor microstructure and other pathologies through histogram analysis. Ongoing research aims to broaden VSD applicability and optimize GESFIDE parameters.
Simultaneous assessment of arterial pulsatility at different segments of cerebral arteries using multiband dual-VENC Phase-Contrast MRI
Authors: Tang Jianing , Jing Ning , Bi Xiaoming , Yan Lirong
Published: November 2024

Motivation: Arterial pulsatility is used for assessing cerebral vascular dysfunction. However, 2D PC-MRI and 4D flow have limitations including multiple separate scans, prolonged scan time, and flow saturation effects. Goal(s): Our study aims to develop a Multi-band Dual-Venc PC-MRI (MB-DV) senquence to measure pulsatility for larger and distal small vessels within short scan time. Approach: MB-DV scans were performed on 8 subjects to image velocity waveforms from ICA, M2, M3, LSA, and M4 simultaneously. For comparison, multiple standard single-slice PC-MRI were performed. Results: MB-DV PC-MRI successfully measured flow waveforms and provided reliable PI measurements with no significant difference from PI measured by standard PC-MRI. Impact: MD-DV PC-MRI enables simultaneously assessing arterial pulsatility at multiple vessel segments in the brain within a short scan time, which could be a potentially useful imaging tool to study pulsatility in the cerebral vascular tree for cerebrovascular and neurodegenerative diseases.
Aneurysm Wall Permeability in Assessing Non-Saccular Intracranial Aneurysms Rupture Risk Based on DCE-MRI
Authors: Li Yan , Xu Ziming , Dong Linggen , Wang Yajie , Liu Peng , Lv Ming , Chen Huijun
Published: November 2024

Motivation: Evaluating intracranial aneurysm (IA) risk holds significant clinical importance. Recent studies have proposed wall permeability (Ktrans) as a significant risk predictor, but its validity in non-saccular aneurysms remains unclear. Goal(s): Our study aims to predict non-saccular IA rupture by Ktrans from DCE-MRI. Approach: Ktrans was derived from the extended Kety/Tofts model. We analyzed correlation between Ktrans and IA size, the Population, Hypertension, Age, Size, Earlier Subarachnoid Hemorrhage, and Site (PHASES) score and other clinical factors. Results: Ktrans significantly related to IA size and PHASES score, but there was no correlation between Ktrans and intramural hematoma (IMH) and wall enhancement index (WEI). Impact: By analyzing correlation between aneurysm wall permeability (Ktrans) and other risk factors of non-saccular aneurysms rupture, we found that Ktrans might be a risk indicator for rupture of non-saccular IAs.
Discrimination Between Malignant and Healthy Breast Tissue Using Restriction Spectrum Imaging and MultiBand in a Screening Population
Authors: Loubrie Stephane , Howard Nicole , Batasin Summer Joyce , Ebrahimi Sheida , Yu Hon , Kuperman Joshua , Seibert Tyler , Guidon Arnaud , Ojeda-Fournier Haydee , Rakow-Penner Rebecca
Published: November 2024

Motivation: Diffusion weighted imaging (DW-MRI) holds great potential in improving specificity of findings detected on contrast enhanced breast MRI. A breast-specific Restriction spectrum imaging (BS-RSI) has been developed and proved to be able to discriminate cancers from benign lesions and healthy breast tissue. Goal(s): To evaluate BS-RSI’s performance in differentiating malignant from healthy fibroglandular tissue in a breast cancer screening dataset (BCS). Approach: We prospectively scanned 14 BCS patients with high-resolution multishell DWI added to standard BCS clinical protocol. Results: The BS-RSI model was able to discriminate healthy tissue from cancers in all C-compartments. Impact: Differences were observed between healthy tissue and malignant lesions in all C-compartments (p<0.01). ADC values were also significantly different in cancers than in healthy issue (p=0.044).
Intravoxel Incoherent Motion Diffusion-weighted imaging for evaluating the pancreatic perfusion in cirrhotic patients
Authors: Hu Ran , Yang Hua , Nie Lisha
Published: November 2024

