bluuebunny/crossref_metadata_2025 huggingface dataset:
Towards Vector Encoding Methods on GPUs for Dictionary Search Memory Optimization
Authors: Zihlmann Gabriel , Salameh Najat , Sarracanie Mathieu
Published: November 2024

Motivation: GPU memory size can be a limiting factor for MRF dictionary search. Accelerated grouped search on GPUs was implemented previously without addressing GPU memory constraints. Goal(s): To investigate the feasibility of GPU memory footprint reduction through vector storage format compression. Approach: We built a CPU-only prototype search engine storing dictionaries as approximations enabling 2x and 4x memory reduction. Additionally, we implemented a post processing step that refines approximate results using the non-approximated vectors. Results: Refinement was effective at mitigating errors from vector compression. Overall, 2x group memory compression resulted in no quality loss and speed gains, while 4x compression resulted in speed loss. Impact: Dictionary search errors from vector storage compression of up to 4x have been found to be well mitigated by an uncompressed refinement step. This indicates the feasibility of implementing compression on GPUs to make efficient use of limited GPU memory.
Discordant Molecular, Imaging, and Survival Manifestations between Molecular Glioblastomas and Histological Glioblastomas
Authors: Park Yae Won , Ahn Sung Soo , Lee Seung-Koo , Jain Rajan
Published: November 2024

Motivation: Whether molecular GBMs are equivalent to early histological GBMs are controversial. Goal(s): To compare the clinicopathologic, imaging, surgical factors, and prognosis between molecular GBMs and histological GBMs. Approach: Retrospective chart and imaging review was performed in 974 IDH-wildtype GBM patients (43 molecular GBMs and 931 histological GBMs) from a single institution. Results: Molecular GBMs were significantly younger with higher rate of TERTp, higher incidence of gliomatosis cerebri and non-lobar location compared with histological GBMs, resulting in less aggressive resection. Survival was significantly longer in molecular GBM, which was attributed to the longer OS in histological grade 2 molecular GBMs with isolated TERTp mutation. Impact: Discordant clinicopathologic, imaging, and surgical features suggest molecular GBMs may not be equivalent to early histological GBMs. Histological grade 2 molecular GBMs with isolated TERTp mutation may not be sufficient to assume an aggressive clinical behavior similar to histological GBM.
Habitats based multiparametric magnetic resonance imaging radiomics model for prediction of endometrial cancer molecular subtypes.
Authors: Jin Wentao , Zhang He , Li Haiming , zhang Guofu , Li Wentao , Wang Tianping
Published: November 2024

Motivation: Endometrial cancer (EC) is a highly heterogeneous cancer comprising of both histological and molecular subtypes. The p53abn subtype is associated with a poor prognosis particularly. Goal(s): We aimed to develop habitats based multiparametric MRI radiomics model for the prediction of EC molecular subtype and evaluated the performance. Approach: Our study is a dual-center retrospective research. Results: Our habitats model demonstrated good performance in both internal and external validations. It exhibited higher efficacy compared to radiomics and clinical models. Impact: Using a non-invasive modality method to trigger these subtypes of EC as early as possible will aid clinicians to establish individual treatment. This research also marks the first use of habitat analysis in the study of EC.
Development and validation of an MRI-based radiomics nomogram to predict progression-free survival in patients with endometrial cancer
Authors: Liu Ling , Ji Xiaodong , Liang Caihong , Zhu Jinxia , Shen Wen
Published: November 2024

Motivation: There is no appropriate index to assess the prognosis of patients with endometrial cancer after surgery. Goal(s): Our goal was to explore the value of an MRI-based radiomics nomogram in predicting the progression-free survival (PFS) of patients with endometrial cancer. Approach: A nomogram was established using multivariable Cox regression by incorporating significant clinical-pathological predictors and radiomics signatures developed by another least absolute shrinkage and selection operator Cox regression. Results: The nomogram proved itself valuable in predicting PFS. Furthermore, the radiomics nomogram–defined risk stratification was associated with PFS. Impact: The novel nomogram may contribute to the precise stratification of patients with a high risk of progression. Therefore, more active follow-up and adjuvant therapy should be carried out after surgery to prolong and improve the patients' quality of life.
Utilizing XGBoost and LR to find the significant predictive factors of MR-guided high intensity focused ultrasound ablation in uterine fibroids
Authors: Li Zhihao , Li Chenxia , Liang Ting , Li Xiang , Wang Rong , Zhang Yuelang , Yang Jian
Published: November 2024

Motivation: MR-HIFU offers a new treatment option for women with uterine fibroids. However, there is currently a lack of quantitative models to predict the efficacy of MR-HIFU based on T2WI of fibroids for guiding preoperative clinical decisions. Goal(s): We hope to identify the most important predictive factors of MR-HIFU treatment for uterine fibroids and predict the efficacy using radiomics data combine with clinical data. Approach: We employed XGBoost and logistic regression (LR) to build two prediction models. SHAP values of XGBoost and LR coefficients were used to pinpoint significant predictive factors. Results: Both models achieved outstanding results and the significant predictive factors are consistent. Impact: Our excellent model results have identified the optimal predictive factors for assessing the efficacy of MR-HIFU in the treatment of uterine fibroids. These factors aid physicians in preoperative guidance and clinical strategy formulation, clarifying which patients will achieve better outcomes.
Highly Accelerated DCE-MRI Analysis with Deep Learning and Dispersion-applied AIFs
Authors: Zhao Kai , Pang Kaifeng , Babapour Sara , Wu Holden , Sung KyungHyun
Published: November 2024

