bluuebunny/crossref_metadata_2025 huggingface dataset:
Enhancement of Radiation Therapy Response in Prostate Cancer by Metabolic Modulation
Authors: Orlovskiy Stepan , Gupta Pradeep Kumar , Nelson David , Arias-Mendoza Fernando , Narayan Vivek , You Ming , Kalyanaraman Balaraman , Koch Cameron , Nath Kavindra
Published: November 2024

Motivation: When prostate cancer is treated with external beam radiation therapy (RT) with doses up to 78 Gy, gastrointestinal and genitourinary toxicities are often observed. Goal(s): Tumor sensitization by mito-lonidamine (mito-LND) will lower RT doses reducing the risk of adverse effects. Approach: The effects were assessed in vitro and in vivo in prostate cancer models using Seahorse, 1H and 31P MRS respectively. Results: Our findings showed a sustained and tumor-selective decrease in intracellular pH, bioenergetics, oxygen consumption rate and lactate. Selective tumor acidification, deenergization and oxygenation induced by mito-LND may improve the radiation response in prostate cancer. Impact: Exploiting the modulation of tumor metabolism and microenvironment for improving therapeutic efficacy of radiation therapy (RT) in early stage prostate cancer will lead to improved outcomes in prostate cancer patients.
Comparison of breast cancer subtypes in treatment response using Dynamic Contrast-Enhanced MRI
Authors: Qayyum Sawwal , Zhang Jin , Aronowitz Eric , Han Myung Shin , Lee Seung Koo , Kim Gene
Published: November 2024

Motivation: Breast cancer molecular subtype may affect therapeutic efficacy of metronomic chemotherapy, which currently has not been investigated with non-invasive methods. Goal(s): This study is to utilize DCE-MRI for quantitative measurement of treatment response in different breast cancer subtypes. Approach: Preclinical orthotopic models 67NR(ER+) and 4T1(triple negative) of breast cancer were treated with the same metronomic chemotherapy, while the heterogenous treatment response was evaluated using DCE-MRI. Results: In both tumor models, treatment induced higher Vp, Fp, and Ktrans. The 67NR tumors had higher vascular measures with slower growth rates than the 4T1 tumors. Impact: The preliminary data in this study suggest that the tumor volume alone does not provide adequate information about the changes induced by treatment, and the treatment response differs substantially between breast cancer subtypes as observed by DCE-MRI parameters.
Predicting post-hepatectomy liver failure with whole-liver histogram analysis on T1 maps of gadoxetic acid-enhanced MRI
Authors: Li Jun , Li Yi , Chen Yuan-Yuan , Wang Xiao-Ying , Fu Cai-Xia , Grimm Robert , Ding Ying , Zeng Meng-Su
Published: November 2024

Motivation: Current clinical modalities still have several limitations for accurately predicting post-hepatectomy liver failure (PHLF). Goal(s): To explore more effective non-invasive tools to quantitatively predict PHLF. Approach: The performances of the hematological tests, the indocyanine green (ICG) clearance test and the newly-based albumin-bilirubin (ALBI) scoring system for predicting PHLF were compared with that of whole-liver histogram analysis on gadoxetic acid-enhanced T1 maps. Results: Whole-liver histogram analysis on gadoxetic acid-enhanced T1 maps had a better performance than the ICG clearance test and ALBI scoring system. It also showed potential for stratifying preoperative liver function. Impact: The histogram parameters extracted from whole-liver regions of interest (ROI) on gadoxetic acid-enhanced T1 maps were proved to be effective and non-invasive tools for assessing liver function. Further accurate liver function assessment based on sectional histogram analysis is promising.
Advancing Low-Field Abdominal MRI: A Comprehensive Dataset with Various Denoising Strategies
Authors: GUAN Jingwei , LI Yi , YANG Kexin , WANG Yuwan , HUANG Shoujin , HUANG Pengxing , LIU Haiguang , ZHOU Weichen , WEN Kaihua , LI Jingyu , LYU Mengye
Published: November 2024

Motivation: Abdominal MRI plays a crucial role in non-invasively visualizing abdominal structures. Goal(s): But abdominal MRI data often faces artifacts such as the strong noise. Approach: In this work, a low-field abdominal MRI dataset is presented, comprising multi-contrast multi-repetition abdominal images of 58 healthy subjects. The dataset includes images with different contrasts, i.e., BH (Breath Hold)-T1, FB (Free breath)-T2, RT (Respiratory Triggered)-T2, RT-FST2 (Respiratory Triggered Fat-Suppressed T2). Additionally, various denoising methods, including traditional, supervised and self-supervised approaches, were explored with different structures and various loss functions. Results: These methods show promising results and provide some initial comparative conclusions for abdominal MRI denoising task. Impact: A low-field abdominal MRI dataset is presented, and various denoising methods were explored. During denoising, the main challenge is the trade-off between detail preservation and denoising. This dataset can inspire further exploration and research in this field.
Assessment of repeatability and reproducibility of brain oxygen extraction fraction mapping through QQ-CCTV
Authors: Zhuang Hangwei , Zhang Qihao , Spincemaille Pascal , Nyugen Thanh , Wang Yi
Published: November 2024