Motivation: Pancreatic perfusion disturbances is found in cirrhotic patients with portal hypertension, which is correlated with reduced insulin secretion. Intravoxel incoherent motion (IVIM) can noninvasively reflect tissue microcapillary perfusion. Goal(s): To assess the characteristics of pancreatic perfusion in normal pancreas vs. cirrhotic patients using IVIM. Approach: 67 cirrhotic patients and 33 healthy subjects underwent IVIM on a 3.0 T MRI scanner. Results: The f value of pancreas in cirrhotic was lower than that in normal group (p = 0.01). In cirrhotic group, the f value of pancreas decreased with the increase of the Child-Pugh classification (R = -0.49, p = 0.00). Impact: IVIM-derived perfusion-related parameter (f value) could be helpful to evaluate pancreatic perfusion in liver cirrhosis. Pancreatic perfusion decreased is present in liver cirrhosis, and pancreatic perfusion tends to decrease with the increasing severity of hepatic function.
Time-Dependent Diffusion MRI for Quantitative Microstructural Mapping of Liver fibrosis to cirrhosis
Authors: Zhang Lijie , Liu Xiaoming , Zhang Xiaoxiao , Sun Peng , Shi Heshui
Published: November 2024

Motivation: The potential of characterizing cellular tissue microstructures using recently developed time-dependent diffusion MRI has been examined. Goal(s): However, its value in imaging liver fibrosis to cirrhosis remains unknown. Approach: In this study, we conducted a preclinical investigation using a dietondiethylnitrosamine (DEN)-induced rat liver fibrosis model with temporal diffusion spectroscopy (TDS) MRIResults: Our findings revealed that the diameter, intracellular volume fraction (Vin), and cellularity were associated with varying degrees of liver fibrosis. Moreover, the diameter and Vin demonstrated better discrimination ability in the model. Overall, these prognostic indicators exhibit significant potential for clinical application. Impact: This research highlighted the promising applications of temporal diffusion spectroscopy MRI in evaluating liver fibrosis. Our results indicated innovative noninvasive indicators for liver fibrosis, so as to early identify those who need to be referred to clinics for further assessment.
Intravoxel incoherent motion imaging with phase-cycled stimulated-echoes for simultaneous cardiac diffusion tensor and perfusion imaging
Authors: Munoz Camila , Lim Eunji , Ferreira Pedro , Pennell Dudley , Nielles-Vallespin Sonia , Scott Andrew
Published: November 2024

Motivation: Intravoxel incoherent motion (IVIM) imaging can provide information about cardiac microstructure and microvascular perfusion from a single examination. Current spin-echo based approaches for cardiac IVIM suffer from low perfusion sensitivity. Goal(s): We implemented a stimulated-echo (STEAM)-based method for cardiac IVIM and diffusion tensor imaging to enable accurate cardiac diffusion and perfusion imaging in vivo in the human heart. Approach: We introduced a novel IVIM-STEAM sequence incorporating phase cycling to obtain true b=0 s/mm2 images. Results: We tested the feasibility of our approach in 9 healthy subjects and obtained homogeneous perfusion and diffusion tensor maps, with values in the range described in the literature. Impact: Phase-cycled IVIM-STEAM provides good quality images that enable reliable fitting of cardiac diffusion tensor and perfusion parameters in healthy subjects. This approach may identify areas of microstructural aberration and perfusion abnormalities in patients with cardiovascular disease without exogenous contrast agents.
Noise-Robust Reconstruction for Accelerated MRI using Contrastive Learning
Authors: Kim Seonghyuk , Park Sung-Hong , Park HyunWook
Published: November 2024

Motivation: Deep learning-based accelerated MRI reconstruction methods have shown outstanding performance but do not consider noise. Corruption due to noise may lead to wrong diagnosis in clinical practices. Goal(s): Propose a noise-robust reconstruction method, which reconstructs noise-free full-sampled images from noisy undersampled data. Approach: A noise-robust reconstruction method is proposed using contrastive learning framework consisting of two stages. The first stage extracts feature representations related to the noise level, which is used in the second stage to reconstruct alias-free image. Results: Experiment results show that the proposed method provides robust reconstruction with limited training data, yielding superior image reconstruction compared to other reconstruction methods. Impact: The encoder trained in the first stage extracts representation features that contain content-invariant noise level information. Therefore, the trained encoder can be applied to other downstream tasks with limited amount of training data.
A Phase-Injected Complex Forward-Distortion Approach for Deep Unsupervised Correction of Susceptibility Artifacts in EPI
Authors: Alkilani Abdallah Zaid , Çukur Tolga , Saritas Emine Ulku
Published: November 2024