Motivation: Existing techniques for DCE-MRI analysis is time-consuming and often assume a fixed arterial input function (AIF) across various locations and patients, leading to imprecise outcomes. Goal(s): 1) The development of a Deep learning model for fast DCE-MRI analysis, and 2) the design of location- and patient-specific AIFs. Approach: We use deep-learning model for fast DCE-MRI analysis, and propose to represent dispersion-applied AIF to allow for location- and subject-specific AIFs by interpolation between constant AIFs. Results: 1) Reduced per-patient processing time by one-tenth, 2) improved fitting accuracy, and 3) higher-contrast parameteric maps between the lesion and normal tisue. Impact: Other scientists, clinicians and patients may benefit from the faster processing time, and higher-contrast parametric maps for cancer diagnosis.
Advancing Prenatal Diagnosis of Placenta Accreta Spectrum Using MR T2-Weighted Radiomics Analysis: A Promising Approach for Early Detection
Authors: Zou Li , Xiao Zhen , Wang Lian , Wang Yang , Zhang Yu , Wei Wei , Xie Zhi , Liang Ting
Published: November 2024

Motivation: Enhancing Placenta accreta spectrum (PAS) prediction through advanced MR T2WI radiomics, improving outcomes for both mothers and babies. Goal(s): Improve PAS detection, enhance prenatal care, and reduce maternal and fetal risks. Approach: Employing radiomics analysis on MR T2-weighted imaging, we establish a predictive model for PAS, augmenting prenatal diagnosis. Results: The radiomics model exhibited exceptional accuracy and reliability, showcasing its potential for significantly enhancing PAS prediction in clinical practice. Impact: The successful implementation of this study stands to significantly enhance the early identification capacity for PAS and offer robust support for clinical decision-making. Such an achievement carries immense practical significance in addressing the potential rise in PAS cases.
MRI-based radiomics analysis of different components of primary lower extremity lymphedema
Authors: Liu Mengke , Tian Yuchi , Liang Xiaoyun , Wang Rengui
Published: November 2024

Motivation: Fluid and fat accumulation can be observed in MRI images of patients with PLEL; however, the microscopic characteristics of the different components of PLEL are currently unknown. Goal(s): This study aimed to explore the MRI radiomics features of different components of subcutaneous soft tissues in patients with PLEL, such as simple fat, mixed fat and water, fat interstitial edema, and effusionApproach: We propose a machine learning model to analyze the radiomics characteristics of different tissue components of lower extremity lymphedema in MRI. Results: he four-class model, using 15 selected radiomics features, shows outstanding performance with an overall accuracy of 0.866. Impact: The different components of subcutaneous soft tissues of PLEL patients, such as simple fat, mixed fat and water, adipose interstitial edema and effusion, have unique radiomic features.
FOCUS DWI and FOCUS DWI with deep learning-based reconstruction in breast MRI: A comparison with conventional DWI
Authors: Ming Yue , Yang Fan , Sun Jiayu , Zhang Bo , Liang Huilou
Published: November 2024

Motivation: DWI MRI is widely used in diagnosis and treatment evaluation of breast cancer but is prone to artifacts due to the breast's superficial location and large field of view (FOV). Goal(s): To investigate the feasibility and performance of reduced-FOV FOCUS DWI and FOCUS DWI with deep learning-based reconstruction (DLR) for breast MRI in Asian patients with small breast volumes. Approach: Both subjective and objective methods were used to compare the image quality of FOCUS DWI, FOCUS-DLR DWI and conventional DWI for breast cancer imaging. Results: Our results demonstrated that FOCUS-DLR DWI showed improved image quality and higher image scores compared to conventional DWI. Impact: FOCUS-DLR DWI enhances the visibility of lesion details, offering a novel approach to optimize breast MRI. This technique also holds promise for improving diffusion imaging in other regions of the human body, particularly small organs with surrounding tissue.
Patients initially diagnosed with MR-visible Gleason 6 prostate cancer: can AI predict upgrade to clinically significant cancer at follow-up?
Authors: Raichandani Surbhi , Besser Alexandra , Li Cynthia , Bhattacharya Indrani , Rusu Mirabela , Ghanouni Pejman
Published: November 2024

Motivation: Patients with low risk (Gleason 6) MR visible prostate cancer on initial biopsy are frequently upgraded to aggressive higher risk (Gleason 7 or higher) cancer. Identifying this progression early is difficult. Goal(s): To address this using a neural network trained with radiologist labels and whole mount histology of Gleason ≥7 cases to predict pathological upgrading in our cohort. Approach: DecNet was applied to the Gleason 6 initial MRIs to assess if the model could retrospectively identify patients with higher grade disease. Results: Our model had a sensitivity of 84.6% for lesions upgraded to Gleason 7, outperforming PSA density, lesion size and ADC values. Impact: These results showcase the potential of our model in unveiling higher-grade prostate cancer within lesions initially diagnosed as lower grade on pathology.
Multi-Region MRI-Based Radiomics Predictive Model for METTL3 Expression in Hepatocellular Carcinoma
Authors: Chen Feng , Luo Shishi , Dong Mengying , Huang Weiyuan , Liao Yuting , Yu Xiao , Xu Yongzhou
Published: November 2024