Motivation: QQ-CCTV has been validated under 3T to map brain oxygen extraction fraction. Yet its repeatability and reproducibility has not been evaluated under 1.5T. Goal(s): Assess the repeatability and reproducibility of QQ-CCTVApproach: QQ-CCTV was performed to calculate brain oxygen extraction from repeated scans under 3T and 1.5T. OEF values are compared between scans using linear regression and Bland-Altman plots. Results: The OEF values obtained from 3T and 1.5T scans show minimal bias and good correlation. Although under 1.5T the sampled data have lower SNR, QQ-CCTV may benefit from lower characteristic frequency and longer characteristic time. Impact: QQ-CCTV can be used with mGRE data acquired under 1.5T to map brain oxygen extraction with good repeatability and reproducibility and has the potential to be used clinically.
Quantitative MRI biomarker for classification of clinically significant prostate cancer: calibration for reproducibility across echo times
Authors: Kallis Karoline , Conlin Christopher , Ollison Courtney , Hahn Michael , Rakow-Penner Rebecca , Dale Anders , Seibert Tyler
Published: November 2024

Motivation: Restriction Spectrum Imaging restriction score (RSIrs) is a quantitative biomarker for detection of clinically significant prostate cancer. However, magnitude of RSIrs is influenced by imaging parameters, including echo time (TE). Goal(s): We introduce a calibration technique to generate consistent RSIrs biomarker values for data acquired with different TEs. Approach: We demonstrate a partial linear relationship between RSIrs and TE and compare calibrated to reference RSIrs values at two TEsResults: The proposed calibration reduced bias between calibrated and reference RSIrs values. Impact: Restriction Spectrum Imaging restriction score (RSIrs) is a quantitative MRI biomarker of clinically significant prostate cancer, but RSIrs values are dependent on echo time. This study introduces an approach to calibrate RSIrs for echo time variations and yield reproducible results.
Comparison of two deep learning models for contrast agent dose reduction in dynamic contrast enhanced breast MRI
Authors: Lemainque Teresa , Huck Luisa , Müller-Franzes Gustav , Bode Maike , Nebelung Sven , Kuhl Christiane , Truhn Daniel
Published: November 2024

Motivation: In the context of MRI-based breast cancer screening, reducing contrast agent dose is desirable. However, this yields decreased contrast-to-noise ratio in dynamic-contrast-enhanced subtraction images. Goal(s): This work aimed to compare two deep learning techniques, diffusion probabilistic models (DDPM) and general adversarial networks (GAN), for retrospective contrast enhancement of low-dose breast MRI subtraction images. Approach: Training and testing was performed on virtual low dose subtraction images, which we generated by subjecting original subtraction images to different amounts of noise. Results: Both DDPM and GAN could denoise these images; however, neither model was superior over the other across all tested dose levels and evaluation metrics. Impact: Diffusion probabilistic models and general adversarial networks can retrospectively enhance the signal of virtual low-dose images. They may supplement imaging with reduced doses in the future; yet, further development and validation on real low dose images are warranted.
MRI Diagnosis of Lesions Presenting as Architectural Distortion on DBT: Comparison of Diagnostic Performance Using BI-RADS and Radiomics Models
Authors: Zhou Jiejie , Chen Xiao , Zhang Yang , Liu Yan-lin , Pan Yong , Chen Jeon-Hor , Cao Guoquan , Wang Meihao , Su Min-ying
Published: November 2024

Motivation: Diagnosis of lesions shown as architectural distortion (AD) on DBT is challenging, and breast MRI may help. Goal(s): To compare the diagnostic performance of 60 cases using reading based on BI-RADS of DBT and MRI, Kaiser score, and radiomics models. Approach: In addition to comparing the diagnostic performance, features shown on DBT and MRI, and the distribution in different MRI BI-RADS categories, were reported. Results: The malignant rate of AD varied in associated features and MRI-RADS groups. MRI showed better diagnostic performance than DBT. When using radiomics models, the accuracy was almost the same, but the AUC of DBT+MRI fused model improved. Impact: Diagnosis of lesions presenting as AD on DBT can be improved with more understanding of associated features, as well as the predictive features based on the supplementary MRI.
Lipid composition mapping for early response to neoadjuvant chemotherapy in breast cancer using chemical shift-encoded imaging
Authors: Cheung Sai Man , Chan Kwok-Shing , Senn Nicholas , Sharma Ravi , McGoldrick Trevor , Gagliardi Tanja , Husain Ehab , Masannat Yazan , He Jiabao
Published: November 2024

Motivation: Deregulation of lipid composition, holding a critical role in breast cancer progression, can be accurately mapped using chemical shift-encoded imaging (CSEI), and might support response monitoring in patients undergoing neoadjuvant chemotherapy (NACT). Goal(s): We aimed to determine the power of CSEI in differentiation of poor responders after one cycle of NACT. Approach: Seventeen patients were imaged before and after one cycle of NACT to compute the percentage change in monounsaturated, polyunsaturated and saturated fatty acids. Results: There was a treatment induced lipid normalisation in all participants, however there was no difference in poor responders in comparison to good responders. Impact: CSEI is sensitive to the longitudinal change in lipid composition in the peri-tumoural region and the whole breast, however the impact on lipid metabolism is secondary to determining poor responder and other markers of metabolic imaging should be explored.
SmartBlade: AI-based reconstruction for motion robust abdominal PROPELLER imaging
Authors: Selivanov Alexander , Eggers Holger , Meineke Jakob , Laves Max-Heinrich , Doneva Mariya
Published: November 2024

