bluuebunny/crossref_metadata_2025 huggingface dataset:
Accelerated Glioma characterization with VERDICT MRI: a comparison between deep learning and non-linear least squares fitting
Authors: Figini Matteo , Palombo Marco , Bailo Michele , Callea Marcella , Mortini Pietro , Falini Andrea , Alexander Daniel , Cercignani Mara , Castellano Antonella , Panagiotaki Eleftheria
Published: November 2024

Motivation: Complex multi-compartment models of diffusion MRI, as the recent adaptation of VERDICT-MRI for brain tumours, can provide important microstructural information, but traditional fitting is time-consuming and may not be accurate. Goal(s): To explore the feasibility of deep-learning-based fitting of VERDICT for brain tumours. Approach: We fit the VERDICT model to data from 15 glioma patients using both traditional and deep-learning approaches. We compared the resulting parameters between the two methods and with histology. Results: VERDICT estimates from deep-learning and traditional fitting showed a good correlation and reflected histology features. The deep-learning fitting was much faster once the model was trained. Impact: We have successfully used deep learning to fit the complex VERDICT model for brain tumour microstructure. As deep-learning fitting is much faster and potentially more precise than traditional methods, this could facilitate the clinical application of VERDICT for brain tumours.
Whole tumor amide proton transfer–weighted imaging histogram analysis to predict prostate cancer bone metastases: a preliminary study
Authors: zhang li , zhang jing , li longchao , ai kai , zhu yi
Published: November 2024

Motivation: Accurate prediction of prostate cancer(PCa) bone metastases can be challenged by radiologists. The utility of APTw histogram methods for evaluating bone metastases involvement in PCa is still unclear. Goal(s): The purpose of this study was to evaluate APTw-derived whole-tumor histogram analysis parameters in predicting PCa bone metastases. Approach: Diagnostic performance was evaluated using ROC analysis and the AUC comparisons were conducted using the DeLong method. Results: Our preliminary research showed that whole-tumor histogram analysis of APTw images combined with clinical factors (tPSA, cT stage) showed good diagnosis efficiency in predicting PCa bone metastases while using 68Ga-PSMA PET/CT as the reference standard. Impact: Our preliminary research showed that whole-tumor histogram analysis of APTw images combined with clinical factors(tPSA, T stage) showed good diagnosis efficiency in predicting prostate cancer bone metastases while using 68Ga-PSMA PET/CT as the reference standard.
Assessment of RAGE-DIAPH1 interaction agonist induced changes to cardiac and muscle metabolites in a murine model of Type-1 Diabetes
Authors: Menon Rajiv , Hasan Syed , Schmidt Ann Marie , Ramasamy Ravichandran , Regatte Ravinder
Published: November 2024

Motivation: Diabetes Mellitus causes systemic changes in lipids of multiple organs. Previous studies have shown that receptor for advanced glycation end products (RAGE) and its tail binding partner diaphanous 1 (Diaph1) are key mediators of metabolic changes in T1D mice. Goal(s): To use 1H-MRS and CSE-MRI to investigate the metabolic effects of the RAGE-DIAPH1 interaction antagonist, RAGE229, in T1D murine hearts and hind limb. Approach: 18 mice were divided into 3 cohorts (Rage229/+db, RegDiet/+db, RegDiet/-db) and scanned using 1H-MRS and CSE-MRI. Results: Inhibition of RAGE-DIAPH1 interaction by RAGE229 leads to significant reduction in triglyceride levels in hearts and hind limb of T1D mice. Impact: The results of this study set the stage for further testing of RAGE229 as potential therapeutic adjuncts in alleviating metabolic dysfunction in T1D.
Cardiovascular magnetic resonance-derived left ventricular hemodynamic force among patients with systemic lupus erythematosus
Authors: Hou Yangzhen , Zhou Hui , Luo Jing , Yang Ji , Zhang Huiting , Bi Xiaoming
Published: November 2024

Motivation: Cardiovascular involvement in systemic lupus erythematosus (SLE) patients is common and serious; early and accurate identification of the cardiac dysfunction is crucial. Goal(s): Our goal was to investigate the potential role of hemodynamic force (HDF) in early recognizing impaired cardiac function in SLE. Approach: Left ventricular (LV) HDF and strain analysis were performed based on long-axis cine imaging using feature-tracking in 36 SLE patients and 34 healthy volunteers. Results: The force ratio between transverse and longitudinal direction was larger in SLE patients with no difference of strain analysis, indicating that the orientation of HDF altered in LV. Impact: This study demonstrated that HDF analysis can early detect systolic dysfunction in SLE in advance of significant decrease of LV EF, which may contribute to the early diagnosis of cardiac involvement and prognosis improvement of patients.
Assessing different CKD stages by cardiac magnetic resonance native T2 mapping and NT-pro BNP, a polit study
Authors: Chen Yating , Zhang Jingyu , Li Junjie , Li Zhiyong
Published: November 2024

Motivation: We can use the positive correlation between natural T2 value and NT-pro BNP to achieve early clinical detection, diagnosis and treatment of myocardial abnormalities caused by chronic kidney disease, thereby improving the prognosis of patients with CKD. Goal(s): Our aim was to analyze the relationship between native T2 value and NT-pro BNP over stage progression. Approach: CMR is performed on a 3.0-T scanner. Cardiac data were assessed using CVI.42. Pearson, Spearman coefficients and ANOVA analysis were used to analyze the data. Results: There was a statistically significant difference in autologous T2 value between different CKD groups. and NT-pro BNP. Subgroups stratified by eGFR showed a significantly differential correlation between NT-pro BNP and native T2 value. Impact: We can use the positive correlation between natural T2 value and NT-pro BNP to achieve early clinical detection, diagnosis and treatment of myocardial abnormalities caused by chronic kidney disease, thereby improving the prognosis of patients with CKD.
Hemodynamics and vessel morphology in Fontan patients at a high-altitude surgical center
Authors: Platnick Carson , Spears Hannah , Schofield Samuel , Drysdale Nicolas , Frank Benjamin , Stone Matthew , DiMaria Michael , Romanowicz Jennifer , Zablah Jenny , Morgan Gareth , Kay Joseph , Kheyfets Vitaly , Fonseca Brian , Browne Lorna , Moghari Mehdi
Published: November 2024