Motivation: To the best of our knowledge, our study is the first to non-invasively assess methyltransferase-like 3 (METTL3) expression in hepatocellular carcinoma (HCC) using multi-sequence MRI-based radiomics. Goal(s): To develop a multi-region radiomics-based predictive model using multi-sequence MRI to assess METTL3 expression in HCC. Approach: Three Models (Tumor, Tumor-Expand5, Tumor-Expand10) were constructed and evaluated. Results: The Tumor-Expand5 model showed the highest efficacy with an AUC of 0.71 in the test set, outperforming both the Tumor and Tumor-Expand10 models. Multi-sequence MRI-based radiomics models hold the potential for preoperatively assessing METTL3 expression in HCC, aiding clinical decision-making. Impact: This study introduces a novel multi-region radiomics-based model for predicting METTL3 expression in HCC using multi-sequence MRI. The results demonstrate the potential of radiomics, with an emphasis on the Tumor-Expand5 model, highlighting its promise for enhancing clinical decision-making in HCC.
Accelerated Spiral Ultrashort Echo Time (Spiral-UTE) MRI of the Lung Using Deep Learning
Authors: Pei Haoyang , Wang Yao , Feng Li
Published: November 2024

Motivation: Spiral-UTE MRI has been proposed for more efficient lung imaging to permit breath-hold ultra-short echo time acquisition of the lung. It is more valuable to further accelerate the acquisition of the spiral-UTE MRI of lung images, thus enabling shorter breath-holds and higher spatial resolutions. Goal(s): This work presents a deep learning based method to enable the reconstruction of spiral-UTE MRI of lung images from accelerated spiral k-space. Approach: An unrolled network was developed for reconstructing images from the accelerated non-cartesian k-space. Results: The unrolled network allows for higher reconstruction quality for spiral-UTE MRI of lungs compared to a standard U-Net. Impact: The proposed unrolled network tailored for spiral MRI reconstruction enables reconstruction of accelerated spiral-UTE of lung images to allow shorter breath-holds and higher spatial resolutions. This reconstruction technique can also extended to other multi-coil non-cartesian accelerated MRI reconstructions.
Feasibility study of stack-of-spirals free breathing ultrashort echo time for lung MRI in patients with malignant tumors at 1.5T
Authors: Fu Qing , Wu Jia-wei , Sun Xin , Meng Xue-ni , Xiao Ao-dong , Yin Ting , Benkert Thomas
Published: November 2024

Motivation: Ultrashort echo time (UTE) MR imaging has been developed to visualize lung tissues, but is limited by long acquisition time and capacity for displaying pulmonary tissues. Goal(s): 3D free breathing stack-of-spirals UTE (spiral-UTE) could provide a higher readout efficiency, but its comprehensive performance with and without contrast material for follow-up of patients with identified malignant tumors at 1.5T has not been reported. Approach: Aim to investigate clinical feasibility of spiral-UTE compared to enhanced-VIBE with CT as the reference. Results: Spiral-UTE was superior with improved image qualities for depicting bronchi and lung parenchyma than enhanced-VIBE in lung screening during the oncology patient follow-upsImpact: Spiral-UTE provides a potential alternative to CT for lung follow-up with a significantly superior quality visualization of the pulmonary anatomy than routine enhanced-VIBE
Automated Respiratory Pattern Analysis for Dynamic MRI of the Lung in Post COVID-19 patients at 0.55 T
Authors: Luthra Prerna , Pei Haoyang , Mikheev Artem , Rusinek Henry , Bruno Mary , Sood Terlika , Wang Yao , Chandarana Hersh , Feng Li
Published: November 2024

Motivation: There is a lack of non-invasive approaches for quantitatively analyzing the patterns of respiration motion in proton MRI in patients with lung diseases such as post-COVID symptoms. Goal(s): The goal of this work is to determine whether post-COVID-19 patients can be classified as having either long COVID or no symptoms by analyzing dynamic MRI motion fields within various regions of lungs. Approach: A deep learning-assisted framework was developed for automatically analyzing localized respiratory motion in lung MRI. Results: The framework was able to successfully categorize patients into different categories based on their degrees of no symptoms using the proposed image analysis framework. Impact: This work develops an automated framework that can aid radiologists in quickly determining not only the presence but also the severity of long COVID. It can also be extended for applications in other lung diseases.
NMR-based metabolomics for early detection of prostate cancer biomarker/s
Authors: Kumar Virendra , Kumar Pradeep , Kumar Rajeev , Thulkar Sanjay , Sharma Sanjay , Khan Maroof
Published: November 2024

Motivation: Current diagnostic methods cannot predict the aggressiveness of prostate cancer (PCa) at a treatable stage of disease. Goal(s): To interrogate tumorigenesis of PCa and AI/ML techniques to NMR-based targeted blood plasma metabolomic profiling analysis for prediction of PCa. Approach: Use AI/ML approaches to NMR metabolic profiling for PCa patient blood plasma data analysis Results: Phosphocreatine, choline, 3-hydroxybutyrate, taurine and glucose showed highest discriminate using CFS, PLS-DA, OPLS-DA, random forest models. Impact: It will pave way to enhance understanding of cancer pathogenesis and biomarker/s identification and early detection systems.
Application of a deep learning reconstruction to routine liver 3D LAVA-Flex acquisitions
Authors: Milshteyn Eugene , Ghosh Soumyadeep , Nakrour Nabih , Mercaldo Nathaniel , Roberts Nathan , Tsai Leo , Guidon Arnaud , Harisinghani Mukesh
Published: November 2024