Motivation: The motion correction in PROPELLER is typically limited to rigid body motion and only the averaging effect is exploited for motion artifact reduction for abdominal scans. Goal(s): Our goal was to reduce motion artifacts and improve image quality in abdominal MRI. Approach: We proposed an AI framework for reconstructing high-resolution motion-free image from a free-breathing abdominal PROPELLER scan. The AI model was trained on synthetic data and tested on abdominal T2W TSE scans. Results: The proposed AI reconstruction outperforms the conventional PROPELLER reconstruction and PROPELLER with non-rigid motion correction in terms of residual motion artifacts and general image quality. Impact: The proposed AI-based reconstruction allows obtaining motion-free images with high-spatial resolution from PROPELLER MRI scans, which facilitates abnormality detection.
Feasibility study of 4-dimensional free-breathing dynamic contrast-enhanced MRI for liver quantitative perfusion at 3T on oncology patients
Authors: Li Jing , Liu Yuan , Sun Peng , Fu Qing , Li Xin
Published: November 2024

Motivation: The requirements for multiple breath-holds would cause motion artifacts, which may impair the accurate evaluation of lesion properties in dynamic perfusion scans. Goal(s): To evaluate the clinical feasibility of free-breathing dynamic liver MR perfusion(FBP) compared with breath-holding dynamic perfusion(BHP) method. Approach: A case-control study of 58 oncology patients were collected. The image quality, intervolume motion effect and diagnostic confidence were compared between the two groups. Results: Four-dimensional free-breathing dynamic liver MR perfusion (4D-FBP) could provide comparable image quality but significantly fewer motions than routine method, making 4D-FBP an attractive alternative to existing breath-holding techniques in clinical dynamic liver MR scans. Impact: Free-breathing dynamic liver MR perfusion provides significantly improved intervolume motion and comparable image quality in comparison to the routine breath-holding method, which may promote the clinical translation of liver quantitative perfusion.
Using Image Quality Metric to Improve Navigator-based Motion Correction Robustness
Authors: Haji-valizadeh Hassan , Sharma Samir
Published: November 2024

Motivation: Navigator-based rigid-body motion correction (MoCo) can improve IQ when the correct motion parameters are extracted from navigator data. However, navigator-based MoCo can reduce IQ when incorrect motion parameters are derived. Goal(s): We propose combining IQ assessment with navigator-based MoCo to improve correction robustness. Approach: The proposed solution was implemented by calculating the entropy (IQ metric) of images with/without navigator-based rigid-body MoCo, and MoCo was applied only when the image entropy decreased. Results: During volunteer studies, the proposed solution reduced the number of slices that showed IQ reduction due to rigid-body correction, while maintaining a large majority of slices that benefited from rigid-body correction. Impact: Navigator-based rigid-body motion correction (MoCo) robustness was improved by using an image quality metric to determine whether MoCo improved IQ.
Accuracy requirements for an automated deep-learning-based slice prescription for cardiac MRI
Authors: Gorodezky Margarita , Kaushik Sandeep , Janich Martin , Delso Gaspar
Published: November 2024

Motivation: Automated plane prescription tools can make cardiac MRI more accessible, but their accuracy needs to be validated. Goal(s): We aim to determine the accuracy requirements for an AI-driven automated prescription tool. Approach: To determine the accuracy requirements for an AI-driven automated prescription tool we compare landmarks set by the tools to those set manually by operators with different levels of experience. Results: The prototype can match the average performance of an operator group, outperforming the less experienced individuals. Impact: To be reliable the performance of automated prescription tools needs to be established. Here an AI-driven automated prescription tool for cardiac MRI planes could match the average performance of an operator group, outperforming the less experienced individuals.
A combined application of Deep Learning Recon and MRI mute technique
Authors: Wang Hongbin , Tang Weinan , Wu Jianghua , Xi Wei
Published: November 2024

Motivation: MRI mute technique reduces scanning noise by lowering the gradient slew rate which increase echo spacing, resulting in image blurring and longer scan time. Goal(s): To design a scanning method that simultaneously reduces scan time and scanning noise without compromising image SNR and clarity. Approach: Develop a DL-Recon post-processing model to enhance SNR and clarity of images which are acquired from optimized knee scanning protocol with mute technique. Results: The proposed scanning method improves SNR about 38% and reduces scan time and noise sound pressure separately about 44.8% and 86%. Impact: Combining DL-Recon with mute sequences help doctors to diagnose more patients. It also enhances the success of clinical scans by decreasing scan noise to improve patient comfort. This is a successful application of DL-Recon with mute scanning technique.
RAKI informed unrolled iterations for robust simultaneous multi-slice reconstruction on new datasets
Authors: Mei Lifeng , Yang Kexin , Li Yi , Huang Shoujin , Li Jingyu , Liu Jingzhe , Guo Hua , Wu Bing , Xiong Yuhui , Zhang Lingyan , Lyu Mengye
Published: November 2024

Motivation: To improve reconstruction quality of Simultaneous Multi-Slice (SMS) imagingGoal(s): To develop a deep learning reconstruction method without sacrificing image detail and fidelity, even in data-scarce scenarios. Approach: Our approach involves a novel integration of subject-specific RAKI and a Variational Network (VarNet) within an unrolled iteration framework, testing three different guidance strategies to improve reconstruction quality. Results: The RAKI-VarNet In-Iteration Parallel method yielded the most promising results, showing a reduction in noise and artifacts while maintaining robustness on both seen and unseen data sets, including challenging EPI data. Impact: This technique presents a robust solution for high-fidelity SMS MRI reconstruction with improved generalizability and detail preservation.
Transferable Variational Feedback Network for Accelerated MRI Reconstruction
Authors: Paul Riti , Vora Sahil , Ding Pak Lun Kevin , Patel Ameet C. , Hu Leland S. , Li Baoxin , Zhou Yuxiang
Published: November 2024