Motivation: There is currently limited data examining longitudinal changes in hemodynamics and vascular properties in Fontan patients living at altitude. Goal(s): We aimed to assess hemodynamic parameters and vessel morphology in Fontan patients at a single high-altitude surgical center and assess correlations between these parameters and post-Fontan outcomes. Approach: We retrospectively reviewed patients undergoing Fontan operation from 1999 to 2021. We analyzed data from cardiac magnetic resonance imaging and cardiac catheterization. Results: Our findings highlight post-Fontan changes in flow, pressure, and area. The changes in systemic and caval flow, along with pressure variations, offer insights into the effects of Fontan circulation. Impact: This study advances our understanding of Fontan circulation at high-altitude, paving the way for improved prognostic information and long-term outcomes in this patient population.
Right Ventricular Perfusion Reserve Predicts Response to Pulmonary Thromboendarterectomy
Authors: Fan Lexiaozi , Benefield Brandon , Cuttica Michael , Mylvaganam Ruben , Malaisrie S. Chris , Avery Ryan , Schimmel Daniel , Raza Yasmin , Durkin Jordyn , Hsu Li-Yueh , Nieto Donny , Lee Daniel , Freed Benjamin , Kim Daniel
Published: November 2024

Motivation: Little is known about the right ventricular (RV) perfusion reserve in patients with chronic thromboembolic pulmonary hypertension (CTEPH) and whether pulmonary thromboendarterectomy (PTE) surgery improves RV perfusion. Goal(s): This study sought to assess whether PTE improves RV perfusion in CTEPH patients and whether RV perfusion reserve correlates with invasive pulmonary hemodynamics. Approach: We prospectively enrolled 6 CTEPH subjects undergoing PTE, performed stress-rest MRI and right heart catheterization, and calculated RV myocardial perfusion reserve pre and post PTE. Results: RV perfusion reserve is improved in CTEPH with PTE, correlates with invasive pulmonary hemodynamics, and may serve as a non-invasive marker for monitoring treatment efficacy. Impact: This study demonstrates feasibility of utilizing right ventricular perfusion reserve as an imaging marker for evaluation of pulmonary thromboendarterectomy (PTE) in chronic thromboembolic pulmonary hypertension (CTEPH) patients.
Preparation Scheme Optimization for Abdominal MRF
Authors: Griesler Tom , Hamilton Jesse , Kaplan Sydney , Cummings Evan , Seiberlich Nicole , Cruz Gastao
Published: November 2024

Motivation: Optimal sequence design for magnetization-prepared abdominal MRF is largely unexplored. Optimization could potentially result in reduced scan times and increased precision and accuracy. Goal(s): Investigate the predictive power of CRLB-based cost functions on the performance of magnetization-prepared MRF sequences. Approach: Sequences with different preparation schemes were selected based on CRLB cost functions and evaluated in simulations and in vivo experiments. In the resulting T1 and T2 maps, precision and accuracy were compared to the relative performance predictions of the cost functions. Results: The CRLB depends on selection and placement of preparation modules and is correlated with the quantification performance of the resulting sequences. Impact: Preliminary simulations and experiments indicate that the CRLB correlates well with precision and accuracy in magnetization-prepared abdominal MRF, and may be used for the optimization of these sequences.
Scalable Zero-Shot Self-Supervised Learning for Accelerated MR Fingerprinting
Authors: Liu Yilin , Chen Yong , Yap Pew-Thian
Published: November 2024

Motivation: Collecting ground truth tissue property maps for training deep learning networks for MR Fingerprinting reconstruction can be challenging. Goal(s): We aim to reduce reliance on training datasets for deep learning based MR Fingerprinting. Approach: We propose a novel zero-shot self-supervised MRF framework that requires only the undersampled k-space measurements. We develop strategies to rapidly retrieve fingerprints from the dictionary for efficient network training. Results: Our method demonstrates promising results on 2x and 4x accelerated MRF without requiring supervised learning based on ground truth tissue property maps, laborious reconstruction, explicit dictionary matching, and network pre-training. Impact: Our preliminary results validated the feasibility of zero-shot self-supervised MRF reconstruction from undersampled MRF data with the help of physics-based constraints.
Unsupervised individual-specific in vivo parcellation of the human cerebral cortex using magnetic resonance fingerprinting
Authors: Moinian Shahrzad , Vegh Viktor , Reutens David
Published: November 2024

Motivation: In vivo MR image resolution limitations hinder precise characterization of individual gray matter microstructure. Addressing this unmet need can particularly enhance neurosurgery precision and assist neurological disorder monitoring. Goal(s): We aimed to investigate the feasibility of developing an unsupervised method for individual-specific voxel-wise cortical mapping by extending the MR fingerprinting (MRF) residual analysis framework. Approach: We employed k-shape clustering to leverage the wealth of cortical area-specific microstructural information in voxel-wise MRF residual timeseries for training an unsupervised model. Results: High sensitivity (75.7%) and specificity (88.3%) of our method demonstrated the feasibility of unsupervised in vivo microstructural cortical mapping using on MRF residual signals. Impact: Our unsupervised MR fingerprinting residual-based method offers a transformative approach to individual-specific human cortical mapping, potentially enhancing neurosurgical precision and neurological disorder diagnosis. This lays the foundation for exploring novel questions in microstructural neuroimaging and cerebral cortex developments in individuals.
Cluster-Based Low-Rank Regularization for Cardiac Rosette MR Fingerprinting
Authors: Cummings Evan , Cruz Gastao , Kaplan Sydney , Richardson Jacob , Hamilton Jesse , Seiberlich Nicole
Published: November 2024