Motivation: Fat suppressed T1 images, such as LAVA-FLEX, are routinely used in liver imaging, but can suffer from SNR and IQ issues. Goal(s): Our goal was to validate application of 3D deep learning to 3D LAVA-FLEX in routine adult liver imaging via a reader study and noise characterization. Approach: DL and conventionally reconstructed images were assessed across several IQ attributes (motion, ringing, edge, vessel) by two radiologists. Noise characteristics were evaluated by calculation of total variation and edge detection. Results: Based on the calculated odds ratios, the radiologists preferred DL across the various IQ attributes, with decreased noise and improved sharpness in DL images. Impact: The application of 3D DL to routine 3D LAVA-FLEX imaging provides increased diagnostic quality, and has the potential to improve routine abdominal care in patients who can't hold their breath.
Tensor PCA denoising improves reproducibility of quantitative Multi-Parameter Mapping.
Authors: Herthum Helge , Hetzer Stefan
Published: November 2024

Motivation: Quantitative biomarkers such as proton density, longitudinal- and transverse relaxation rates, and magnetization transfer saturation measured by multi-parameter mapping (MPM) reflect microstructural tissue characteristics. However, resolution and reproducibility of MPM are constrained by SNR limits of the imaging process resulting in long acquisition times. Goal(s): We investigate tMPPCA denoising performance for improved SNR and reproducibility of quantitative maps at different voxel volumes for a clinically optimized protocol. Approach: Denoising of repeated MPM acquisitions at different voxel volumes and its effects on model-based SNR and reproducibility of parameter maps. Results: Denoising increase SNR of quantitative maps up to sixfold while scan-rescan fluctuations were halved. Impact: Tensor-based MPPCA denoising of multi-contrast images enhances SNR of quantitative Multi-Parameter Mapping which results in improved reproducibility with greater benefits for high-resolution applications. Consequently, tMPPCA denoising could be considered for future studies and for retrospectively enhancing sensitivity of MPM studies.
Insights for in vivo MR axon radius mapping from simulations based on large-scale histology
Authors: Mordhorst Laurin , Edwards Luke , Morozova Maria , Jäger Carsten , Rusch Henriette , Weiskopf Nikolaus , Morawski Markus , Mohammadi Siawoosh
Published: November 2024

Motivation: To understand deviations between axon radii from in vivo MR experiments and histology. Goal(s): To assess the sensitivity of the intra-axonal MR signal to the axon radius; to assess the impact of confounders (extra-axonal signal and Rician noise bias); to discuss deviations between in vivo MR experiments and simulations. Approach: We simulated MR signals for axon radii distributions from large-scale histology with and without confounders; we compared radii fitted to in vivo MR experiments and simulations. Results: Large MR radii are inherently underestimated; confounders are expected to further bias MR radii and can potentially explain deviations between in vivo MR experiments and simulations. Impact: We reveal an inherent bias in the MR axon radius model for in vivo measurements. Furthermore, we identified two main confounders that can significantly narrow the dynamic range of MR radius measurements and reduce sensitivity to small-axon radii regions.
Quantification of 1H-MRSI metabolites in mild traumatic brain injury using relaxation correction from MRF
Authors: Chen Anna , Klein Andrea , Gerhalter Teresa , Gajdošík Martin , Dehkharghani Seena , Peralta Rosemary , Gajdošík Mia , Tordjman Mickael , Sheriff Sulaiman , Ahn Sinyeob , Bushnik Tamara , Zarate Alejandro , Silver Jonathan , Im Brian , Wall Stephen , Madelin Guillaume , Kirov Ivan
Published: November 2024

Motivation: Accurate 1H-MRS metabolite quantification requires adjustments for metabolite and water signal relaxation, which are challenging to measure. Goal(s): Our goal was to examine whether an MRF-based correction of subject-specific water relaxation times, applied to patients with mild traumatic brain injury (mTBI), yields results and effect sizes comparable with a conventional literature-based correction approach that utilizes one set of relaxation times for all subjects. Approach: MRF and 1H-MRSI were acquired in 21 mTBI patients and 20 age-matched controls for quantification of metabolite concentrations in six white matter regions. Results: Both methods yielded similar findings with comparable effect sizes across all metabolites in all regions. Impact: In the context of intermediate TR and short TE, the standard absolute quantification method based on one literature-derived set of water relaxation times for all subjects may be appropriate for studying white matter metabolism in mild traumatic brain injury.
What is the optimal myelin marker? Evidence from multi-contrast MRI, histology, and deep learning
Authors: Liang Zifei , Lee Choong , Minteer Jennifer , Kim Yongsoo , Zhang Jiangyang
Published: November 2024

Motivation: Deep neural networks trained with MRI and myelin histology data offer enhanced sensitivity and specificity compared to conventional MRI markers, yet their inner workings remain unknown. Goal(s): To elucidate the relationships between MRI and myelin histology. Approach: We mapped multi-parametric MRI data of developing mouse brains and their myelin content onto a 3D manifold after dimension reduction and defined the relationships between MRI and myelin signals in a piecewise fashion. Results: Our findings revealed how the relationships between multiple MRI parameters and tissue myelin content evolved throughout brain development. Impact: We have developed a novel data-driven approach to characterize the complex relationship between MRI parameters and myelin. The results suggest that multi-parametric MRI is necessary for accurate myelin mapping.
A mathematical description of the changes of quantitative MPM parameters in ex-vivo whole brain human brains during fixation and hydration
Authors: Fritz Francisco , Streubel Tobias , Mushumba Herbert , Püschel Klaus , Mohammadi Siawoosh
Published: November 2024