Motivation: Classical susceptibility-artifact correction methods are impractical in clinical settings given their computational burden. Goal(s): Fast and effective correction of susceptibility artifacts in EPI via physics-driven unsupervised deep learning by utilizing phase-injected complex-valued forward-distortion. Approach: Previous methods apply distortion correction on magnitude images, potentially yielding suboptimal performance near regions of signal dropout/pileup. We propose a novel model, compFD-Net, employing phase-injected complex forward-distortion that leverages a predicted phase image, additionally to the magnitude image and displacement field estimates, for improved capture of signal dropout/pileup artifacts in EPI images. Results: The proposed model boosts susceptibility-artifact correction performance, notably improving predicted image and field quality. Impact: Robust emulation of signal-dropout/pileup via the complex forward-distortion formulation boosts reliability in unsupervised artifact correction. compFD-Net facilitates rapid and performant correction of susceptibility artifacts in EPI, with possible impact in time-sensitive applications in clinical settings.
The value of non- and super-selective 4D-MRA in the assessment of internal carotid artery occlusion: comparison with TOR-MRA and CE-MRA
Authors: Zhang Jin , Sun Beibei , Wu Peng , Cheng Yongjun , Chen Weibo , Zhao Huilin
Published: November 2024

Motivation: 4D-MR angiography techniques have been developed to visualize both luminal stenosis and collateral circulation. Goal(s): This study aimed to assess whether 4D-MRA (4D-PACK and 4D-S-PACK) can be used as a noninvasive alternative to intraarterial DSA in internal carotid artery occlusion (ICAO). Approach: We prospective enrolled patients diagnosed as internal carotid artery occlusion by ultrasound or CTA and scheduled for intra-arterial DSA for this study. All patients underwent carotid multi-contrast MR imaging and DSA. Results: 4D-PACK and 4D-S-PACK can be used to diagnose ICAO, type of Circle of Willis (CoW), collateral circulation via CoW and blood flow direction of cerebral arteries. Impact: We offered a noninvasive alternative to intraarterial DSA to diagnose ICAO, type of CoW, collateral circulation via CoW and blood flow direction. By using three durations, we could greatly reduce time cost while ensuring the accuracy of 4D-PACK and 4D-S-PACK.
Accelerating whole brain vessel wall imaging of isotropic 0.4 mm3 on 5T by 10-fold using deep learning reconstruction
Authors: Jia Sen , Zhao Jiaying , Zhang Lei , Cheng Jing , Cui Zhuoxu , Li Ye , Liu Xin , Zheng Hairong , Liang Dong
Published: November 2024

Motivation: Whole brain vessel wall imaging (VWI) of isotropic 0.4 mm3 on 3T can’t utilize higher than 5-fold acceleration to reduce the scan time due to insufficient signal-to-noise. Goal(s): To achieve 10-fold accelerated whole brain VWI of isotropic 0.4 mm3 on the 5T scanner with a 48-channel transmit receive head coil. Approach: Deep learning (DL) reconstruction equipped with 3D convolution neural network was developed to alleviate the nonuniform noise amplified by SPIRiT reconstruction and the B1 inhomogeneity of 5T scanner. Results: The proposed DL SPIRiT reconstruction achieves 10-fold accelerated intracranial VWI scan on 5T in 6 minutes and give better VWI quality than 3T. Impact: This work develops a 10-fold accelerated whole brain vessel wall imaging of isotropic 0.4 mm3 in 6 minutes using deep learning (DL) unrolled SPIRiT reconstruction on the 5T scanner equipped with a 48-channel transmit receive head coil.
Assessing Cerebral Microvascular Compliance with High-Resolution VASO MRI at 7T
Authors: Guo Fanhua , Zhao Chenyang , Shou Qinyang , Shao Xingfeng , Wang Danny
Published: November 2024

