bluuebunny/crossref_metadata_2025 huggingface dataset:
Rapid High-resolution Whole-brain 3D Multi-parametric and Multi-contrast MRI with Deep Learning-based Acquisition & Reconstruction
Authors: Shin David , Takei Naoyuki , Zhu Xucheng , Nikbeh Fara , Banerjee Suchandrima
Published: November 2024

Motivation: As the public demand for MRI grows exponentially, there is an increasing need for a one-click 3D MR exam that can generate multiple image contrasts and parametric maps as an effective way to improve patient throughput. Goal(s): Our goal was to implement an acquisition and reconstruction method that makes high-resolution whole brain multi contrast examination possible in less than 3 minutes. Approach: We implemented a deep learning-guided vast undersampled MR acquisition and a time efficient recon algorithm that uses a densely connected unrolled neural network. Results: Our proposed method preserved image quality and quantitative accuracy of the multicontrast and multiparametric images. Impact: This study demonstrates that with highly undersampled 3D QALAS acquisition combined with the DL recon algorithm, a 3-minute one-click exam is feasible that generates whole-brain high-resolution brain volumes of multiple contrasts and quantitative maps, which can enhance patient workflow in a busy clinical practice.
Latent-Optimized Adversarial Regularizers for accelerated MRI
Authors: Wang Huayu , Cheng Jing , Luo Chen , Xie Taofeng , Jin Qiyu , Cui Zhuo-xu , Liang Dong
Published: November 2024

Motivation: Introduce latent optimization techniques to enhance the interpretability of learnable regularization methods, thereby improving the performance of MRI acceleration reconstruction. Goal(s): Theoretically, we aim to elucidate the iterative direction of learnable regularization methods. Experimentally, we aim to achieve high-quality reconstruction of undersampled MRI data. Approach: Revise the optimization objective of the network by incorporating a stochastic gradient descent generator, training learnable regularizers that guide the latent process during iteration, and accomplish reconstruction using the projected gradient method. Results: Compared to other regularization methods, proposed method achieved a higher level of interpretability and accomplished higher-quality reconstruction. Impact: The method directly learns the distribution information of real data and guides the iteration towards the real data manifold. We believe that the method and its theoretical properties are undoubtedly inspiring for researchers seeking to further acquire data distribution information.
High-Resolution Deep Learning Reconstruction (HR-DLR) to Improve Sharpness in Diffusion Weighted Imaging
Authors: Shinoda Kensuke , Uematsu Shun , Takai Yuki , Kutsuna Hideaki
Published: November 2024

Motivation: Echo planar diffusion weighted imaging (EPI-DWI) often suffers from Gibbs ringing artifact and/or image blurring, because of limited matrix size. A recently proposed High-Resolution Deep Learning Reconstruction (HR-DLR) may bring a breakthrough to the limitation. Goal(s): Our goal was to test benefits of HR-DLR when applied to brain EPI-DWI. Approach: HR-DLR was compared to conventional reconstruction method (zero-filling interpolation[ZIP] and low-pass filtering) with regards to image sharpness and ringing artifact suppression, with a conventional and an accelerated scan conditions. Results: The advantage of HR-DLR over the conventional method was confirmed by measurements of edge slope width (ESW) and ringing variable mean (RVM). Impact: A recently proposed High-Resolution Deep Learning Reconstruction successfully improved the sharpness of single shot EPI-DWI while suppressing Gibbs artifacts. The method could help improve clinical confidence by increasing image resolution and gain examination throughput by shortening acquisition time.
Model based rEconstruction by Deep Algorithm unrolLing (MEDAL) for fast 3D whole-heart T2 mapping
Authors: Di Biase Alberto , Schneider Alina , Botnar Rene , Prieto Claudia
Published: November 2024

Motivation: T2 mapping provides quantitative myocardial tissue characterization. However, current approaches acquire several 2D contrast images which are then fitted to a model to estimate the T2 values, leading to limited coverage, and long acquisition and reconstruction times. Goal(s): Here we propose to speed up 3D whole-heart T2 mapping using a model-based deep learning unrolling network (MEDAL) that leverages the power of machine learning and physical knowledge. Approach: MEDAL reconstructs the T2 maps directly without reconstructing any intermediate contrast weighted images or fitting. Results: The proposed approach was evaluated in iNAV-based free-breathing 3D T2 mapping 4x accelerated showing promising results. Impact: A novel method for reconstructing parametric maps using a model-based deep learning unrolling network is presented. The method was demonstrated in a highly accelerated free breathing 3D whole-heart T2 mapping sequence allowing for fast and accurate T2 measurements.
Post-contrast multi-parametric mapping from only pre-contrast conventional weighted images
Authors: Moya-Sáez Elisa , Nunez-Gonzalez Laura , de Luis-García Rodrigo , Alberola-López Carlos , Hernandez-Tamames Juan
Published: November 2024