Motivation: Relaxation rates in the in-vivo human brain are strongly different to their counterparts in formalin-fixed postmortem tissue. Goal(s): To model the changes of the relaxation rate parameters for different tissue stages from in-vivo to ex-vivo: unfixed, during fixation and during hydration. Approach: The multi-parameter mapping (MPM) protocol was used to measure the changes of five whole-human brains across the aforementioned tissue stages, and different saturation models were tested to describe relaxation parameter changes during fixation. Results: The MPM parameters varied strongly per tissue stage, and a mathematical description of the change of the MPM during fixation was found. Impact: We characterised the MPM parameters during the fixation and hydration process across the entire brain and propose a mathematical model to describe the changes. This information could facilitate translating microstructure-mapping methods from fixed ex-vivo tissue samples to in-vivo application
Clinical Validation of Deep Learning-Accelerated vs. Wave-CAIPI Post-Contrast 3D-T1 MPRAGE for Evaluation of Intracranial Enhancing Lesions
Authors: Tabari Azadeh , Vejdani-Jahromi Maryam , Lang Min , Nickel Dominik , Lo Wei-Ching , Clifford Bryan , Conklin John , Huang Susie
Published: November 2024

Motivation: Deep learning (DL)-enabled reconstruction has emerged as a promising approach to accelerate MRI exams; however, the performance of DL-accelerated 3D sequences for the detection of intracranial enhancing lesions has not been clinically investigated. Goal(s): To evaluate post-contrast DL-accelerated 3D-T1-MPRAGE compared to state-of-the-art Wave-CAIPI accelerated 3D T1-MPRAGE for evaluation of intracranial enhancing lesions. Approach: Two neuroradiologists performed head-to-head evaluation of 115 cases of post-contrast DL- vs. Wave-CAIPI-MPRAGE for visualization of dural, parenchymal, leptomeningeal, and ependymal enhancement; sharpness; noise; artifacts; and overall diagnostic quality. Results: Highly accelerated post-contrast DL-T1-MPRAGE achieved noninferior image quality to the standard clinically validated Wave-CAIPI accelerated sequence. Impact: Deep-learning-accelerated post-contrast 3D T1-MPRAGE demonstrates robust diagnostic quality in visualizing enhancing intracranial pathology in all compartments while maintaining similar perception of noise and artifact. DL offers a powerful approach to accelerating post-contrast 3D T1-MPRAGE for clinical and research studies.
Intravoxel incoherent motion analysis after preoperative endovascular embolizationvascular embolization in supratentorial meningioma
Authors: Li Ling , Miyati Tosiaki , Ohno Naoki , Sakai Mizuki , Kasai Harumasa , Mase Mitsuhito
Published: November 2024

Motivation: Some studies have reported decreased tumor volume and blood flow after preoperative endovascular embolization in meningioma, but it remains unclear how the meningioma and peritumoral edema are altered after endovascular embolization, and a practical evaluation method has not been established. Goal(s): We investigated IVIM analysis to evaluate perfusion and diffusion simultaneously before and after endovascular embolization in supratentorial meningioma. Approach: We assessed IVIM parameters (FD*, F, D*, and D) before and after endovascular embolization in supratentorial meningioma. Results: FD* and F were significantly lower after endovascular embolization in meningioma than those before the endovascular embolization. Impact: The IVIM analysis makes it possible to evaluate perfusion and diffusion simultaneously after preoperative endovascular embolization in supratentorial meningiomas in a short time.
Transient-state CSF suppression for time-efficient, high-resolution 3T whole-brain 3D relaxometry
Authors: Liu Hongyan , Versteeg Edwin , Fuderer Miha , van der Heide Oscar , van den Berg Cornelis A.T. , Sbrizzi Alessandro
Published: November 2024

Motivation: Cerebrospinal fluid (CSF) pulsation artifacts using 3D unbalanced gradient-echo sequences hinder the accuracy of whole-brain relaxometry measurements. Goal(s): To develop a CSF-suppressed 3D MR-STAT sequence and eliminate CSF pulsation artifacts, allowing for fast, high-resolution whole-brain relaxometry with enhanced image quality. Approach: The time-varying RF flip-angle train is optimized with an additional CSF suppression constraint to achieve both high quantitative parameter encoding capability and low CSF signal intensity. Results: The new CSF-suppressed 3D MR-STAT sequence has been validated on healthy volunteers, demonstrating effective mitigation of CSF ghosting artifacts, and allowing whole-brain relaxometry acquisition within 5.5-minute scan time. Impact: The proposed MR-STAT sequence with CSF suppression requires a short scan time (less than 6 minutes) and shows robustness in whole-brain 1mm3 isotropic relaxometry, and therefore shows the ability to adopt quantitative imaging in broad neuroscientific and clinical applications.
SSIMPLE: Scan-SpecIfic parameter MaPping from contrast weighted images with self-supervised LEarning
Authors: Dogangun Fatih , Jun Yohan , Bilgic Berkin
Published: November 2024

Motivation: There is rich and complementary information in clinical images, which may lend itself to the estimation of relaxometry parameters. Goal(s): To develop a self-supervised network that can estimate T1, T2, and PD maps from contrast-weighted images with high fidelity. Approach: We developed a scan-specific self-supervised model (SSIMPLE) that harnesses Bloch equations and estimates parameter maps from multi-contrast images without the need for a training dataset and additional constraints. Results: High-fidelity T1, T2, and PD maps with minor biases 4.5%, 11.76%, and 15.45%, respectively, were obtained using the proposed self-supervised network. Impact: Using the developed scan-specific self-supervised neural network, SSIMPLE, high-fidelity parameter maps can be estimated from clinically routine contrast-weighted images without the need for an external training dataset or additional constraints.
Safety of Intraoral Coils: Accuracy of Simulations
Authors: Tesfai Agazi , Reiss Simon , Bock Michael , Özen Ali Caglar
Published: November 2024