Motivation: Long MR procedure times often result in a shortage of patient data for specific cases, affecting the performance of data-dependent deep networks. Transfer learning offers a remedy, enabling pretrained models to adapt to new domains with limited data availability. Goal(s): Our goal is to create a network capable of producing clinically acceptable reconstructions with limited data. Approach: We leverage representation learning to refine low-resolution data and enhance final reconstructions in data-limited scenarios. Results: Successful transfers with 100 and 40 training sample sets were achieved. Both networks achieve comparable results to the large dataset (240 samples) trained network. Impact: Our approach has broader clinical uses beyond acquisition protocols, extending to vendor differences and scenarios with limited access to disease scans due to privacy concerns. It presents an opportunity to tackle limited data generalization challenges without adding architectural complexity.
mrftools: A research framework for integrated design, simulation, acquisition, and reconstruction of Magnetic Resonance Fingerprinting (MRF)
Authors: Dupuis Andrew , Bolding Reid , Sun Jessie , Chen Yong , Boyacioglu Rasim , Griswold Mark
Published: November 2024

Motivation: Developing robust MRF acquisitions and reconstructions requires meticulous manual tracking of parameters and dependencies, as well as integration of diverse tools. Increasing ease of development and use, regardless of research or clinical purposes, would help with broader adoption and understanding of MRF. Goal(s): Improve tooling to increase MRF research flexibility while maximizing traceability of acquisitions and reconstructions. Approach: Develop a unified Python framework, mrftools, supporting MRF-specific modular sequence abstraction, integration of reconstruction dependencies, and online reconstruction. Results: Built-in support for many common techniques and high extensibility for research use is demonstrated for both sequence and reconstruction development. Impact: By providing a unified framework for sequence development and reconstruction, mrftools streamlines workflow, ensuring traceability and repeatability of MRF for research or clinical usage. Key contributions lie in MRF-specific modular sequence design, the integration of reconstruction dependencies, and online reconstruction.
MR software tools for real-time decision making and FOV prescription
Authors: Wighton Paul , Hinds Oliver , Frost Robert , Hoffmann Malte , Gagoski Borjan , Varadarajan Divya , Proulx Sebastien , Reuter Martin , Polimeni Jonathan , Fischl Bruce , Ghosh Satrajit , van der Kouwe Andre
Published: November 2024

Motivation: Many cutting-edge MR neuroimaging paradigms require real-time decision making and precise FOV positioning. We present two software tools to support such paradigms. Goal(s): Develop two modules. 1) vSend: opens a socket and sends imaging data to another computer in a vendor-agnostic format, enabling real-time analysis. 2) AAhijack: reads a matrix from a socket and overwrites the Siemens AutoAlign matrix, enabling online slice prescription. Approach: Modules are implemented as Siemens image reconstruction modules (ICE functors) in C++ and two slice prescription systems utilizing the modules are demonstrated. Results: The slice prescription systems have comparable performance and various advantages and disadvantages. Impact: The software tools presented have enabled a variety of cutting-edge MR neuroimaging paradigms including real-time fMRI, motion tracker calibration, real-time shimming, fetal head-pose detection and automated FOV prescription, reacquisition planning and single-slice BOLD imaging FOV prescription.
Bloch simulation of geometric distortion around air bubbles acquired with a 7 T MRI system
Authors: Kose Katsumi , Kose Ryoichi , Fujimoto Koji , Okada Tomohisa
Published: November 2024

Motivation: To reproduce geometric distortions around air bubbles imaged with a 7 Tesla MRI system using Bloch simulation. Goal(s): To find a strategy to calculate geometric distortion caused by susceptibility effects in Bloch simulation. Approach: Inhomogeneous magnetic fields were calculated using summing up magnetic fields generated by magnetic dipoles in the air bubbles in a spherical phantom. Results: Geometric distortions were reproduced by the Bloch simulations using the inhomogeneous magnetic fields calculated by summation of the magnetic fields generated by the magnetic dipoles in the air bubbles. Bubbles deformed by the strong static magnetic field were observed. Impact: Geometric distortion around arbitrary shaped air bubbles was reproduced by the Bloch simulation. An air bubble at 7T was found to be deformed by the strong static magnetic field and the large diamagnetic susceptibility of water.
4Dflow-unwrap: A collection of python-based phase unwrapping algorithms
Authors: Dirix Pietro , Jacobs Luuk , Kozerke Sebastian
Published: November 2024

Motivation: Low-Venc PC-MRI acquisitions present the advantage of higher velocity-to-noise ratio (VNR). To the best of the author’s knowledge, no Python-based public repository exists with 4D flow MRI unwrapping algorithms and realistic examples. Goal(s): To share a collection of simple unwrapping tools for 4D Flow MRI and test their performance. Approach: To use three established unwrapping techniques to create a Python package to unwrap 4D flow MRI data. Results: We demonstrate the performance of these algorithms on aortic data including multiple Venc values and undersampling factors. Impact: The Python repository with a collection of simple unwrapping tools facilitates access to unwrapping techniques. These scripts can readily be used to unwrap any PC-MRI dataset.
Can Deep Learning Reconstruction Allow Rapid Diffusion Weighted Imaging for Rectal Cancer?
Authors: Wan Xinyi , Ma Chao , Xue Ting , Dai Jiankun , Shi Jie , Jiang Song , Fan Li
Published: November 2024