Motivation: Gadolinium-based contrast agents (GBCAs) have become a cornerstone in clinical routine for lesions characterization and treatment monitoring. However, issues such as safety concerns related to deposition of GBCA in the body and brain, prolonged acquisitions, and cost increase advocate against its usage. Goal(s): To replace the usage of GBCAs in post-contrast imaging with parametric maps and deep learning. Approach: A cascade of two convolutional-neural-networks for pre- and post-contrast parametric mapping and the synthesis of post-contrast T1-weighted images from only two pre-contrast conventional weighted images. Results: The proposed approach presents potential for predicting post-contrast T1w-enhancement without the usage of GBCAs. Impact: The proposed deep learning approach provides both pre- and post-contrast parametric maps and, consequently, the capability of synthesizing any post-contrast image from only two pre-contrast conventional weighted images. Thus, it paves the way towards GBCAs-free acquisitions.
Accelerated Multi-Contrast Parallel Imaging Reconstruction with Implicit Neural Representation
Authors: Roshanzamiran Ali , Heydari Amir , Kim Tae , Ahmadi Abbas , Bilgic Berkin
Published: November 2024

Motivation: Multi-contrast MR scans provide rich information for clinical diagnosis and research studies. However, long scan time is a limitation. Goal(s): an implicit neural representation is proposed for accelerated multi-contrast parallel imaging reconstruction. The proposed scan-specific method obviates the need for fully sampled priors. Approach: The spatial and temporal feature maps of an initial reconstruction are implicitly represented into the weights of a prior network. It exploits the physics-based parallel imaging forward model of sparsely sampled measurements. Results: The proposed method outperforms the evaluated parallel imaging techniques at acceleration rates as high as R=16 in both reconstructed echo images and parameter mapping. Impact: The proposed scan-specific method reconstructs multi-contrast images by implicit representation of the feature maps learned from interim reconstructions and exploitation of parallel imaging forward model in the training stage. It outperforms evaluated parallel imaging techniques.
Deep Learning-Assisted Joint Estimation for 3D Retrospective Motion Correction: An In-Vivo Validation
Authors: Nghiem Brian , Wu Zhe , Kashyap Sriranga , Kasper Lars , Uludağ Kâmil
Published: November 2024

Motivation: Data-driven retrospective motion correction methods currently face challenges with respect to robustness and long runtimes, which can be addressed by combining deep learning- and physics-based methods. Goal(s): To validate a novel deep learning-assisted joint estimation algorithm on real motion-corrupted 3D MRI data. Approach: A dataset of motion-corrupted data was acquired on 4 healthy volunteers. The performance of the proposed method was compared to a state-of-the-art deep learning method and a physics-based method. Results: The proposed method outperformed the deep learning- and physics-based methods, yielding better image correction and converging faster. Impact: The proposed retrospective motion correction method can be adopted into clinical practice as an alternative to rescanning, having demonstrated that it can salvage real motion-corrupted data without special hardware and requiring minimal sequence modifications.
A Comprehensive Quality Assessment on AI-assisted Fast MRI Acquisition for China Baby Connectome Project (CBCP)
Authors: Zhu Lixuan , Gu Zhuoyang , Cai Xinyi , Tao Tianli , Yang Qing , Yang Mingwen , Lan Zuozhen , Zhang Lin , Lin Ying , Zhang Yajuan , Huang Jiawei , Zhang Weijun , Liu Jungang , Shen Dinggang , Zhang Han
Published: November 2024

Motivation: Acquiring a high-quality 3D brain MRI in a short time is highly desired for infants/children studies. Goal(s): To optimize and systemically evaluate an innovative technology, AI-assisted Compressed Sensing (ACS), used in Chinese Baby Connectome Project (CBCP) with faster 3D-T1w scans and preserved imaging fidelity. Approach: We compared ACS with conventional techniques along the entire processing pipeline. CBCP-ACS was also compared with the existing infant cohort (BCP) with P2 acceleration regarding the derived development trajectories. Results: Results suggested that CBCP data, with halved acquisition time, had comparable quality and derived neuroscience findings to BCP, indicating that a large cohort study with ACS is mature. Impact: Equip high-resolution 3D-T1w MRI acquisition with ACS effectively shortens the acquisition time by 44%, providing a stable and robust solution for building large-scale infant/children brain imaging cohorts. The established technique could also facilitate clinical scans and patient studies.
Respiratory-resolved 4D MRI: Further enhancements on the interplay of DL reconstruction and binning strategies
Authors: Pirkl Carolin , Wang Xinzeng , Cashen Ty , de Arcos José , Milshteyn Eugene , Cozzini Cristina , Wiesinger Florian , Guidon Arnaud , Stec Sarah , Rich Karen , Harisinghani Mukesh , Hong Theodore
Published: November 2024