Motivation: Compliance of the cerebral microvasculature is critical for brain hemodynamics but remains challenging to measure due to the complex cerebral vascular architecture and limitations in imaging technology. Goal(s): This study aims to utilize high-resolution VASO MRI across cardiac cycles to quantify microvascular compliance at 7T. Approach: Vascular compliance (VC) was defined as the ratio of CBV changes to changes in blood pressure across a cardiac cycle, which was proportional to the change in rCBV. Results: The middle layer of grey matter exhibits lower VC than superficial and deep layers. While higher VC was observed in the white matter (WM), especially deep WM. Impact: The proposed high-resolution VASO MRI offers a promising noninvasive method for estimating cerebral microvascular compliance.
Improved ex-vivo cerebral microbleed detection using self-supervised learning with fuzzy segmentation
Authors: Nikseresht Grant , Evia Arnold , Bennett David , Schneider Julie , Agam Gady , Arfanakis Konstantinos
Published: November 2024

Motivation: Accurate and efficient detection of cerebral microbleeds (CMBs) on postmortem MRI is necessary for MR-pathology studies on the relationship between CMBs and cerebral small vessel disease (SVD). Goal(s): The development and improvement of an automated detection framework for identifying cerebral microbleeds (CMBs) on MRI scans of community-based older adults. Approach: Fuzzy segmentation, a novel self-supervised auxiliary task based on CMB data synthesis, is proposed for pre-training a CMB detection model alongside other state-of-the-art SSL methods. Results: Self-supervised pre-training with fuzzy segmentation and rotation prediction led to an 11% increase in average precision for automated CMB detection on postmortem MRI. Impact: This study demonstrates a new state-of-the-art for postmortem CMB detection performance using self-supervised learning. Automated CMB detection on postmortem MRI will enable future MR-pathology studies into the links between CMBs and neuropathology observed at autopsy such as cerebral amyloid angiopathy.
Cerebral microbleed detection on susceptibility weighted imaging using solely artificial training data
Authors: Disselhorst Jonathan , Hall Caroline , Venkategowda Punith B , Griffa Alessandra , Dunet Vincent , Kober Tobias , Allali Gilles , Maréchal Bénédicte
Published: November 2024

Motivation: Cerebral microbleeds (CMBs) are small brain hemorrhages detectable with MRI associated with conditions like cerebral amyloid angiopathy. As their detection can be difficult, automated methods are needed for quick and precise detection and localization of CMBs. Goal(s): To propose an algorithm to detect CMBs. Approach: A neural network was trained on SWI/T2* images, with artificial bleeds generated and added during training. The model’s performance was tested on an independent test set with actual CMBs. Results: Despite the absence of real CMBs in the training data, the simulated bleeds provided sufficient information to train a model with good performance in the independent test set. Impact: We propose an algorithm that can help with the tedious radiological task of detecting cerebral microbleeds in the brain. We further demonstrate that a model trained solely on simulated bleeds can effectively detect actual microbleeds in real MRI data.
Optimizing an array-compressed parallel transmission system for dynamically RF-shimmed multislice brain imaging at 7T
Authors: Sappo Charlotte , Martin Jonathan , Sharma Anuj , Yan Xinqiang , Grissom William
Published: November 2024

Motivation: Ultra-high field imaging has several advantages like high SNR but require expensive RF shimming hardware to mitigate image shading artifacts caused by transmit RF field inhomogeneity. Goal(s): We developed a low-cost, highly customizable hardware solution to extend conventional RF shimming capabilities. Approach: We used array compressed parallel transmission system with an optimal network and fixed optimal weights designed to sit inside the bore with the coil arrayResults: The optimal network achieved about two-fold improvement in RF homogeneity compared to conventional circularly polarized network. Impact: This is the first time a design framework is described to optimize a full acpTx system for a desired imaging application. This hardware-centric approach has no additional pre-scan preparation or time considerations, lending itself to future clinical use.
Continuous In-line Monitoring of Perfusion Culture Viability with Contact-Free Magnetic Resonance Relaxometry
Authors: Gaensbauer Hans , Park Do Hyun , Bevacqua Alexander , Han Jongyoon
Published: November 2024