Motivation: In dental applications intraoral coils (IOCs) offer higher SNR than external surface coils, but the details of RF-induced heating of an intraoral coil dependent on many factors such as coil size, shape, incident E-field, surrounding tissue, and acquisition protocol. Goal(s): This study investigates the effect of anatomical model complexity on SAR simulations for IOCs. Approach: Numerical simulations were compared to electric field mapping and temperature measurements to evaluate RF-induced heating of various IOCs. Results: RF heating and SAR simulations of IOCs can be performed with limited tissue models, but the required complexity of the tissue model depends on the coil type. Impact: The complexity of numerical simulations can be reduced to limited tissues while maintaining accuracy of SAR estimation to determine the safety of intraoral coils through hotspot detection.
Reduction of RF-heating on bilateral DBS leads using two channel RF-shimming on 3T MRI
Authors: Van Speybroeck C.D.E. , Roskamp W. , Vinke R.S. , van der Graaf M. , Brink W.M.
Published: November 2024

Motivation: Bilateral deep brain stimulation (DBS) surgery would benefit from safe intraoperative 3T MRI using conventional scanners. Goal(s): Our goal was to develop a method to determine patient-specific safety constraints to scan a bilateral lead configuration with minimal RF-heating, by minimizing RF-induced currents at maximal B1. Approach: We scanned a phantom with two leads in different configurations, measured local heating and determined the transimpedance. Results: We could determine the phantom-specific safety constraints and find a low-SAR sequence meeting the constraints, after optimizing the two channel RF-shimming to minimize RF-heating by minimizing RF-induced currents at maximal B1. Impact: The proposed method can be used to minimize heating of a bilateral lead configuration and estimate patient-specific safety constraints, but further research has to be conducted to be able to use it during deep brain stimulation surgery.
Heating Observed in EEG Amplifiers with Different EPI Slew Rates at 7T
Authors: Waggoner R. Allen , Uji Makoto , Ueno Kenichi , Suzuki Chisato , Sato Fumiaki , Li Xuemei , Kamiguchi Hiroyuki , Tamaki Masako
Published: November 2024

Motivation: Evaluate the impact of EPI read-out gradient slew-rate on EEG amp heating, for EEG-fMRI experiments at 7T. Goal(s): Identify slew-rates that will minimize heating of EEG amps, for EEG-fMRI experiments at 7T. Approach: Test a range of EPI slew-rates while measuring the temperature of the EEG amp, during phantom tests at 7T. Results: EPI read-out slew-rates of 120, 88, 61 and 46 T/m/s were tested and the slew-rates of 61 and 46 T/m/s were found to induce minimal heating of the EEG amp during EEG-fMRI experiments at 7T. Impact: These results should help neuroscientists doing EEG-fMRI studies at 7T, to conduct their experiments without damaging the EEG amps.
Investigation of Radiofrequency-induced Heating of Abandoned Cardiac Lead Wire with Conductive Caps of Different Electrical Conductivities
Authors: Yatsushiro Satoshi , Kuroda Kagayaki
Published: November 2024

Motivation: To reduce radiofrequency (RF)-induced heating of abandoned implantable cardiac leads equipped with plastic caps, which remarkably increase heat generation around the lead tip. Goal(s): RF-induced heating with various electrical property of the cap was investigated to find optimal condition. Approach: Tissue heating around the lead wire with caps of electrical conductivity ranging over 10-6 to 10+6 S/m were compared in the ASTM phantom model by FDTD simulation, and validated by experiments. Results: Tissue heating around the lead tip clearly decreased and became similar to the uncapped case, when the conductivity was higher than 10-1 S/m, which was approximately the same as the media. Impact: Simulations and experiments on abandoned cardiac leads revealed that the "electrically conductive cap" dramatically reduces RF-induced heating around the lead tip. Such cap may protect the lead from body fluid infiltration into the insulation material while also contribute to MR-safety.
Investigation of RF-Induced Heating of Active implantable Medical Devices with Shorted Leads Under 1.5T MRI
Authors: Yang Lijian , Wang Qingyan , Wilcox Matthew , Zhang Wenhao , Chen Ji
Published: November 2024

Motivation: In active implantable medical devices (AIMDs), electrical shorts may occur either between electrodes of two leads in multi-lead systems (inter-lead shorts), or within the AIMD header due to incomplete lead insertion (intra-lead shorts). Goal(s): Whether these shorts invalidate the originally-established MR safety condition for RF-induced heating?Approach: The RF-induced heating for three shorted-lead cases were investigated. Device heating models (transfer functions) and RF-induced heating were measured for 1.5T MR systems. Results: Though the intra-lead short case exhibited a similar transfer function (TF) and RF-induced heating to non-shorted leads, inter-lead shorts had significant impact on the RF-induced heating. Impact: This research improves MRI safety for patients with active implantable medical devices (AIMDs) by studying the effects of electrical shorts.
Study of RF-Induced Heating in Active Implantable Medical Devices with Open Impedance Leads at 1.5T MRI
Authors: Wang Qingyan , Yang Lijian , Wilcox Matthew , Zhang Wenhao , Chen Ji
Published: November 2024