Motivation: To contribute to the clinical evidence generation for 4D MRI in radiation therapy planning. Goal(s): Emphasize the impact of a DL reconstruction with amplitude and phase binning on respiratory motion characterization. Approach: 4D MRI data of 10 healthy volunteers and 8 patients were acquired using a free-breathing T1-weighted stack-of-stars sequence at 1.5T or 3T. Results: Independent of the binning strategy, DL reconstruction consistently improves image quality and conspicuity of small anatomical details with the potential to shorten scan times. Differences of binning strategies become prominent for irregular breathers, where amplitude binning reveals larger motion ranges than phase binning. Impact: To foster the ultimate goal of clinical adoption of 4D MRI for radiotherapy planning, we present an enhanced 4D MRI application supporting multiple binning strategies and an embedded DL reconstruction.
Enhancing Reliability in Model-based DL Reconstruction: A Systematic Study of MC Dropout for Uncertainty Quantification
Authors: Fu Ziyu , Fujita Naoto , Terada Yasuhiko
Published: November 2024

Motivation: Monte Carlo (MC) Dropout, a powerful uncertainty quantification (UQ) method for deep learning-based reconstruction, can impact reconstruction performance. Finding ways to enhance reliability assessment without compromising performance is essential. Goal(s): This study aims to provide advisory information on how to incorporate MC Dropout into a model-based unrolled neural network, and to evaluate the reliability of UQ. Approach: Different architectures with varying dropout rates are used to assess image quality. Images with visible structural aberrations and artificial perturbation are tested. Results: Findings indicate that appropriate MC Dropout configurations improve reconstruction quality, and UQ maps effectively identify structural anomalies in images. Impact: This research enhances the reliability of DL reconstructions by systematically investigating MC Dropout’s impact on reconstruction performance, particularly in scenarios lacking ground-truth references. The findings guide the incorporation of uncertainty quantification techniques, improving the overall quality of medical imaging applications.
Integrating Deep Learning for Detection and Correction of Motion Artifacts in multi-echo GRE MRI
Authors: Ha Eun-Gyu , Jung Kyu-Jin , Al-masni Mohammed , Kim Dong-Hyun
Published: November 2024

Motivation: Motion artifacts in mGRE MRI scans reduce image quality, increasing the risk of misdiagnosis and often necessitating repeat scans, negatively impacting patient care and diagnostic accuracy. Goal(s): Our goal is to develop a novel deep learning-based framework for reducing motion artifacts in mGRE MRI k-space data, ensuring the generation of high-quality images. Approach: The methodology proceeds by detecting and correcting motion-corrupted phase encoding lines within the k-space domain, employing a two-stage DeepFillv2 algorithm. It also integrates motion parameter estimation to enhance the framework's robustness. Results: The model’s effectiveness in identifying and rectifying motion artifacts in MRI was confirmed through quantitative and qualitative evaluation. Impact: The proposed k-space domain framework progresses by identifying phase encoding lines affected by motion and repairing them using deep learning techniques, thereby proving improved image quality and demonstrating potential as a diagnostic aid.
A Low-Cost, Handheld, and Real-Time 2D MR Imager
Authors: Kuang Irene , Adalsteinsson Elfar , White Jacob
Published: November 2024

Motivation: To enable 2D phantom imaging using low-cost, handheld MRI for point-of-care and educational applications. Goal(s): Optimize a handheld spokes-and-hub magnet for 2D imaging, focusing on cost-effective design to create a comprehensive imaging system with affordability, accessibility, and versatility. Approach: This research extends prior studies focusing on a handheld spokes-and-hub magnet. Spin echo and turbo spin echo phantom imaging is enabled through optimization of magnet's design, RF pulses, and digitally-controlled H-bridge gradient coil drive. Results: 64x64 pixel MR images obtained in seconds from 10 mm diameter phantoms using a duration-phase-encoded TSE sequence on our redesigned handheld imager (spokes-and-hub magnet, dithered-pulse RF, switch-driven gradients). Impact: From magnet design and budget-friendly hardware constraints, we delve into 2D image acquisition in handheld spokes-and-hub MRI. Our tools aid iterative design, demonstrating viability for education and potential clinical use. Advancing low-field MRI, this work boosts MR accessibility and affordability.
Design of novel loop coil array for B0 and gradient field generation
Authors: Ha Yonghyun , Samardzija Anja , Sun Chenhao , Sun Heng , Gross Ryan , Galiana Gigi , Constable R. Todd
Published: November 2024