Motivation: Breathholds limit the amount of data which can be acquired in cardiac MRF, which can impact the precision of fat/water separated T1, T2, and T2* maps. Goal(s): We developed a regularization method to reconstruct accurate maps from multi-echo cMRF data without introducing blurring into the resulting tissue property maps. Approach: A k-means cluster-based approach is used to group the signal evolutions during reconstruction and a low-rank constraint is applied to each cluster. We compared our method to existing approaches in 23 healthy volunteers. Results: This approach can be used to generate accurate myocardial T1, T2, and T2* maps using rosette MRF data. Impact: Traditional cardiac MRF reconstructions can fail when working with multi-echo rosette MRF data due to insufficient sampling. We developed a reconstruction method which enables T1, T2, and T2* maps to be collected in a single breathhold without compromising accuracy.
Factorized Spatiotemporal Convolutions for Simultaneous Multislice Magnetic Resonance Fingerprinting
Authors: Lu Lan , Liu Yilin , Zhou Amy , Yap Pew-Thian , Chen Yong
Published: November 2024

Motivation: Quantitative MR imaging with volumetric coverage is relatively slow, which hinders its clinical evaluation. Goal(s): To leverage simultaneous multislice MR Fingerprinting (SMSMRF) and deep learning to achieve high multi-band factors for rapid quantitative imaging. Approach: We introduced a Spatio-Temporal UNet (STUN) method to exploit both spatial and temporal correlations of signals in SMSMRF to largely accelerate data acquisition. Results: High multi-band factors (3 and 4) with an additional 4x acceleration along the temporal dimension were achieved, providing rapid data sampling of 1.5~2 sec per slice for quantitative brain imaging. Impact: The developed SMSMRF method holds great potential to accelerate quantitative imaging for challenging subjects, f.g. pediatric patients.
Cardiac MR Fingerprinting with a Low-Rank Reconstruction for Simultaneous T1, T2, and T1ρ Mapping
Authors: Kaplan Sydney , Cruz Gastao Lima da , Hamilton Jesse , Seiberlich Nicole
Published: November 2024

Motivation: Quantification of T1, T2, and T1ρ may provide insight into myocardial fibrosis without the need for gadolinium-based contrast agents. Goal(s): This study seeks to present and validate a rapid, single breath-hold cMRF approach for simultaneously mapping T1, T2, and T1ρ. Approach: A cardiac-gated MRF sequence was designed to measure T1, T2, and T1ρ and was tested in simulation, phantom, and six healthy subjects. Results: The proposed T1/T2/T1ρ cMRF technique yielded accurate high-resolution T1, T2, and T1ρ maps in simulation, phantom, and in vivo. Impact: Cardiac Magnetic Resonance Fingerprinting can be used to quickly, accurately, and simultaneously map T1, T2, and T1ρ, and has the potential to quickly probe myocardial fibrosis without contrast.
Trade-off between Readout Duration and Concomitant Field Effect in 3D MRF at 0.55T
Authors: Zhu Zhibo , Lee Nam , Nayak Krishna
Published: November 2024

Motivation: The use of long readouts in 3D MRF improves SNR efficiency but also suffers from stronger concomitant field effects. Goal(s): To evaluate the trade-off between readout duration and concomitant field effects in 0.55T 3D MRF and evaluate mitigation using the MaxGIRF framework. Approach: We performed 0.55T 3D MRF scans of 14 readout durations on the NIST/ISMRM phantom and compared reconstructions with and without concomitant field effects compensation. We analyzed T1 array's standard deviations as a function of readout duration. Results: With correction, MRF T1 standard deviation reduced to stability due to improved SNR efficiency of long readouts, however, broke due to uncorrectable blurring. Impact: Practical trade-off between a readout duration and concomitant field effects are demonstrated. 0.55T 3D MRF benefits from a longer readout duration (6.1-14.7ms) provided a concomitant field effect correction.
Robust Highly-accelerated MR Fingerprinting Using Transformer-based Deep Learning
Authors: Huang Peizhou , Eck Brendan , Liu Ruiying , Li Hongyu , Yang Mingrui , Kim Jeehun , Zhang Xiaoliang , Li Xiaojuan , Ying Leslie
Published: November 2024

Motivation: MR fingerprinting (MRF) conventional reconstruction methods need a substantial reconstruction time and memory space. We aim to propose a novel deep-learning method for accelerated MRF reconstruction. Goal(s): To achieve more accurate quantification reconstruction for T1 and T2 from highly undersampled MRF data. Approach: A novel training process was also proposed to construct reliable training data with noise-like aliasing artifacts boosted by Transformer network without need to know the structure information. Results: Experimental results demonstrate that the proposed method achieves more accurate quantification for T1 and T2 than pattern matching and DRONE. Impact: The proposed method can generate more accurate quantitative maps for highly accelerated MRF data that enable clinical use in real application. In addition, the proposed training process is robust to different structures in the image to be reconstructed.
DCE-MRI radiomics models predict IDH mutation in adult diffuse gliomas
Authors: ZHU ZHENGYANG , Cao Zehong , Zhou Jianan , Ye Meiping , Yang Huiquan , Zhang Xin , Shi Feng , Zhang Bing
Published: November 2024