Motivation: Bioreactor cell density measurements currently require removing samples from the culture. This limits how many samples can be taken in a day and precludes real-time culture monitoring. Goal(s): We sought to take nondestructive cell density measurements every ten minutes without removing cells from the bioreactor. Approach: We built a system that takes T2 relaxometry measurements through the bioreactor’s silicone tubing every ten minutes and compared these to measurements taken by a commercial cytometry system. Results: The T2 relaxation data closely tracks the true cell density. Changes made to the culture are detected much more quickly by the relaxometer. Impact: Magnetic resonance relaxometry can be used to track bioreactor culture growth in real time, and makes it possible to identify and correct problems with the culture before the culture fails.
Dielectric Constant Measurement Method for High Permittivity Metamaterial Design in MRI
Authors: Chen Haiwei , Li Long , Zhang Xiaotong , Gao Yang
Published: November 2024

Motivation: High permittivity metamaterials have shown benefits in UHF MRI. However, precise dielectric constant measurement method for metamaterials is in absence, which may lead to bias in field manipulation. Goal(s): Developing a precise measurement method to better mimicking dielectric materials’ role in field manipulation, where samples are placed at the reactive near-field region. Approach: A microstrip line resonator was used to measure resonances of metamaterials. The resonance of dielectric materials with particular properties were chosen as the reference. Their difference in resonance frequency is required to be minimized. Results: Metamaterials designed using our approach exhibited superior accuracy in mimicking the target dielectric pad. Impact: The proposed method leads to better mimicking dielectric materials’ role in field manipulation at reactive near region. Metamaterials designed with this method exhibit enhanced B1 field and lower SAR, signifying a significant advancement in UHF MRI and promising precise diagnoses.
A system supervisor for safer parallel radiofrequency transmission MRI research
Authors: Tam Fred , Johnson Amanda , Leone Cathleen , Li Vicki , Yang Benson , Graham Simon
Published: November 2024

Motivation: Careful attention to safety is required for human research with novel parallel transmission (PTx) MRI technologies, particularly when they may cause harmful interactions with implanted devices. Goal(s): Our goal was to add an additional layer of safety during experiments with a custom PTx research platform. Approach: A system supervisor was created to monitor and react to a variety of amplifier faults and power conditions. Results: Initial tests showed that it could safely disable the PTx system with acceptably short and stable response times. Impact: A system supervisor was developed and tested for parallel transmission MRI research. The presence of such a supervisor may accelerate acceptance of PTx research systems by safety-conscious researchers and reviewers.
Testing platform for comparison between optical position tracking systems for motion correction
Authors: Silic Marina , Khan Yumna , Tam Fred , Graham Simon
Published: November 2024

Motivation: Optical position tracking (OPT) systems have been used successfully in motion correction, however, the comparison of new and pre-existing OPT systems remains challenging due to lengthy calibration procedures. Goal(s): Our goal was to create a testing platform capable of operating two systems concurrently for validating new OPT methods. Approach: Two dual camera systems were set up in the magnet room with a custom calibration stage. A phantom was used to transform coordinates from the camera to the magnet coordinate frame. Results: Camera calibration is limited to 30-minutes and outputs expected values. The pre-existing OPT system is accurate. Impact: With this testing platform, two optical position tracking (OPT) systems can be compared simultaneously on participants in the MR environment, rather than individually on the benchtop. This allows validation of OPT methods that are highly dependent on the testing environment.
Solenoid Float Cable Trap for MRI
Authors: Lu Ming , Wang Ruilin , Chai Shuyang , Wang Yuxiao , Yan Xinqiang
Published: November 2024

Motivation: Float cable trap has gained widespread adoption in RF coil owing to its unique advantages. However, conventional float trap requires considerable dimensions to achieve an excellent performance. Goal(s): This study is aiming to propose a novel float balun that can achieves high performance but with a compact size. Approach: It was inspired by the well-established solenoid trap. The solenoid structure leverages strong inductive coupling between the cable shield and float resonator, ensuring high efficiency and enabling a compact design. Results: With a compact size of 1.5x3cm2, solenoid float balun shows a measured CMRR of -31 dB (vs. -5 dB using conventional float trap). Impact: The novel, compact and float balun offers significant advantages in the field of MRI RF coil and RF safety. It reduces the overall bulkiness of the RF coil, enables the incorporation of a greater number of elements in receive arrays.
Evaluating the Performance of a Visual Coil in Physiological and Thermal Noise Dominated Regimes at Ultra-High Magnetic Field
Authors: Akbari Atena , Gilbert Kyle , Gati Joseph , Menon Ravi
Published: November 2024