Motivation: By adjusting individual coil currents, a loop coil array can generate a uniform B0 field and it offers the flexibility to control gradient field direction making it suitable for compact MRI systems. Goal(s): The goal is to show that the loop coil array produces a uniform and/or gradient magnetic fields. Approach: The current values of individual loops for a uniform magnetic field were determined by ptimization to achieve either uniform field strength, whose orientation can be rotated, or a linear field gradientResults: A method for calculating required current values has been presented, and simulations validate its effectiveness. Impact: Suggested coil arrays offer the potential for developing more compact and lighter MRI systems by substituting traditional magnets and 3-axis gradient coils in current low-field MRI setups.
Effect of electromagnetic interference on very low-field images
Authors: Poojar Pavan , Oiye Ivan Etoku , Geethanath Sairam
Published: November 2024

Motivation: Very low-field MRI offers several benefits related to accessibility. However, electromagnetic interference (EMI) poses a significant challenge by degrading SNR. Hence, evaluating the effect of EMI on image quality is improvement. Goal(s): The study aims to investigate the influence of EMI on image quality using 50mT. Approach: We varied the distance and amplitude of the EMI producing coil from signal generator. We analyzed the effect of EMI on the image quality. Results: As expected, the SNR decreases with an increase in amplitude. The standard deviation and RMS of the background increase as the increase in amplitude and decrease in distance. Impact: Highlight the influence of EMI on image quality.The results indicate that as the amplitude of the coil increases or the distance between the transmitter and scanner decreases, the signal-to-noise ratio decreases, the standard deviation and RMS of the background increase.
Design and comissioning of a new field-cycling imaging scanner for clinical applications
Authors: Ross James , Davies Gareth , Lurie David , Broche Lionel
Published: November 2024

Motivation: Field-cycling imaging is a new imaging technique that allows access to new imaging biomarkers. To allow investigation of clinical applications a high performance and patient acceptable scanner is needed. Goal(s): We have constructed a new clinical-grade resistive field-cycling imaging system operating at 0.2 T with a 60 cm bore and high performance gradient and magnet amplifiers, with a custom made outer covering. Approach: The system is in the late stage of commissioning. The field homogeneity and temporal stability have been assessed. Results: The final system is visually attractive and has < 1 ppm temporal stability and < 8 ppm field homogeneity. Impact: This system will allow us to investigate the clinical utility of field-cycling and low-field imaging in a much larger patient population. The high performance gradient amplifiers will enable much faster imaging, which will enable new possibilities for new sequence development.
Beyond Boundaries – A versatile Console ­ for Advanced Low-Field MRI
Authors: Schote David , Silemek Berk , Seifert Frank , Kolbitsch Christoph , O'Reilly Thomas , Kofler Andreas , Webb Andrew , Winter Lukas
Published: November 2024

Motivation: We challenge proprietary barriers in low-field MRI to enhance methodological integration. Our focus is on improving system versatility for advanced imaging. Goal(s): To create a versatile, MRI console driven by open-source software, capable of integrating sophisticated low-field imaging techniques. This involves for instance additional sensors or real-time adaptions. Approach: We implemented Spectrum-Instrumentation measurement cards with a high-performance reconstruction system. The open-source Python software, incorporating a Pulseq interpreter, allows to streamline flexible, fast, and transparent low-field imaging applications. Results: Successful implementation evidenced by high-fidelity Pulseq sequence execution to image 3D printed brain phantoms on a system capable of in-vivo applications. Impact: The open and versatile design of our proposed console paves the way for advanced techniques in low-field MRI, enabling widespread adoption in research facilities and fostering innovative MRI applications in resource-limited settings.
Improvements in R1 mapping at ultra-low field using denoised and motion corrected field-cycling MRI in brain and head
Authors: Senn Nicholas , de Vries Clarisse F. , Ayde Reina , Krishna Adarsh , Mallikourti Vasiliki , Ross P. James , Broche Lionel M. , MacLeod Mary-Joan , Waiter Gordon D.
Published: November 2024

Motivation: Field-cycling MRI makes it possible to characterise the clinical potential of endogenous spin-lattice R1 image contrast that arises at ultra-low-magnetic field strengths. Goal(s): Our goal was to examine the extent translated motion correction and denoising approaches improve the utility of R1 mapping performed at ultra-low field. Approach: : Improvements in sensitivity to differences in R1 between tissue types and fitting accuracy were determined across brain and head tissues. Results: Improvements in sensitivity and goodness of fit were observed. Significant difference in R1 values between regions of white matter and confluent small vessel disease were observed between 0.2 - 200 mT. Impact: The improvement in sensitivity of R1 mapping using translated motion correction and denoising approaches provides new opportunities to assess the clinical potential of new endogenous image contrast mechanism at ultra-low field strengths.
Concomitant field effects in MR Elastography
Authors: Darwish Omar , Sinkus Ralph , Neji Radhouene
Published: November 2024