Motivation: IDH mutation status of glioma have important influence on its occurrence and prognosis. Goal(s): To build radiomics models in DCE-MRI for predicting IDH mutation in adult diffuse gliomas. Approach: Several groups of features were extracted through multiparametric image: 1) Automatically calculated DCE-MRI metrics; 2) Structural MRI radiomics features, and 3) DCE-MRI radiomics features. Z-score normalization was used for feature normalization. Mann–Whitney U test was used for tumor selection. Stochastic gradient descent was used for machine learning classifier. Results: We achived an AUC of 0.874 for model combing structural-MRI, DCE-MRI radiomics and automatically-calculated DCE-MRI metrics. Impact: DCE-MRI radiomics models demonstrated great potential to predict IDH mutation status in Gliomas.
A new approach for automatic segmentation of prostate and its lesion regions on the magnetic resonance imaging
Authors: Ren Huipeng , Wan Qinyun , Wei Xiaocheng , Tian Hongzhe , Li Shan , Wang Huan , Ren Zhuanqin
Published: November 2024

Motivation: In recent years,many neural network models based on prostate lesion segmentation in magnetic resonance images have different stability and diagnostic efficiency. Goal(s): we want to get an automatic segmentation model with high performance for the prostate and its lesion region. Approach: Our Network DCNN is inspired by the U-Net model with the encoding-decoding path as the backbone,importing dense block,attention mechanism techniques,and group norm-Atrous Spatial Pyramidal Pooling,these could be broadly used to improve the capability of CNN. Results: Compared to the state-of-the-art models,FCN,U-Net,U-Net++,and ResUNet.The segmentation performance of DCNN for prostate lesions On the MR DWI image swas better than the other models. Impact: The DCNN model with dense block, convolution block attention module, and group norm-Atrous Spatial Pyramid Pooling performed well in the segmentation of the prostate and its lesion regions. which supports its potential to assist prostate disease diagnosis in clinical medicine.
Prediction of Postsurgical Progression of Prostate Cancer Using MRI Cancer Risk Maps
Authors: Gibbons Matthew , Cowen Janet , Carroll Peter , Cooperberg Matthew , Noworolski Susan
Published: November 2024

Motivation: Biochemical recurrence (BCR) remains a significant concern for patients after radical prostatectomy. Goal(s): This study’s objective was to determine whether automated mpMRI cancer risk maps could predict postsurgical prostate cancer (PCa) progression (Biochemical Recurrence (BCR). Approach: Derived lesion volumes and mpMRI parameters from the cancer risk maps were used to analyze factors for BCR. Results: A decision tree model for BCR was generated with sensitivity = 0.81, specificity = 0.82, and ROC AUC = 0.85. The prediction results indicate the potential of mpMRI and PCa risk maps to improve prediction of BCR after prostatectomy. Impact: In this study we used preprostatectomy multiparametric MRI (mpMRI) cancer risk maps to analyze potential predictors of postsurgical prostate cancer (PCa) progression (Biochemical Recurrence (BCR) or treatment failure). A decision tree model was generated with ROC AUC 0.85.
The value of synthetic MRI for early prediction of NAC response in breast cancer: a complement to apparent diffusion coefficient in radiomics
Authors: Zhang Yanni , Du Siyao , Xie Lizhi , Zhang Lina
Published: November 2024

Motivation: Contrast-free sequences is receiving increasing attention. As a novel technology, radiomics analysis of Sythetic MRI(SyMRI) in breat treatment has not been widely explored. Goal(s): To analyse the radiomics features extracted from SyMRI and its complementary value to conventional ADC sequence. Approach: Recursive feature elimination (RFE) was used to select features and support vector machine (SVM) was used to build models. The model performance was assessed by receiver operating characteristic (ROC) analysis and compared by DeLong test. Results: Delta-radiomics models based on SyMRI sequences outperformed the single time-point models. The SyMRI sequence that complements the conventional ADC is delta-T2 mapping. Impact: Radiomics modle generated from delta-T2 mapping showed stable performance and complementary value to ADC sequence. Once scan-multiparameter and contrast-free SyMRI can obtain the comparative or even elevated value as ADC in early prediction of NAC response in breast cancer.
A machine learning approach based on multiparametric MRI to identify the risk of non-sentinel lymph node metastasis in patients with breast cancer
Authors: Yu Haitong , Li Qin , Niu Qingliang , Wu Pu-Yeh
Published: November 2024

Motivation: ALN status is crucial for clinical staging, prognosis assessment, and treatment decision for breast cancer patients. Goal(s): We aimed to assess feasibility of ML based on mpMRI for predicting the risk of NSLN metastasis in breast cancer patients. Approach: mpMRI including T1WI, T2WI, DWI, and DCE-MRI was acquired, and four ML models were constructed. Results: ML model incorporating mpMRI features and clinical factors can predict NSLN metastasis with fair accuracy for breast cancer, with an AUC of 0.781 in test dataset. Five factors for NSLN metastasis were found, including histological grade, cortical thickness, fatty hilum, short axis of lymph node, and age. Impact: The proposed ML model may benefit for breast cancer patients with 1-2 positive SLN but consistently negative NSLN to avoid overtreatment and improve individualized axillary management.
Tailored Prostate MRI Screening: A Deep Learning Classifier for Intelligent Scanning
Authors: Musipatla Amritha , Tong Angela , Dutt Tarun , Mailhe Boris , Sodickson Daniel , Chopra Sumit , Chandarana Hersh , Johnson Patricia
Published: November 2024