Motivation: The RF-induced heating from different configurations of open impedance leads connected to a commercial Pulse Generator (IPG) was investigated. It was observed the RF-induced heating from open impedance leads can behave differently from an intact lead. Goal(s): Heating in the distal end of open impedance leads need to be estimated. Approach: Transfer functions and heating measurements were performed. Results: Most open-impedance configurations led to increased electrode heating with the highest heating occurring when only a single channel of the eight-channel lead was left intact. Additionally, significant differences in heating were found depending on the location of the open impedance. Impact: Patients with open impedance fractured leads could take MR scan with the help of this study.
RF-induced Heating Estimation of a Stent in a 3T MRI using Transfer Function Approach with a table-top E-field generator
Authors: Jeong Hongbae , Guag Joshua , Kumar Ananda
Published: November 2024

Motivation: The TF validation process is time-consuming work. Here, we reduced the burden of TF approach using a low power tabletop E-field generator. Goal(s): Using the TF approach to estimate the RF-induced heating of the stent tip at 3T using a E-field generator and compared predicted values against the estimated values. Approach: TF of the stent was measured via piX method at 128MHz . TF was validated by exposing the device under diverse test fields using table-top E-field generator. The body coil was used to evaluate the TF approach. Results: The stent tip heating was estimated using TF approach with a low-power E-field generator. Impact: TF approach could aid estimating the RF-induced heating, not only active implantable device, but also elongated passive implantable device. A compact E-field generator can be used for TF validation.
Real-time SAR Supervision for a 32-channel RF Transmit System with Virtual Observation Points
Authors: Fiedler Thomas , Grimm Johannes , Klein Christoph , Kratzer Fabian , Mayer Falk , Orzada Stephan , Schweins Luisa , Ladd Mark
Published: November 2024

Motivation: Real-time local SAR supervision is a major obstacle in pTx system especially since the computational demand scales proportionally with the number of RF channels. Goal(s): To develop a safety supervision that monitors 32 transmit channels phase sensitive and perform local SAR calculation with a higher number of VOPs. Approach: Directional couplers are placed in the transmit path, digitizers sample the RF signal, and a GPU performs the local SAR calculation. Results: This study demonstrates real-time RF supervision for a 32-channel pTx transmit system with local SAR calculation using 600 VOPs on a single GPU. Impact: This study demonstrates real-time RF supervision for a 32-channel pTx transmit system with single-GPU-based local SAR calculation using 600 virtual observation points.
Combining the advantages of FDTD and FEM for MRI RF safety evaluation using the Huygens method
Authors: Tao Yun , Wong Marshal , Medina Pablo , Lemdiasov Rosti , Huang Shao Ying , Guerin Bastien
Published: November 2024

Motivation: MRI RF safety evaluation requires simulations of many coils and body models, which is typically done using FDTD or FEM. These have complementary weaknesses, therefore we propose to combine them. Goal(s): We propose a simulation process that combines FEM with FDTD via the Huygens’ principle. Approach: We first compute the incident electric field created by the coil alone using FEM. Second, we compute field perturbations created by the body model using an FDTD Huygens’ implementation. Results: The proposed method agrees well with the all-FDTD calculation at 64 MHz, but allows more efficient and flexible modelling of RF coils. Impact: We propose a robust and flexible simulation method combining the strengths of FEM and FDTD via the Huygens principle that expedites coil and implant (ISO/TS10974 Tier 3) RF safety evaluations.
Numerical Evaluation of the Specific Absorption Rate Change of Transmit Arrays at 9.4T due to Presence of B0 Field Probes
Authors: Berezko Egor , Solomakha Georgiy , Avdievich Nikolai , Bause Jonas , Lindig Tobias , Scheffler Klaus
Published: November 2024

Motivation: Imperfections of B0-field can reduce the MR image quality. To track the B0 change during imaging, NMR field-probes inside the RF coil can be used. Insertion of the field-probes may lead to alteration of the B1+ and pSAR. Goal(s): To evaluate alterations of B1+ field and pSAR of the 16-channel Tx-array in the presence of NMR field-probes at 9.4T. Approach: To reach the goal, we simulated the Tx-part of the 16Tx32Rx array loaded by a phantom and human voxel model in the presence of 11 field-probes. Results: Insertion of field-probes led to a small drop of B1+ and a slight change of pSAR. Impact: We showed that inserting field-probes doesn’t significantly alter the B1+ field and pSAR of the 16Tx32Rx array coil at 9.4T. Therefore, field-probes can be safely used in-vivo to evaluate B0.
Radiological Image Quality Assessment of Synthetic 3T MRI Image from 64mT MRI
Authors: Islam Kh Tohidul , Zhong Shenjun , Zakavi Parisa , Kavnoudias Helen , Farquharson Shawna , Durbridge Gail , Barth Markus , McMahon Katie , Parizel Paul , Egan Gary , Dwyer Andrew , Law Meng , Chen Zhaolin
Published: November 2024

Motivation: The necessity to enhance the quality of portable low-field MRI images, which are crucial for wider accessibility but lack high-resolution, drives this research. Goal(s): This study aims to determine whether Synthetic 3T technology can elevate low-field image quality to that of high-field 3T standards, making it diagnostically adequate. Approach: We employed a generative network to transform low-field images to higher quality, maintaining pixel-level accuracy and structural integrity. The study involved a paired dataset from 37-healthy subjects and an evaluation on 20-images by two neuroradiologists. Results: Synthetic 3T demonstrated improved clarity, structure, and contrast, aligning closer to the 3T-quality than the original low-field images. Impact: This investigation highlights the potential of Synthetic 3T to bridge the gap in portable MRI imaging, enabling broader clinical utility. Further research could pivot on its application in pathological cases, with an aim to enhance diagnostic capabilities in resource-limited settings.
A Decision Tree Diagnostic Scheme Based on Multi-label Deep Learning Network for Classification of Adult-type Diffuse Gliomas
Authors: Xu Xinyi , Zhang Liqiang , Pan Hongyu , Gao Jueni , Wang Linling , Liu Zhi , Cao Xu , Wen Ming
Published: November 2024