Motivation: To our knowledge, concomitant field effects in MRE have not been investigated yet, which might become of importance when translating MRE to low field MR systems. Goal(s): Propose a framework to investigate concomitant field effects on MRE, in particular Hadamard-encoded 3D MRE at 0.55T. Approach: A 6x6 encoding scheme is proposed to study the effects of concomitant fields on 3D MRE at 0.55T in phantom experiments. Results: Phantom experiments demonstrated that the effects of concomitant fields on 3D Hadamard-encoded MRE at 0.55T are negligible. Impact: A framework to assess concomitant field effects in MRE, in particular Hadamard-encoded 3D MRE at 0.55T in phantom experiments.
Denoising diffusion MRI with an improved non-local principal component analysis approach
Authors: Ye Xinyu , Ma Xiaodong , Pan Ziyi , Zhang Zhe , Auerbach Edward , Guo Hua , Uğurbil Kâmil , Wu Xiaoping
Published: November 2024

Motivation: Previously, we proposed an improved 2-step non-local principal component analysis (PCA) approach and demonstrated its utility for denoising diffusion MRI with many diffusion directions. Goal(s): Our goal here was to investigate how our approach would benefit diffusion tensor MRI (DTI) with a few diffusion directions. Approach: we evaluated our approach’s denoising performances using both simulation and human-data experiments, and compared the results to those obtained with existing local-PCA-based methods. Results: Our approach substantially enhanced image quality relative to the noisy counterpart, yielding improved performances for estimation of relevant DTI metrics. It also outperformed existing local-PCA-based methods in reducing noise while preserving anatomic details. Impact: Capable of improving image quality for DTI with reduced diffusion directions, our improved non-local PCA denoising approach is believed to have utility for many applications, especially those targeting quality DTI or parametric mapping or both within a clinically relevant timeframe.
Resting-state fMRI network efficiency mediates the association between glymphatic system and cognition function in OSAHS: a DTI-ALPS study
Authors: Xiong Zhenliang , Bai Mingxian , Wang Zhongxin , Wang Rongpin , Nie Lisha , Zeng Xianchun
Published: November 2024

Motivation: Obstructive Sleep Apnea Hypopnea Syndrome (OSAHS) is a serious sleep disorder linked to cognitive impairment. This study investigates the glymphatic system's role in OSAHS and its impact on cognition. Goal(s): Explore glymphatic system changes in OSAHS and their influence on cognitive function. We aim to uncover the mechanisms connecting OSAHS and cognitive issues. Approach: We use DTI-ALPS and rs-fMRI to assess the glymphatic system and brain network properties. Correlations and mediation analysis examine the link between glymphatic function, cognitive abilities, and OSAHS severity. Results: OSAHS patients exhibit reduced glymphatic function, affecting cognitive performance. Impact: This study may enhance comprehension of cognitive problems in OSAHS, suggesting dementia risk. It could inform early intervention, enhancing OSAHS patients' quality of life.
Evaluation of Microstructural Brain Abnormalities and Cognitive Impairment of OSAHS patients with axial and radial kurtosis from DKI
Authors: Zhang Ning , Peng Kun , Guo Jinxia
Published: November 2024

Motivation: Microstructural brain changes resulted from obstructive sleep apnea hypopnea syndrome（OSAHS）related cognitive impairment can help to understand the potential neuropathological mechanism. Goal(s): To assess the brain microstructural alteration in patients with OSAHS using DKI imaging. Approach: 33 OSAHS patients and 33 healthy controls were performed DKI/3D-T1WI MRI imaging. The AK and RK in different brain regions were extracted. Results: Significant differences were found in AK and RK values in various brain regions for OSAHS group compared to HC group. Abnormal changes in some crucial brain regions were found to be correlated with MoCA score and sleep parameters. Impact: Altered AK and RK in brain regions associated with the microstructural change in OSAHS, such as demyelination and axonal destruction. The correlation of respiratory parameters and scoring indicators is conducive to more objective assessment for cognitive level of patients.
Investigating Postmortem Neurite Orientation Dispersion and Density Imaging Parameters and Their Influences on Deceased Human Brains
Authors: Bauer Melanie , Berger Celine , Zirn Andrea , Scheurer Eva , Ropele Stefan , Lenz Claudia
Published: November 2024