Motivation: Multiparametric prostate MRI is lengthy and costly, presenting a challenge for widespread implementation. Goal(s): To develop an automated triage protocol using a deep learning classifier to discern, based on an abbreviated biparametric MR examination, between high-risk patients who would benefit from additional sequences and low-risk patients who would not. Approach: A double-branched ResNet50 with 3D convolutions was trained on biparametric scans to predict the presence of clinically significant prostate cancer. Results: The classifier achieved a sensitivity of 0.93 with 88% negative predictive value, indicating potential to reduce comprehensive MRI exams for those without clinically significant disease by 40%. Impact: Our triage protocol has the potential to streamline prostate cancer screening by reducing the number of full mpMRI exams, thereby lowering healthcare costs. The classifier could pave the way for personalized, risk-adaptive screening protocols, allowing more precise and resource-efficient diagnostics.
Improving Abdominal MR Image Quality at 0.55T Using Deep Learning Reconstruction: A Comparative Study with Commercial 0.55T and High-Field Scans
Authors: Kelsey Lauren , Seiberlich Nicole , Wells Shane , Sellers Robert , Ramachandran Anupama , Richardson Jacob , Gulani Vikas , Hussain Hero
Published: November 2024

Motivation: Deep-learning reconstruction may overcome two shortcomings of 0.55T, low SNR and extended scan time, without compromising lesion conspicuity. Goal(s): To demonstrate that image quality and SNR of deep-learning reconstructed 0.55T images are at least similar to 1.5T/3T images, while maintaining visibility of pathologies. Approach: 23 patients imaged at 0.55T using standard and deep-learning HASTE and DWI. Three radiologists rated IQ and SNR at 0.55T and HF. Pathologies were evaluated in deep-learning images. Results: Deep-learning reconstructed HASTE and DWI 0.55T images were of same or better quality and SNR than 1.5T/3T images. All pathologies were visible on deep-learning 0.55T images. DL reduced HASTE scan-time. Impact: Deep-learning reconstruction algorithms of select sequences at 0.55T can help overcome low SNR and extended scan times of current 0.55T abdominal imaging, making it comparable or superior to standard-of-care 1.5/3T, thereby expanding global use of a more accessible MRI system.
Deep Learning for Predicting Prostate Cancer with Gray-Zone Prostate-Specific Antigen Levels to Reduce Unnecessary Biopsies
Authors: zhang li , zhu yi , jiang shanshan , ai kai , li longchao
Published: November 2024

Motivation: Radiologists face challenges in the accurate prediction of prostate cancer (PCa) with gray-zone PSA levels. Deep learning (DL) evaluated PCa with gray-zone PSA levels remains unclear. Goal(s): The aim of this work was to investigate the comparative performance of DL and radiologists. We trained a 3D DenseNet 121 model for automatic PCa classification with gray-zone PSA levels. Approach: We trained a 3D DenseNet 121 model for automatic PCa classification with gray-zone PSA levels. Results: The DL model yielded an AUC of 0.95 (0.85-1.0) for the identification of PCa with gray-zone PSA levels in the test set, significantly improving performance over the inexperienced radiologists. Impact: The deep learning model yielded an AUC of 0.95 (0.85-1.0) for the identification of PCa with gray-zone PSA levels in the test set, significantly improving performance over the inexperienced radiologists.
Evidence for Mesoscale Gaussian Water Diffusion in Living Human Brain
Authors: Magdoom Kulam Najmudeen , Avram Alexandru , Witzel Thomas , Huang Susie , Basser Peter
Published: November 2024

Motivation: Diffusion MRI is a promising means to map mesoscopic human brain architecture in vivo, however an appropriate model is required that relates the MR signal and features of the underlying microstructure within the voxel. Goal(s): The goal of this study is to determine whether a Gaussian diffusion model is applicable at the mesoscale. Approach: We compared single and double diffusion encoded signals acquired with large b-values at two different diffusion times to test for Gaussianity. Results: We found no significant time-dependence in the diffusion weighted signals in brain parenchyma, confirming the applicability of the Gaussian diffusion at the mesoscale. Impact: The study resolves the ongoing debate on the appropriate model to use to analyze the diffusion weighted signals in live human brain at clinically accessible spatiotemporal scales.
Tract-specific g-ratio using COMMIT: comparison with conventional g-ratio tractometry
Authors: Lu Wen Da , Nelson Mark , Schiavi Simona , Campbell Jennifer , Leppert Ilana , Rowley Christopher , Pike G. Bruce , Daducci Alessandro , Tardif Christine
Published: November 2024

Motivation: Tractometry is used to estimate the microstructural properties of white matter tracts from volumetric images. However, it has significant limitations due to multi-fiber voxels that bias tract measurements. Goal(s): We aim to estimate the tract-specific g-ratio, a ratio of the inner and outer radius of the myelin sheath. Approach: Building on the COMMIT framework, we disentangle the microstructural features of individual white matter tracts to estimate tract-specific g-ratio. Results: Tract-specific g-ratio had higher contrast between tracts and had a stronger correlation with tract caliber (i.e. the axonal cross-sectional area between two nodes derived from COMMIT) and length in comparison to tractometry. Impact: By using this novel COMMIT-based pipeline to analyze diffusion and myelin-sensitive MRI data, we anticipate that tract-specific g-ratio measures will be more sensitive to subtle differences in g-ratio across tracts and individuals due to the elimination of partial volume effects.
Quantitative water content mapping in situ by in situ MRI: a promising forensic tool for post-mortem edema characterization
Authors: Oros-Peusquens Ana-Maria , Bauer Melanie , Lenz Claudia , Scheurer Eva , Shah N. Jon
Published: November 2024