Motivation: Advancements in array coil technology may not yield proportional gains in temporal SNR when physiological fluctuations are the primary noise source. Goal(s): We constructed a highly dense array covering the visual cortex and evaluated its noise characteristics and performance as a function of resolution. Approach: We evaluated image SNR and temporal SNR in vivo at 7 Tesla and produced BOLD activation maps using a visual paradigm. Results: The improvement in image SNR with decreased resolution was significantly higher than the improvement in temporal SNR. This suggests physiological noise dominance at the lower resolution. Impact: The image and temporal SNR offered by the visual coil allows for sub-millimeter resolution. Insight into the transition between thermal and physiologically noise dominated regimes will aid in reducing the risk of false positives in fMRI using this dense array.
Radiomics and Background Parenchymal Enhancement of Fibroglandular Tissue for Predicting Treatment Response in Triple-Negative Breast Cancer
Authors: Mohamed Rania , Son Jong Bum , Adrada Beatriz , Moseley Tanya , Whitman Gary , Candelaria Rosalind , Panthi Bikash , Chen Huiqin , Guirguis Mary , Leung Jessica , Hwang Ken-Pin , Pashapoor Sanaz , Patel Miral , Scoggins Marion , Le-Petross Huong , Lane Deanna , Perez Frances , Wei Peng , Xu Zhan , Tripathy Debu , Yang Wei , Yam Clinton , Ma Jingfei , Rauch Gaiane
Published: November 2024

Motivation: No imaging biomarkers are currently available for predicting response to neoadjuvant systemic treatment (NAST) in triple negative breast cancer (TNBC), contributing to toxicity to patients from ineffective treatment regimens. Goal(s): To evaluate if quantitative analyses of breast parenchyma can serve as noninvasive biomarker of treatment response in TNBC. Approach: Mean background parenchymal enhancement (BPE) and radiomic features of fibroglandular tissue from longitudinal DCE-MRI were evaluated using AUC analysis on a prospective cohort of 273 TNBC patients. Results: Four first order radiomic features were predictive of pCR with AUC>0.6. Multivariable radiomic models and BPE changes had AUC<0.6 for pCR prediction in TNBC undergoing NAST. Impact: Radiomic features from fibroglandular tissue and background parenchymal enhancement changes in ipsilateral and contralateral breasts using DCE MRI during treatment of triple-negative breast cancer patients were evaluated as noninvasive biomarkers for prediction of pathologic complete response to neoadjuvant systemic therapy.
Associations Between Prostate Cancer Lesion Location on mpMRI and Upgrading from Biopsy-Confirmed Histopathology to Final Pathology
Authors: Murthy Vishnu , Naim Sohaib , Babapour Sara , Raman Steven , Sung Kyung
Published: November 2024

Motivation: Given discrepancies in biopsy-confirmed histopathology and final pathology in prostate cancer, there is a clinical need to improve diagnosis and treatment planning using multiparametric MRI (mpMRI). Goal(s): To assess associations between mpMRI lesion location and upgrading of prostate cancer from biopsy-confirmed histopathology to final pathology. Approach: 191 patients who underwent both MRI-ultrasound fusion biopsy and prostatectomy were included. A modified χ² test assessed associations between mpMRI lesion location and upgrading from biopsy-confirmed histopathology to final pathology. Results: mpMRI lesions in the peripheral zone and posterior region were more likely to be upgraded than lesions in the transition zone and anterior region respectively. Impact: In patients who undergo MRI-ultrasound fusion biopsy and prostatectomy, mpMRI lesions in the peripheral zone and posterior region were more likely to be upgraded from biopsy-confirmed histopathology to final pathology than lesions in the transition zone and anterior region respectively.
Weakly Supervised Learning for Prostate Cancer Detection from Co-registered bi-parametric MRI and Patient’s Clinal Data
Authors: Zabihollahy Fatemeh , Grabke Emerson , Haider Masoom
Published: November 2024