Motivation: Neurite orientation dispersion and density imaging (NODDI) is expected to advance our still incomplete understanding of diffusion properties in postmortem brains. Goal(s): We examined NODDI parameters in deceased human brains in situ and investigated their associations with postmortem interval (PMI), age at death and core temperature. Approach: Ten subjects underwent postmortem in situ brain MRI, enabling NODDI analysis. Correlations between NODDI parameters and external factors were assessed. Results: The results revealed higher NODDI parameters in deceased compared to living subjects. Longer PMIs were associated with increased fractional intracellular volume (FICVF) and orientation dispersion index (ODI) values, while higher temperatures had the opposite effect. Impact: Our study expands the understanding of postmortem brain microstructure. NODDI's potential in deceased brain analysis and its relation to postmortem interval and temperature pave the way for further research with applications in diagnostics and forensics.
Correcting the unwanted diffusion-weighting in STEAM-DWI Sequence for Time-Dependent Diffusional Kurtosis Imaging
Authors: Zhu Qinfeng , Li Haotian , Ba Ruicheng , Hsu Yi-Cheng , Yan Xu , Wu Dan
Published: November 2024

Motivation: Time-dependent diffusion kurtosis imaging (tDKI) enables the noninvasive mapping of transmembrane water exchange by measuring diffusion signals at varying diffusion-times. To access long diffusion-times, the diffusion-weighted STEAM sequence is typically used, in which the unwanted diffusion weighting produced by the crushers and slice selection gradients. Goal(s): We first showed that this unwanted weighting, particularly on the b0 image led to an underestimation of kurtosis. Approach: We proposed a strategy to resolve this problem by removing the crusher gradient while adding a smaller and fixed b value to the b0 acquisition. Results: Proposed strategy showed reasonable tDKI measurements in phantom and human brain experiments. Impact: This study presented an important issue of unwanted diffusion-weighting in STEAM-DWI, which led to inaccurate estimation of tDKI-based transmembrane water exchange. We proposed a strategy that removed the crusher and dynamically adjusting the diffusion gradient to achieve the desired b-value.
Effectiveness of IVIM/DWI MRI parameters in evaluating anti-PD-1 antibody treatment response in xenograft models in vivo
Authors: Hayami Sawako , Iima Mami , Nobashi Tomomi , Someya Yuko , Imai Hirohiko , Le Bihan Denis , Nakamoto Yuji
Published: November 2024

Motivation: Although intravoxel incoherent motion (IVIM) parameters are garnering attention as a DWI analytical method, their effectiveness in evaluating the immunotherapy efficacy is still largely unvalued. Goal(s): Our goal was to assess if IVIM/DWI parameters reflect tissue changes subsequent to anti-PD-1 antibody treatment in vivo. Approach: Colon (CT26) and breast (4T1) cancer xenograft murine models received the treatment and afterward MR study. Results: fIVIM (flowing blood volume fraction) was significantly lower in the treated tissue in the CT26 model, while no change was observed in the 4T1 model. Also, there was no difference in the lesion size in both models. Impact: The difference of fIVIM (flowing blood volume fraction) parameter and lesion size between two anti-PD-1 antibody-treated xenograft models might reflect a different level of treatment efficacy, which may indicate the IVIM potential to observe tissue heterogeneity.
A comparison of four non-Gaussian diffusion models combined with whole-tumor histogram analysis for evaluating cervical cancer
Authors: Yang Lu , Duan Xiaohui , Wang Mengzhu , Qin Haodong , Chen Zhao , Hu Huijun , Yan Zhuoheng , Yan Xu
Published: November 2024

Motivation: Non-Gaussian diffusion models can effectively characterize the microstructure of tissues. Goal(s): To investigate the potential predictive value of multiple non-Gaussian diffusion models for assessing cervical cancer (CC). Approach: Diffusion parameters derived from continuous-time random-walk (CTRW), diffusion-kurtosis imaging (DKI), fractional order calculus (FROC) and intravoxel incoherent motion (IVIM) were calculated. The most significant histogram features selected by univariate analysis and multivariate logistic regression were used to establish predictive models. The predictive performance was evaluated by receiver operating characteristic (ROC) analyses. Results: The combination of multiple non-Gaussian diffusion models and whole-tumor histogram analysis could distinguish pathological types and differentiation degree in CC. Impact: Predicting pathological types and differentiation degree of cervical cancer is crucial for appropriate treatment and prognosis. The use of multiple non-Gaussian diffusion models combined with whole-tumor histogram analysis offers a precise and non-invasive solution to this clinical issue.
Assessment of mesenchymal stromal cells promoting liver regeneration via intravoxel incoherent motion diffusion-weighted imaging
Authors: Wang Xu yang , Xie Shuang shuang , Shen Wen
Published: November 2024