Motivation: Detection of brain edema at forensic examination remains subjective and observer dependent, but more objective criteria perform poorly. A notable exception is the normalized brain weight. Goal(s): Investigate MRI measures of death-associated edema. Approach: We establish in situ water content mapping and T2* relaxometry in a pilot study, adapting a fast quantitative protocol (~6min) using a standard mGRE sequence. Results: Using the derived quantitative maps, we find correlations between water content and T2* in WM, between tissue water weight and brain weight, and macromolecular density vs normalized brain weight. Microstructural characterization of brain oedema with qMRI seems feasible. Impact: Assessing the presence of edema as indicative of the cause of death is important for forensic examinations but is currently observer dependent. We seek to establish objective qMRI-based diagnostic measures and propose microstructural markers such as the macromolecular density.
A metric to access the noise error propagation in phase-based MR-EPT reconstruction
Authors: Cui Chuanjiang , Jung Kyu-Jin , Meerbothe Thierry G. , van den Berg Cornelis A.T. , Mandija Stefano , Kim Dong-Hyun
Published: November 2024

Motivation: To devise a metric to assess the extent of noise amplification in MR-EPT reconstruction algorithms. Goal(s): We explore the correlation between the proposed metric and noise amplification for phase-based conductivity reconstructions. Approach: This study conducted experiments using a concentric cylindrical simulated phantom with uniform electrical properties. Additionally, the proposed metric was applied to in-vivo data. Results: This metric serves as an indicator of the reliability of the reconstructed conductivity maps. the size of the Laplacian kernel and the chosen weighting method significantly impact the metric. Impact: This work reveals that the power of a designed MR-EPT reconstruction kernel acts as a noise error propagation factor in the MR-EPT conductivity reconstruction. Consequently, this map can offer insights into the reliability of reconstructed conductivity map.
Mobility-weighted Effective Extracellular Ion Concentration Imaging using MRI
Authors: Katoch Nitish , Kim Eun Ju , Kim Sang-Young , Hwang Jinwoo , Park Ji Ae , Hur Young Hoe , Kim Jin Woong , Kim Hyung Joong
Published: November 2024

Motivation: Extracellular fluid within the brain is complex electrolyte comprising various ions. Disturbances in the balance of electrolytes have been associated with various pathological disorders. Goal(s): The study aimed to provide quantitative visualization of mobility-weighted effective extracellular ion concentration at every pixel. Approach: Three phantom experiments were conducted using CTI, precisely controlling ion concentrations, mobilities and cell-alike phantom validation. Results: The measured values of ion concentrations in three phantoms were comparable to the intended values, demonstrating accuracy. The conductivity tensor imaging method could extract the contribution of ionic concentration and mobility in measured conductivity information. Impact: We proposed a method to derive the mobility-weighted effective extracellular ion concentration using conductivity tensor imaging (CTI). Ionizing radiation significantly alters the concentration of ions in tissues. Understanding cellular-level ion concentrations is critical for optimizing the effectiveness of radiation therapy.
Added Value of Electrical Conductivity Information to Apparent Diffusion Coefficient in Distinguishing Thoracic Lesions
Authors: Wang Jincheng , Katscher Ulrich , Kikuchi Eiki , Kitagawa Maho , Kikuchi Yasuka , Yoshino Yuki , Tha Khin Khin
Published: November 2024

Motivation: The electrical conductivity (σ) has been proven as helpful for glioma and breast cancer characterization. Our prior study also suggests its potential utility in distinguishing benign and malignant thoracic lesions. Goal(s): This study aimed to evaluate the added value of σ to thoracic lesion diagnosis by apparent diffusion coefficient (ADC). Approach: In this prospective study, we used radiomics analysis to evaluate the prediction value of ADC and σ. The diagnostic performance of selected ADC, σ, and composite indices were compared. Results: 7 independent features were identified in 21 lesions. Half of the top 4 features were from σ. All indices achieved excellent performance. Impact: The results of this preliminary study highlight the potential usefulness of noninvasive electrical conductivity (σ) measurement in the characterization of thoracic lesions.
Electrical properties of potato as a plant-based electroporation model at high (64 MHz) and low (1 kHz - 1 MHz) frequencies
Authors: Lemainque Teresa , Thomas Athul , Kuhl Christiane , Ritter Andreas , Baragona Marco , Katscher Ulrich
Published: November 2024

Motivation: Plant-based models such as potatoes are employed for research on irreversible electroporation (IRE). Volumetric assessment of electroporation-mediated conductivity changes is desirable. MR-based electric properties tomography (EPT) provides volumetric conductivity assessment at the Larmor frequency. Goal(s): This study aimed to assess IRE-mediated conductivity changes in potato tissue by EPT at 64 MHz and by electrochemical impedance spectroscopy (EIS) between 1kHz and 1 MHz. Approach: Potato samples were electroporated with different pulse amplitudes and analyzed by EPT based on 3D FLAIR measurements and EIS. Results: EIS detected a clear conductivity rise in the low frequency range, while EPT did not detect significant conductivity changes. Impact: MR-based electric properties tomography offers volumetric conductivity measurement method at the Larmor frequency, but was not found capable of detecting significant conductivity changes in potato tissue at 64 MHz. This has implications for treatment response assessment in basic electroporation research.
Determining grade and subtype of meningiomas with inversion recovery multiple overlapping-echo detachment imaging
Authors: Yang Yijie , Yang Qizhi , Bao Jianfeng , Wu Zhigang , Lin Liangjie , Wang Jiazheng , Zhong Jianhui , Cai Congbo , Cai Shuhui
Published: November 2024