Motivation: Bi-parametric MRI (bpMRI) is now part of the diagnostic workup for prostate cancer (PCa). Radiologists cognitively coregister bpMRI sequences when interpreting MRI. Conversely, machine learning (ML) algorithms have difficulty learning this implicit coregistration because of the distortion often present in diffusion-weighted images. Goal(s): Introduce a novel method for automated 1) bpMRI coregistration; and 2) detection of csPCa. Approach: A weakly supervised learning paradigm was employed for bpMRI co-registration. A combination of co-registered bpMRI and the patient’s clinical data was used for automated csPCa detection. Results: The proposed method achieved a true and false positive rate of 86% and 41% on 100 test cases. Impact: The obtained results demonstrated the value of co-registration and including patient clinical data for designing ML-based methods for automated csPCa detection. The proposed algorithm might improve the accuracy of reading bpMRI, thereby beneficial for patients with prostate cancer.
Quantitative assessment of breast tumor: comparison of different region of interest for synthetic relaxometry and diffusion measurement
Authors: Gao Weibo , Yang Quanxin , Wei Xiaocheng
Published: November 2024

Motivation: The lack of guidelines or recommendations for the ROI size of DWI and synthetic MRI. Goal(s): To investigate the effect of different ROI positioning methods on both ADC and synthetic MRI measurements and to subsequently evaluate the diagnostic performance of differently shaped ROIs. Approach: Four different ROI positioning methods on ADC and synthetic parameters measurements. Results: Square ROI showed the optimal AUC followed by freehand ROI. T2 + ADC were more diagnostic than ADC or T2 alone. Impact: The different ROI positioning methods used had a significant impact on the quantitative measurements and the performance in differentiating benign from malignant breast tumors.
Enhancing Breast Cancer Diagnosis through Deep Learning-Based DWI in Conjunction with Kaiser Score
Authors: Xia Wanjun , Zhang Yong , Wang Kaiyu , Cheng Jingliang
Published: November 2024

Motivation: While the Kaiser score serves a pivotal role in diagnosing breast cancer, it still encounters scenarios where false positives necessitate biopsy confirmation. Goal(s): This study aims to investigate approaches to enhance the diagnostic efficacy of the Kaiser score through MRI. Approach: Leveraging deep learning to enhance both the quality of DWI images and diagnosis, we sought more effective indicators in conjunction with the Kaiser score. Results: ADC values derived from DWI images reconstructed using deep learning, with a b-value of 800 s/mm², in tandem with the Kaiser score, significantly enhance the diagnostic performance nearing 1. Impact: Integrating DWI under deep learning with the Kaiser score can elevate the accuracy of differentiating between benign and malignant breast cancers to almost 100%, leading to substantial improvements in breast cancer diagnosis and a reduction in unnecessary biopsies.
A pilot study on the value of T1ρ mapping in preoperatively predicting the status of ER, PR, HER-2 and Ki-67 in breast cancer
Authors: Yang Lanqing , Hu Sixian , Zhang Xiaoyong , Zhang Xiaoxiao , Wu Zhigang , Xia Chunchao
Published: November 2024

Motivation: Noninvasive imaging methods capable of revealing molecular characteristic heterogeneity in breast cancer may help to compensate for the sampling errors of biopsy and guide tailored treatment. Goal(s): To explore the value of T1ρ mapping in preoperatively predicting the status of ER, PR, HER-2 and Ki-67 in breast cancer. Approach: This is a prospective diagnostic study. The differences of T1ρ values of tumors with different status of ER, PR, HER-2 and Ki-67 were compared between groups. Results: The T1ρ values showed significant differences in ER (P<0.01), PR(P=0.01), HER-2 (P=0.04), and Ki-67(P=0.02) negative and positive groups, with respective AUCs of 0.867, 0.79, 0.77, and 0.75. Impact: T1ρ mapping is expected to be a non-invasive tool to help evaluate ER, PR, HER-2, and Ki-67 status before surgery in breast cancer, and to provide assistance in individualized treatment.