Motivation: Diffusion-weighted imaging (DWI) has been widely reported for detection, staging, and treatment response prediction of rectal cancer. High number of excitations for sufficient SNR was normally used in DWI for rectal cancer but with lengthy acquisition. Goal(s): Investigate the role of deep learning reconstruction (DLR) in rapid rectum DWI by comparing with standard protocol. Approach: Forty primary rectal cancer patients were enrolled. Each patient was imaged with standard and rapid DWI. Image quality and diagnostic performance were compared. Results: Rapid DWI with DLR reduced 1/2 scan time without sacrificing image quality and improved the diagnostic performance for distinguishing T1/T2 from T1/T4 patients. Impact: The application of DLR would be beneficial for rectum DWI not only for scan time but also for tumor boundary delineation being important for staging.
High quality diffusion images from accelerated acquisition on an MR-Linac by using Deep learning.
Authors: Nair Prashant , Xiao Yu , Lecoeur Bastien , Tree Alison , Navest Robin , Oelfke Uwe , Blackledge Mathew , Wetscherek Andreas
Published: November 2024

Motivation: Trade-offs between acquisition time and precision of the apparent diffusion coefficient (ADC) hinder the adoption of diffusion-weighted (DW) MRI for biologically-adaptive MR-guided radiotherapy. Goal(s): To obtain high quality DW images and precise ADC maps using deep learning while shortening acquisition times on an MR-Linac. Approach: We trained U-net models to generate high quality DW images and ADC maps from only one average per b-value. Four models were trained using either trace-weighted or single DW direction images and with or without registration to the b0 image. Results: Trained models effectively generated high-quality images from subsampled data. Registration reduced ADC variability. Impact: Using deep learning we obtained high quality diffusion-weighted MRI from subsampled MR-Linac data. Shortened acquisitions and increased precision of the apparent diffusion coefficient facilitate integration into adaptive MR-guided radiotherapy workflows that use diffusion-weighted MRI for treatment response assessment and prediction.
Improved Low Angular Resolution Diffusion Parametric Maps Using Deep Learning
Authors: Tucksinapinunchai Nontharat , VanderLaan Doug , Peragine Diana , Skorska Malvina , Yarach Uten
Published: November 2024

Motivation: The DTI technique is used to analyze and evaluate the white matter microstructure; however, the acquisition time is too long for clinical practice and large-scale research. Goal(s): To reduce acquisition time by improving the low angular resolution diffusion parametric maps. Approach: The deep-learning framework was used to improve the low angular resolution diffusion parametric maps and image quality measured with PSNR, and NRMSE. Results: Our deep-learning framework improves low angular resolution diffusion parametric maps by effectively acquiring fiber information in FA map and enhancing overall image quality with increased PSNR and decreased NRMSE. Impact: The reduced acquisition time and improved quality of the low angular resolution diffusion parametric maps obtained with our deep-learning framework may benefit to clinicians and researchers who study in white matter microstructure in routine clinical practice and large-scale research.
Estimating brain age and identifying important neural connections in mouse models of genetic risk for Alzheimer’s disease
Authors: Moon Hae Sol , Mahzarnia Ali , Stout Jacques , Anderson Robert , Han Zay Yar , Badea Alexandra
Published: November 2024

Motivation: Understanding Alzheimer’s disease (AD) requires decoding the complex interplay of risk factors, particularly how age-related structural connectivity changes affect AD onset and progression. Goal(s): Using a state-of-the-art deep learning method, we aim to identify key brain connections, predict age and assess AD risk factors with structural brain connectomes and behavioral data from mouse models with humanized APOE genotypes. Approach: Our Feature Attention Graph Neural Network (FAGNN) integrates multivariate data types, focusing on aging-related brain connections with a quadrant attention module. Results: FAGNN surpassed other models in age prediction and identified critical neural pathways, like striatum-cingulum connection, offering insights into age-related brain connectivity changes. Impact: We used AI and FAGNN to advance Alzheimer’s disease research, predicting risk factors such as age and identifying crucial neural connections pertinent to the risk factors, potentially paving the way for early detection and targeted interventions in aging-related cognitive decline.
Particle-based modelling of isotope exchange at equilibrium in hyperpolarized [1-13C]pyruvate MR
Authors: Dingwell Dylan , Cunningham Charles
Published: November 2024

Motivation: Isotope exchange at equilibrium complicates interpretation hyperpolarized [1-13C]pyruvate MR. Modelling this phenomenon could help to quantify the effect of exchange on HP MR signal. Goal(s): Investigate how isotope exchange (compensatory backward conversion of unlabeled lactate concurrent with labeled lactate production) occurs and affects hyperpolarized [1-13C]pyruvate MR. Approach: Develop a realistic computational model of pyruvate-lactate interconversion and apply it to characterize how different levels of endogenous lactate influence metabolic reaction kinetics and exchange. Results: Elevation of unlabeled lactate increases isotope exchange at equilibrium. Net production of lactate occurs unless total lactate exceeds its equilibrium ratio with total pyruvate. Impact: This in silico model of isotope exchange in hyperpolarized [1-13C]pyruvate MR realistically replicates spectroscopic measurements, with particle-level data for a range of conditions providing insight into metabolic dynamics relevant to complex cellular architectures with different local equilibria.
Value of intravoxel incoherent motion magnetic resonance imaging in evaluating renal fibrosis in rabbits with renal artery stenosis
Authors: Zha Tingting , Xing Wei , Chen Jie , Wu Peng
Published: November 2024