Motivation: T2 mapping with FLAIR eliminates the interference of cerebrospinal fluid, depicting lesion more precise than conventional T2 mapping, but its use for grading and classifying meningiomas is scarce. Goal(s): To investigate the value of a single-shot T2-FLAIR mapping method, inversion recovery multiple overlapping-echo detachment imaging (IR-MOLED), in distinguishing grades and subtypes of meningiomas. Approach: IR-MOLED was applied on meningioma patients (N = 45), and histogram analysis of enhanced tumor regions was performed based on the resultant parametric maps. Results: T2-FLAIR mapping is sensitive in determining the meningiomas grade (AUC = 0.813) and subtype (AUC = 0.971). Impact: IR-MOLED-based quantitative analysis is promising in differentiating high and low grades and subtypes of meningiomas, especially in patients losing body control.
Spin and gradient echo multiple overlapping echo detachment (SAGE-MOLED) for high-fidelity T2-T2* mapping and simultaneous DSC-DCE imaging
Authors: Yang Qinqin , Wang Lu , Ge Nuowei , Wu Zejun , Bao Jianfeng , Wu Zhigang , Cai Shuhui , Cai Congbo
Published: November 2024

Motivation: Conventional spin and gradient echo EPI (SAGE-EPI) sequence has limited spatial resolution and suffers from geometric distortion. Goal(s): Our goal was to develop a high-fidelity simultaneous T2 and T2* mapping technique used for dynamic MR imaging. Approach: We developed a single-shot spin and gradient echo multiple overlapping-echo detachment acquisition (SAGE-MOLED) method with deep learning reconstruction. The proposed method was tested in phantom and in vivo human brains, and a single-dose contrast dynamic imaging was performed on a clinical case. Results: SAGE-MOLED achieves distortion-free high-precision T2 and T2* mapping compared to gold-standard methods and was successfully used in simultaneous DSC-DCE imaging. Impact: Rapid high-fidelity T2 and T2* mapping can be achieved using our proposed SAGE-MOLED technique, which further enables leakage-corrected simultaneous DSC and DCE imaging at 1.7×1.7×4.0 mm3 spatial resolution and 1.9 s temporal resolution.
Optimizing and Evaluating Fast 3D MulTiPlex (MTP) Imaging for Infant Brain Imaging
Authors: Gu Zhuoyang , Cai Xinyi , Zhu Lixuan , Zhang Weijia , Yang Qing , Tao Tianli , Zhang Weijun , Ye Yongquan , Shen Dinggang , Zong Xiaopeng , Zhang Han
Published: November 2024

Motivation: The novel MulTiPlex (MTP) technique can generate high-resolution, multi-contrast, qualitative/quantitative images, but its reliability and potential for infant study have not been investigated. Goal(s): To optimize MTP parameters and evaluate its reliability and validity for infant study. Approach: n this study, we systematically assessed MTP’s test-retest reliability, compared MTP with varied accelerating rates using AI-assisted Compressed Sensing (ACS), followed by a comprehensive appraisal of its applicability in infant brain imaging, with both qualitative and quantitative methods. Results: The findings indicate that MTP exhibits commendable reliability and holds significant promise for building large infant MRI database. Impact: MTP generates assorted images with varied spectrum of contrasts and precious quantitative images that take hours to acquire conventionally. Our established MTP protocol combines these merits with fast acquisition, warranting its significant usefulness in infant development and clinical studies.
RF power deposition magnification in MRI of fractured passive metallic implants
Authors: Berangi Mostafa , Waiczies Helmar , Niendorf Thoralf
Published: November 2024

Motivation: MRI of (biodegradable) passively conducting implants is challenged by potential elevation of RF power deposition (SAR) in the vicinity of an implant. Accidental implant fractures or fissures due to dynamic degradation of biodegradable implants alter the implants’ structure. This mechanical impact induces changes in the electromagnetic response of the system versus an intact implant. Goal(s): Recognizing this clinical and patient safety challenge, this work first examines the SAR magnification caused by implant fractureApproach: The efficacy of an optimized parallel excitation vectors deduced from a multi-objective genetic algorithm is demonstrated. Results: Reduction of SAR magnification in fractured implant using the optimized excitation vector. Impact: Amplification of RF power deposition in MRI of fractured metallic implants constitutes a patient safety hazard. This risk can be mitigated with parallel transmission using GA-driven excitation. This approach provides a viable clinical alternative for MRI monitoring of implantation sites.
Reducing acoustic noise in head-only scanners with padding on the RF coil
Authors: Boulant Nicolas , Walker Erica , MA Samantha , Beckett Alexander , Vu An , Gunamony Shajan , Feinberg David
Published: November 2024

Motivation: Acoustic noise can be a severe physiological barrier for acquisitions at high field. Goal(s): To isolate the main source of acoustic noise in the NexGen 7T scanner and investigate ways to reduce it. Approach: Vibration and acoustic noise measurements were performed. Sponge window seal materials were stuck on the RF coil to attempt altering its vibrations and decrease acoustic noise. Results: The experimental data is consistent with noise induced by eddy-currents on the RF shield. Altering its vibrations allowed decreasing sound level by up to 10 dB at some EPI echo-spacings. Impact: Identifying the main source of acoustic noise shall open new research avenues to mitigate acoustic noise
Validation and SAR evaluation of a 16-channel 1H/23Na dipole/loop array for 7T MRI
Authors: Wu Menglu , McElroy Sarah , Hajnal Jo , Carmichael David , Ipek Ozlem
Published: November 2024

Motivation: 7T MRI enables 23Na MRI due to enhanced sensitivity. Combined with proton imaging, functional and anatomical information can be acquired to characterise brain status. Goal(s): Establishing safe RF power limits for an in-house built 1H/23Na loop/dipole array for subsequent in-vivo experiments. Approach: Phantom MR results at 7T and electromagnetic simulations were compared to evaluate consistency between experiments and simulations. Human models were simulated to demonstrate SAR10g levels for different patients. Results: Simulated and experimental B1+fields showed high correlation for individual magnitudes and phases. Worst-case SAR10g for human models all remain within safety limits, demonstrating the clinical potential of our proposed coil. Impact: Towards in-vivo RF coil use, we validated simulated RF models of the coil at 23Na/1H frequencies using measured B1+field magnitude and phase maps. Safe RF power limits can therefore be established for research coils to proceed to in-vivo experiments.
Subject-Specific SAR Maps through Conventional MRI Imaging
Authors: Martinez Jessica , Arduino Alessandro , Moulin Kevin , Zanovello Umberto , Cohen Ouri , Otazo Ricardo , Bottauscio Oriano , Zilberti Luca
Published: November 2024