Motivation: Genetic biomarkers and WHO grading of gliomas are critical for the classification of glioma subtypes, treatment planning and survival prognosis. Goal(s): The aim of this study is to apply DL network for non-invasive prediction of multiple genes and classification of subtypes. Approach: A decision tree diagnostic scheme based on multi-label DL network was constructed to classify adult-type diffuse gliomas into 5 subtypes based on the 2021 WHO classification of tumor of the CNS, combining the WHO grading and 3 genetic biomarkers status. Results: The model we developed can reclassify adult-type diffuse glioma with a diagnostic accuracy of 94.4%. Impact: Based on the 2021 WHO CNS tumor classification, this study applies multi-label deep learning to reclassify adult-type diffuse gliomas, which can be helpful for patients to obtain preoperative diagnosis and precise treatment.
A fusion model based on preoperative MRI radiomics features can predict potential bone invasion of meningiomas
Authors: Zhang Hongjing , Zhang Jing , Su Xiaorui , Li Shuang , Yue Qiang , Liang Xiaoyun
Published: November 2024

Motivation: Bone invasion is a common problem in meningioma surgery and is associated with patient prognosis. However, 10-26% of patients with potential bone invasion are difficult to identify by preoperative imaging. Goal(s): To develop an artificial intelligence based preoperative diagnostic model. Approach: Radiomics features were extracted from preoperative, contrast-enhanced T1-weighted (T1C) and T2-weighted (T2) MR images of 296 patients. Candidate radiomics were selected by applying feature reduction and 5-fold cross validation. Results: A more accurate and robust fusion radiomics model was built based on T1C and T2 MR images with AUC of 0.755. Impact: Our results have demonstrated that radiomics features extracted from T1C and T2 MR images may be employed as effective preoperative biomarkers for predicting potential bone invasion in meningiomas.
Impact of image resolution on neural network based automatic scar segmentation in cardiovascular magnetic resonance imaging
Authors: Margolis Isabel , Hoh Tobias , Weine Jonathan , Joyce Thomas , Manka Robert , Weisskopf Miriam , Cesarovic Nikola , Fuetterer Maximilian , Kozerke Sebastian
Published: November 2024

Motivation: Deep learning for myocardial scar segmentation offers an alternative to time-consuming and observer-dependent semi-automatic approaches. Goal(s): The objective of this study was to assess the impact of effective image resolution on neural network training for ventricular scar segmentation. Approach: Convolutional neural networks were trained on magnetic resonance images with constant matrix size and field-of-view but differing resolutions, and tested on a range of resolutions to investigate the effects. Results: Neural networks trained on a specific resolution indicated a bias of the scar area estimation when employed to lower -or higher-resolution images. Deploying a network trained on multiple resolutions resulted in reduced resolution dependency. Impact: The effective image resolution, with constant matrix size and field-of-view, should be considered when training a segmentation model to alleviate unwanted bias in the estimation. Training on multiple resolutions has been shown to increase network precision and robustness.
High-resolution 3D aortic segmentations from standard 2D CMR localisers: an AI application to clinical care and population studies
Authors: Jiang Yue , Punjabi Karan , Knight Daniel , Bhuva Anish , Pierce Iain , Yao Tina , Hughes Alun , Steeden Jennifer , Muthurangu Vivek , Davies Rhodri
Published: November 2024

Motivation: Undiagnosed aortic aneurysms can be fatal. We aim to use machine learning to measure the aorta from standard CMR localisers, allowing screening and characterisation of aneurysms without the need for additional sequences. Goal(s): We aim to generate accurate 3D segmentations (1-1.5mm slice thickness) from standard 2D trans-axial SSFP localisers stacks (10-12mm slice thickness). Approach: We trained an AI model using high-resolution segmentations alongside simulated low-resolution images (2D localisers). This enables the model to predict high-resolution segmentations from unseen, low-resolution images by generalising from the learned patterns. Results: Our model shows promising performance in generating high-resolution segmentations from various unseen low-resolution validation dataset. Impact: With our model, the dilated aorta can be identified from routine CMR scans without the need for extra sequences. Additionally, 3D aorta morphology information can be obtained from previous clinical CMR studies or population studies without additional cost.
The Effects of Simulated SAR Data Processing Methods and Network Parameter Tuning on Gridding Artifacts and Network Estimation Accuracy
Authors: Blanter Katherine , Plumley Alix , Kopanoglu Emre
Published: November 2024

Motivation: Gridding artifacts in neural network estimated images are common and could inhibit neural network estimation accuracy. Goal(s): The goal is to discover which parameters are responsible for gridding artifacts, and whether the artifacts inhibit estimation quality. Approach: We test the effects of simulated body models, neural network parameters, and postprocessing methods on gridding artifacts, and their effect on overall neural network estimation accuracy in the context of local specific energy absorption rate (SAR) matrices, a patient safety concern for MRI scanning. Results: Altering neural network parameters affects the presentation of gridding artifacts the most. Eliminating gridding artifacts improves network estimation accuracy. Impact: Researchers working with computer vision whose images experience a gridding artifact can inform their neural network parameter tuning efforts with the results of this exploratory study.