Motivation: Scholars are proposing the use of mesenchymal stem cells (MSCs) to promote liver regeneration and prevent postoperative liver function failure; however, no method currently exists for evaluating their effectiveness in vivo. Goal(s): We assessed whether intravoxel incoherent motion (IVIM) could be used to assess changes in liver regeneration in rats after partial hepatectomy. Approach: Rats undergoing stem cell therapy underwent imaging examinations at baseline and postoperative days 1, 2, 3, 5, 7, and 14, and were compared with untreated rats. Results: Liver D, D*, and PF differed significantly between control rats and those receiving MSCs at all time points. Impact: Mesenchymal stem cells can promote liver regeneration by inducing cell hypertrophy and extending the proliferation period of liver cells. IVIM parameters enable monitoring changes in liver blood perfusion after mesenchymal stem cell therapy and show correlations with pathological indicators.
Predicting estrogen receptor factor expression based on tumor habitats in patients with malignant breast cancer: a feasibility study
Authors: Duan Mengting , Zhai Yingmin , Wang Mengzhu , Song Yang , Zhang Chengxiu , Yang Guang , Liu Haifa , Liu Hui
Published: November 2024

Motivation: Immunohistochemistry remains the gold standard for diagnosing the estrogen receptor (ER) factor in breast cancer. However, the non-invasive IVIM radiomics method offers a meaningful alternative for predicting ER expression. Goal(s): The objective of this study is to assess the feasibility and performance of a prediction model based on tumors habitats in predicting ER expression in malignant breast cancer patients. Approach: We developed and evaluated a predictive model that utilizes tumors habitats to diagnose ER expression. Results: The IVIM indicators can be employed for tumor subregions’ segmentation to hypoxia. The model demonstrates effective prediction capabilities for determining the probability of ER-positive in breast cancer. Impact: Our research highlights the excellent performance of the predictive model based on tumors habitats in predicting ER expression in breast cancer. The model can aid clinicians in making more informed treatment decisions and subsequently improve patient outcomes.
The impact of deep learning based image reconstruction on IVIM for benign and malignant pulmonary lesion discrimination
Authors: Fan Li , Li Jie , Xia Yi , Dai Jiankun , Shi Jie , Sun Guangyuan , Xu Meiling , Lin Xiaoqing , Liu Shiyuan
Published: November 2024

Motivation: DWI-based IVIM model provides information about tumor microenvironment and is reported useful in discriminating pulmonary lesions. However, lung DWI suffers low SNR and substantial susceptibility artifacts. Recently, a vendor-provided deep-learning reconstruction (DLR), relative to conventional reconstruction (ConR), is provided to improve image quality. Goal(s): Investigate the impact of DLR on IVIM parameters for distinguishing malignant from benign pulmonary lesion. Approach: DWI was acquired using FOCUS-MUSE to alleviate susceptibility artifacts and was reconstructed with DLR and ConR, separately. SNR and diagnostic performance of IVIM were compared. Results: DLR significantly increased DWI image SNR and improved diagnostic performance of IVIM parameters for pulmonary lesion discrimination. Impact: The application DLR would be beneficial for lung DWI in term of image quality and the performance of IVIM quantitative parameters for distinguishing benign from malignant pulmonary lesion.
Investigating SMS in dynamic MR for Free-Breathing Functional Lung Imaging
Authors: Ilıcak Efe , Stäb Daniel , Speier Peter , Strecker Ralph , Zöllner Frank
Published: November 2024

Motivation: Pulmonary functional imaging is critical for diagnosing lung diseases. However, sequential acquisition of multiple slices hinder the investigation of concurrent breathing dynamics while prolonging the overall the acquisition time. Goal(s): Our goal is to investigate the use of simultaneous multi-slice (SMS) imaging as an alternative approach for accelerating dynamic acquisitions for functional lung imaging. Approach: We obtained dynamic images using bSSFP and GRE acquisitions at 1.5T, from two healthy volunteers. Afterwards, registered images were analyzed using dynamic mode decomposition to generate pulmonary ventilation and perfusion maps. Results: Functional maps were obtained using both pulse sequences with SMS in both sagittal and coronal views. Impact: Dynamic lung imaging often requires multiple slices for volumetric coverage, which can be time-consuming. Simultaneous multi-slice (SMS) technique enables the acquisition of multiple slices at the same time, thus enabling the observation of concurrent breathing dynamics in an efficient manner.
Correction of inhaled volume in 19F gas wash-in MRI for improved lung ventilation assessment
Authors: Scheller Julienne , Gutberlet Marcel , Obert Arnd , Müller Robin , Greer Mark , Klimeš Filip , Wacker Frank , Vogel-Claussen Jens
Published: November 2024