Motivation: To enable real-time SAR monitoring in MRI examinations, particularly when numerical simulations are not feasible. Goal(s): This work aimed to explore the feasibility of obtaining subject-specific image-based SAR maps directly from conventional MRI sequences in vivo. Approach: The tissue electrical conductivities were characterized using an MRI phase-based electrical properties tomography approach, and the electric field distribution was measured by applying Ampère's law on the B1 field obtained from B1 mapping. Results: Subject-specific image-based SAR maps can be obtained from conventional MRI data. Median electrical conductivity values and retrieved SAR maps qualitatively agreed with existing literature. Impact: We analyze the feasibility of obtaining in vivo subject-specific image-based SAR maps directly from conventional MRI data. The electrical conductivity is obtained through electrical properties tomography (EPT) and the E-field is estimated from the B1 field.
An Efficient Method to Mitigate RF-induced Heating in DBS patient at 7T Using Geometrically Adjustable RF Head Array.
Authors: Cho Youngdae , Yoo Hyoungsuk
Published: November 2024

Motivation: Patients with deep brain stimulation (DBS) devices are strictly prohibited from undergoing 7 Tesla magnetic resonance imaging (MRI) scans. Goal(s): Mitigating radiofrequency (RF)-induced heating in DBS for 7 T MRI using a convenient approach. Approach: By utilizing the electromagnetic characteristics arising from the near-field region of the RF coil, a novel method of rearranging the positions of the RF channels adjacent to the DBS lead using a geometrically adjustable RF array was proposed. Results: Adjusting the position of channels adjacent to lead reduces the specific absorption rate and the extent of temperature increase around the electrode up to 34% and 23.8 %, respectively. Impact: Our method involves strategically adjusting the position of RF coil near the lead, providing a practical solution to mitigate RF-induced heating in DBS. It eliminates the necessity for a new lead design or complex numerical optimization for incident electromagnetic field.
Evaluation of performance gains combining high-density receive arrays with transceiver arrays for brain imaging at 7T
Authors: Gunamony Shajan , Beckett Alex , Boulant Nicolas , Feinberg David
Published: November 2024

Motivation: To improve signal-to-noise-ratio (SNR) in deep brain structures while using high-density receive arrays at 7T. Goal(s): Compare SNR and g-factor performance of conventional transmit-only receive-only arrays with receive arrays combined with transceiver arrays. Approach: Transceive function was introduced on 8Tx63Rx and 16Tx96Rx 7T head coils, so that the number of receive channels during acquisition increased by 8 and 16, respectively. Three healthy volunteers were scanned, and SNR and g-factor maps were calculated for the different configurations. Results: The transceiver configuration provided about 16% increase in the central SNR. At higher acceleration factors, especially the 112-channel receive configuration provided improved g-factor performance. Impact: : High-density receive-only arrays provide high SNR close to the surface while maintaining the central SNR under sample noise dominant conditions. Transceiver loops surrounding the receive array enhances the central SNR at 7T, although not as high as reported at 10.5T.
Synthesis of 4D Flow MRI Data using Particle-based Bloch Simulations in CMRsim
Authors: McGrath Charles , Dirix Pietro , Weine Jonathan , Kozerke Sebastian
Published: November 2024

Motivation: Signal model-based synthesis of 4D flow MRI data from CFD is promising to study image acquisition, reconstruction methods and augment data for inference. However, the approach assumes idealized encoding, and particle-based Bloch simulations are needed to include complex effects. Goal(s): Implement a particle-based Bloch simulation in CMRsim for generating synthetic multi-venc 4D flow data. Approach: Using pulsatile, stenotic U-bend CFD data as input, simulations of moving magnetization in CMRsim generate synthetic 4D flow MRI data. Results: 4D flow simulation is feasible in CMRsim, demonstrating tractable simulation times and good agreement with ground truth and magnetization history-based effects, namely acceleration and intra-voxel dephasing. Impact: Numeric Bloch simulations in CMRsim permit a detailed study of imaging protocol-dependent flow-related artifacts in 4D flow MRI of complex flows and can thereby assist in improving acquisition and reconstruction approaches.
Assessing the Diagnostic Potential of Synthetic MRI for Hypogonadotropic Hypogonadism in Thalassemia Patients
Authors: Li Meicheng , Zeng Hongxiu , cui wei , Tang Cheng , Peng Peng
Published: November 2024

Motivation: Pituitary T1 values and pituitary height have potential as predictive markers for hypogonadotropic hypogonadism (HH) in thalassemia major (TM) patients, yet their assessment typically needs multiple MRI scans. Goal(s): Assess the diagnostic potential of Synthetic MRI in detection of HH, with the advantage of obtaining the required MR measurements through a single scan. Approach: Pituitary T1, T2 and pituitary height were measured in 112 TM patients using SyMRI technique. Results: The AUC values for diagnosing HH with pituitary T1 values and pituitary height were 0.736 and 0.753, respectively, and the AUC value of combining these two measurements was 0.813. Impact: Synthetic MRI technology facilitates the diagnosis of hypogonadotropic hypogonadism in TM patients, and the combination of pituitary T1 values and pituitary height yields high diagnostic accuracy for hypogonadotropic hypogonadism in TM patient.