Motivation: Intravoxel incoherent motion (IVIM) imaging can evaluate tissue microperfusion and diffusion information respectively, which can more accurately evaluate renal fibrosis (RF) in chronic kidney disease. Goal(s): IVIM can reflect the occurrence and development of RF from two aspects of perfusion and diffusion respectively. It has a great application prospect for the evaluation of RF. Approach: The dynamic changes of renal cortical and medullary signal with the progression of fibrosis were monitored by IVIM, and the compensatory effect of the contralateral kidney was analyzed. Results: Some IVIM parameters of the renal cortex and medulla were moderately or strongly negatively correlated with RF. Impact: Early detection and dynamic evaluation of the process of RF are crucial for reversing renal function damage. IVIM can reflect the development of RF from two aspects of perfusion and diffusion, which has a great application prospect for evaluating RF.
Assessing Liver Fibrosis with Diffusion-weighted MRI-based Virtual Elastography: Comparison with US Shear-wave Elastography
Authors: Yang Li , Zeng Mengsu , Fu Caixia
Published: November 2024

Motivation: Recent reports have shown a strong correlation between tissue water diffusivity and liver elasticity. Goal(s): The aim to this study was to compare the performance of DW MRI-based virtual shear modulus (µDiff) and liver stiffness (LS) measured using US shear-wave elastography (SWE) for staging liver fibrosis. Approach: We retrospectively analyzed µDiff and LS on the right liver lobe in 124 patients. Results: Our results showed the area under the curve of LS was significantly higher than that of µDiff (0.96 vs. 0.87, P<0.05) for advanced fibrosis. However, no significant difference was found between LS and µDiff (0.88 vs. 0.79, P>0.05) for significant fibrosis. Impact: Although DW MRI-based virtual shear modulus showed significant correlation with the histologic stages of fibrosis, it was inferior than liver stiffness measured using US shear-wave elastography for staging liver fibrosis.
Repeatability of Standalone versus Image-Acquisition-Integrated 129Xe Spectroscopy
Authors: Zheng Yi , Zhang Shuo , Costelle Anna , Bechtel Aryil , Mugler John , Mummy David , Driehuys Bastiaan
Published: November 2024

Motivation: 129Xe spectral parameters of lung airspaces, membrane tissues and red blood cells (RBC), provided by an MRS calibration scan, are important indicators of gas exchange function. Goal(s): Currently, such spectra are often acquired separately from gas exchange images, but it may be feasible to integrate 129Xe gas-exchange MRI and MRS into one scan, and test its repeatability. Approach: In this study, we assess the performance of image-acquisition-integrated spectra and compare them with a dedicated spectroscopicacquisition. Results: In subjects with a range of diseases, image-acquisition-integrated spectra were less repeatable than dedicated ones. However, increasing the number of averaged FIDS to 1second may yield equivalent performance. Impact: The repeatability of 129Xe spectroscopy integrated with imaging would be improved by collecting and averaging at least 1 second of data.
4D Flow MRI-Derived Hemodynamic Parameters Related to Growth in Acute Type B Aortic Dissections
Authors: Cebull Hannah , Liu Minliang , Dong Hai , Elefteriades John , Gleason Rudy , Piccinelli Marina , Oshinski John , Leshnower Bradley
Published: November 2024

Motivation: In type B aortic dissections (TBAD), surgical outcomes often worsen as the flap stiffens. Earlier intervention (i.e., acute phase) would require prediction of aortic growth to identify high-risk patients, which has not yet been investigated in acute TBADs. Goal(s): We aimed to identify possible hemodynamic markers of aortic growth from 4D flow MRI data in acute TBAD subjects. Approach: From the 4D flow data we estimated various hemodynamic metrics. Results: Peak entry tear velocity, false lumen retrograde flow pre-entry tear, and forward flow post-entry tear were higher in growth cases. We also demonstrate the feasibility of collecting 4D flow MRI from acute patients. Impact: As the first study focused on in vivo hemodynamics of acute type B aortic dissections (TBAD), we aimed to identify possible metrics for predicting aortic growth. Ultimately, stratifying risk in acute TBAD patients would enable preemptive treatment and improve outcomes.
Broadband Motion Detection with Wavelet MRE: Validation Study in Phantom and in vivo
Authors: Le Yuan , Glaser Kevin , Chen Jun , Rossman Phillip , Sui Yi , Shan Xiang , Manduca Armando , Huston John , Ehman Richard , Yin Ziying
Published: November 2024

Motivation: To test wavelet magnetic resonance elastography (MRE), a promising new technique, on its efficiency in detecting broadband tissue motion. Goal(s): Evaluate the efficiency and reproducibility of broadband motion detection using wavelet MRE. Approach: Transient motion in phantoms was reconstructed using both the inverse wavelet transform and standard deconvolution using MRE with various MEG lengths. The estimated displacements were compared. The reproducibility of brain transient motion measurements was assessed in vivo. Results: Wavelet MRE demonstrated high efficiency and reproducibility for broadband motion detection. These findings suggest that wavelet MRE is a promising technique in brain trauma study. Impact: Wavelet magnetic resonance elastography is developed to detect broadband motion more accurately and efficiently and could be particularly useful in detecting transient motion during brain impact. This study aims to validate its efficacy and assess its repeatability.
Improving PI-RADS rating with Zoomed Diffusion-Weighted Imaging in Deep Learning CAD Systems
Authors: Long Haining , Zhu Wangshu , Hu Lei , Wei Liming , Dai Lisong , Fu Caixia , Lu Yichen , Xu Cancan , Hu Zhonghua , Xu Zhihan , Grimm Robert , Busch Heinrich , Benkert Thomas , Xing Pengyi , Zhao Jungong
Published: November 2024

Motivation: The clinical accuracy of the Prostate Imaging Reporting and Data System (PI-RADS) rating by deep-learning-based computer-aided diagnosis (DL-CAD) models need further enhancement for improved prostate cancer (PCa) detection and fewer unnecessary biopsies. Goal(s): This study aimed to achieve more precise PI-RADS rating for PCa lesions by using zoomed diffusion-weighted imaging (z-DWI) in DL-CAD models. Approach: We compared the diagnostic performance and PI-RADS rating of DL-CAD using advanced z-DWI vs. conventional DWI and extended this analysis to radiological practice. Results: z-DWI improved the PI-RADS rating of PCa lesions by DL-CAD based on superior diagnostic performance compared with conventional DWI. Impact: Deep-learning-based computer-aided diagnosis using zoomed diffusion-weighted imaging provides more accurate PI-RADS rating than conventional DWI, correlating MRI-detected lesions with prostate cancer (PCa) from biopsy. This can help minimize unnecessary biopsies for benign lesions while facilitating timely PCa treatment.
Bi-regional quantitative DCE-MRI for prediction of microvascular invasion in hepatocellular carcinoma and its significance for treatment
Authors: Zhu Yongjian , Wang Peng , Cai Wei , Wang Bingzhi , Meng Xuan , Wang Sicong , Ma Xiaohong
Published: November 2024

Motivation: Accurately predicting microvascular invasion (MVI) risk in hepatocellular carcinoma before surgery could aid clinicians in selecting appropriate surgical approaches to improve the patient’s prognosis. Goal(s): To construct DCE-MRI based nomogram for predicting MVI, and to assess its ability for stratifying the risk of recurrence after hepatectomy and guiding surgical approaches. Approach: Quantitative DCE-MRI parameters from both intra-tumoral region (ITR) and peritumoral region (PTR), along with clinical-radiological (CR) features, were utilized to establish the nomogram. Results: The nomogram presented AUC values of 0.966 in the training and 0.937 in the validation set for predicting MVI. High-risk patients could obtain survival benefit from anatomical resection. Impact: We constructed and evaluated the performance of the bi-regional quantitative DCE-MRI based nomogram for predicting MVI risk in HCC. Our predictive model effectively predicts MVI risk and assists clinicians in selecting appropriate therapeutic strategies for patients.
Field and TE independent liver iron concentration estimation using signal intensity ratios
Authors: Doyle Eamon , Wood John
Published: November 2024

Motivation: Estimation of liver iron concentration by R2* relaxation (LICR2*) is a powerful and widely used technique, however, it may fail from signal loss at high liver iron concentration. Goal(s): To estimate LIC from a single-TE liver-muscle signal intensity ratio (LICSIR) and validate at 1.5 and 3.0 Tesla. Approach: Using LICR2* estimates collected at 1.5T as a reference, we compared LICSIR estimates in 15 subjects who had undergone MRI examination at both 1.5T and 3.0T. Results: We were able to derive field-independent scaling constants that allow LICSIR estimation at 1.5 and 3.0T, more than doubling the effective dynamic range of LICR2* estimation. Impact: This generalized framework for LICSIR estimation allows reasonable LIC values to be reported (and trended) in patients for whom traditional relaxometry has failed. It also allows approximate LIC calculation from commonly used single and dual echo gradient echo acquisitions.
Synthetic MRI for the quantitative Assessment of efficacy of Immunotherapy in nasopharyngeal carcinoma.
Authors: Huang Yu , ZHAO Chen
Published: November 2024

Motivation: Due to the internal heterogeneity of the nasopharyngeal carcinoma, conventional treatment fails to enhance the tumor microenvironment, ultimately leading to recurrence or metastasis. Goal(s): To investigate whether quantitative values derived from T1, T2, and PD maps can serve as an assessment index for combined immunotherapy with chemotherapy in patients with newly diagnosed locally advanced NPC. Approach: Synthetic MRI, non-invasive technique, can generate quantitative values for intrinsic tissue features. Results: The study indicate the potential of PD and T2 values in distinguishing between two treatment. A larger sample size is required to further validate the value of SyMRI in evaluating immunotherapy for NPC. Impact: Synthetic MRI can generate longitudinal relaxation time (T1), transverse relaxation time (T2), and proton density (PD).
Open-Source Algorithm for Automatic Magnetic Susceptibility Determination from Field Maps
Authors: Wehkamp Niklas , Rovedo Philipp , Leupold Jochen , Bär Sebastien , Zaitsev Maxim
Published: November 2024

Motivation: The magnetic susceptibility is a fundamental material property for MR and NMR equipment engineering. The literature provides several theoretical solutions to measure the magnetic susceptibility. However, an openly available implementation that allows to determine the magnetic susceptibility automatically is missing. Goal(s): Develop a non-proprietary approach to determine the magnetic susceptibility from measured field maps. Approach: Measure the field map of a cylindrical sample. Develop a Python program to extract the magnetic susceptibility of the sample. Results: The measured reference samples reflect the magnetic susceptibility of the literature. Code for data processing is available through the open access repository. Impact: Our research provides a programmatic solution to automatically determine the magnetic susceptibility of cylindrical samples from field map measurements in MRI systems. This will aid MR and NMR equipment engineers to measure the magnetic susceptibility of any material of interest.
Measuring CSF net velocity using DENSE at 7T with improved correction for involuntary motion and eddy currents.
Authors: van der Voort Elisabeth , van der Plas Merlijn , Zwanenburg Jacobus
Published: November 2024

Motivation: Clearance is important for healthy brain functioning. The ability to measure CSF net velocity would be valuable to gain insight into the underlying mechanisms and pathways of clearance. Goal(s): To measure CSF net velocities in FH and RL direction whilst accounting for periodic motions, involuntary head motion and eddy currents. Approach: A multi-slice single shot DENSE acquisition is used to measure CSF displacements over time. Results: The measured net velocity does not fit the classical view on CSF excretion and absorption locations. Further validation is needed using a moving flow phantom. Impact: The measured net velocities are about 10 percent of what would be expected. If confirmed in a larger cohort, the results challenge the classical view of main CSF excretion at the choroid plexus and absorption at the sagittal sinus.
Initial experience of proton short echo MR spectroscopy of human brain at 5T
Authors: Lei Hongxia , Wen Linfei , Deng Bin , Xue Aiguo , Wang Chaohong
Published: November 2024

Motivation: Proton short echo (TE≤10ms) MR spectroscopy of human brain at high magnetic fields (≥3T) provides abundant metabolic information beyond MR images, but remains challenging for routine clinical usages beyond 3T. Goal(s): To evaluate feasibility and assess quality of proton ultrashort echo (i.e.10ms) MR spectroscopy of human brain on a clinical, whole-body 5T MRI system. Approach: Stimulated echo acquisition mode (STEAM) spectra with TE=10ms were obtained in human brain at 3T and 5T. Results: The feasibility of short echo MR spectroscopy of human brain at 5T was first demonstrated. The spectral quality is substantially improved when compared to 3T. Impact: The quality of short echo MR spectra at 5T could be reached without exceeding the SAR and thus may offer additional metabolite information for large amount of clinical diagnostic applications.
Semi-supervised learning for non-invasive radiopathomic mapping of treatment naïve glioma with multi-parametric MRI
Authors: Ellison Jacob , Tran Nate , Singh Paramjot , Adegbite Oluwaseun , Phillips Joanna , Molinaro Annette , Pedoia Valentina , Luks Tracy , Shai Anny , Nair Devika , Villanueva-Meyer Javier , Berger Mitchel , Hervey-Jumper Shawn , Aghi Manish , Chang Susan , Lupo Janine
Published: November 2024

Motivation: Radiopathomic mapping of glioma could improve standard of care by helping guide surgical resection and subsequent treatment. Most current methods for predicting tumor pathology using MRI neglect intra-tumoral heterogeneity. Goal(s): We aim to use multi-parametric MRI and deep learning to spatially map pathology for treatment naïve glioma. Approach: We utilized histopathologically analyzed tissue samples taken during surgical resection with known coordinates on pre-surgical multi-parametric MRI and semi-supervised ensemble networks. Results: Our model classifies Ki-67 with an AUROC of 0.84 and 0.73 for combined Ki-67 and percent cancerous cells. Including physiologic MRI and pretraining on patches of unknown pathology improved performance. Impact: We performed radiopathomic mapping in patients with newly-diagnosed glioma using presurgical physiological + anatomical MRI and semi-supervised ensemble networks and achieved AUROCs of 0.84 and 0.73 for Ki-67 and combined Ki-67 and % cancerous cells, respectively.
Pre-operative prediction of cerebral hemodynamics for cognitive dysfunction in adults with Moyamoya Disease based on 3D-pCASL and radiomics
Authors: Wu Tingxi , Zha Xiangyue , Deng Kan , Deng Yaohong , Liu Qin , Xu Yikai
Published: November 2024

Motivation: Cognitive function in adult patients with moyamoya disease (MMD) is often impaired because of low cerebral perfusion. Goal(s): To identify brain regions where low CBF is associated with cognitive dysfunction and assess the predictive performance of radiomics models for cognitive dysfunction in adults MMD. Approach: 3D-pCASL and logistic regression analysis was employed to quantify CBF and explore independent predictors for preoperative cognitive dysfunction. And five different classifiers were used to establish radiomics models. Results: Cerebral perfusion in the left LOFL, left IPL, left SMA, and left ACG showed significant associations with cognitive impairment. The final combined model had the best predictive performance. Impact: Hypoperfusion on 3D-pCASL plays a crucial role in the detection of early cognitive impairment in adults with MMD, and the combined model that combined with CBF and radiomics features of specific brain regions showed better performance in predicting cognitive dysfunction.
An appropriate threshold for LGE images using deep learning-based reconstruction in revelation clinically unrecognized myocardial infarction
Authors: Liu Weiyin , Lu Xuefang , Yan Yuchen , Zha Yunfei
Published: November 2024

Motivation: To precisely screen out infarction in patients with unrecognized myocardial infarction (UMI) in hope of early intervention to reduce adverse cardiac events. Goal(s): To evaluate deep learning reconstruction based late gadolinium enhancement (LGEDL) in comparison with conventional reconstructed LGE (LGEO) and also to explore an appropriate threshold method for LGE measurements. Approach: LGEDL and LGEO of 77 patients diagnosed with UMI were evaluated for image quality and analyzed for MI areas using different standard deviation thresholds and a full-width-half-height (FWHM) method. Results: The STRM ≥ 4SD and ≥ 3SD is respectively reckoned as the best reference threshold for LGEDL and LGEO. Impact: The deep-learning based reconstruction LGE images had better image quality and reliable pathological evidences for detection of UMI. Significantly different Parea using threshold techniques for LGEDL and LGEO indicated the utility of STRM should be concerned.