Motivation: In 19F MR pulmonary ventilation imaging, variations in inspiratory gas volumes can compromise the evaluation of dynamic ventilation parameters. Goal(s): To account for these variations, a correction of the inhaled volumes (CIV) can be applied during post-processing. Approach: This study examined the repeatability of dynamic ventilation parameters and the correlation with spirometric lung function testing with and without CIV in 24 patients with chronic obstructive pulmonary disease. Results: Using CIV increases the correlation of all assessed ventilation parameters with spirometry as well as their repeatability in terms of intraclass correlation coefficient and coefficient of variation between different scans. Impact: The increased repeatability and stronger correlation with spirometry suggest that the implementation of CIV can improve the evaluation of the pulmonary gas wash-in process retrospectively, without requiring additional MR scans or changes in the experimental procedure.
Evaluating R1 and T1w/T2w as myelin-sensitive measures compared to macromolecular proton fraction
Authors: Sui Yu , Storey Pippa , Samsonov Alexey , Lazar Mariana
Published: November 2024

Motivation: In vivo myelin mapping of the human brain holds great research significance due to the critical role that myelin health plays in both normal and neuropathological conditions. Goal(s): To quantitatively assess the relationship and concordance between MRI-based myelin-sensitive metrics, which are not well understood in current literature. Approach: Using the macromolecular proton fraction (MPF) as a standard myelin marker, we compared the longitudinal relaxation rate (R1) and T1w/T2w image ratio and their reliability across tissue types. Results: We show that R1 corresponds well with MPF across the brain while T1w/T2w is reasonably reliable in only limited areas. Impact: By quantitatively comparing R1 and T1w/T2w with more established myelin marker MPF, we highlight their varying levels of concordance across tissue types, which informs future studies planning to use R1 or T1w/T2w as myelin proxies in the brain.
Microstructure-Informed Susceptibility Source Separation (MI-SSS) for Improved Estimation of Neural Myelin and Iron Content
Authors: Şişman Mert , Nguyen Thanh , Kovanlikaya Ilhami , Dimov Alexey , Schwartz Hannah , Spincemaille Pascal , Gauthier Susan , Wang Yi
Published: November 2024

Motivation: Current approaches to identify diamagnetic and paramagnetic susceptibility sources in the brain suffer from confounding effects caused by microstructure or pathological changes such as edema. Goal(s): The aim of this study is to present the microstructure-informed framework developed for the improved estimation of diamagnetic and paramagnetic sources free from confounding effects of fiber orientations and edema. Approach: We employ the biophysical modeling-based generation of gradient-echo signals and stochastic matching pursuit for the parameter estimation via a pre-computed dictionary. Results: The results show that MI-SSS is robust against the fiber orientation dependent field effects and increased tissue water. Impact: This study introduces MI-SSS as an improved susceptibility source separation technique. The aim is to map diamagnetic and paramagnetic source distributions inside the brain free from the confounding effects of fiber orientation and water content changes such as in edema.
Subdivisional Iron Deposition and “Swallow Tail Sign” of Substantia Nigra in RRMS Patients by Quantitative Susceptibility Mapping
Authors: Zeng Chun
Published: November 2024

Motivation: Regionally progressive iron deposition has not been indicated in the substantia nigra (SN), which is the main impaired region in relapsing-remitting multiple sclerosis (RRMS). Goal(s): To study the iron accumulation in substantia nigra (SN) subdivisions and “swallow tail sign” in RRMS patients with different DDs. Approach: Quantitative susceptibility mapping (QSM) values were measured in the anterior and posterior SN at different levels. Results: QSM values of the SN were higher in RRMS patients with 5-10 years DDs than controls. There were significantly positive correlations between “swallow tail sign” scores and QSM values of the caudal pSN in the patients with long DDs. Impact: This is a preliminary study providing objective evidence of the iron-related progression of SN subregions in RRMS patients with different DDs, and abnormal “swallow tail sign” may provide an additional imaging maker for MS patients.
Increased Cerebral Lactate-to-Pyruvate Ratio in Primary Progressive Multiple Sclerosis using Hyperpolarized Pyruvate MRI.
Authors: McGing Jordan , Pisa Marco , Lockhart Andrew , Yeung Kylie , Axford Aaron , Mills Rebecca , Shinozaki Ayaka , Lewis Andrew , Birkhoelzer Sarah , Berner Lise , Zaccagna Fulvio , Schulte Rolf , Rider Oliver , De Luca Gabrielle , Tyler Damian , Grist James
Published: November 2024

Motivation: There remains an absence of imaging modalities capable of probing the neuroinflammatory processes that precede the well-defined brain structural changes in Primary Progressive Multiple Sclerosis (PPMS). Goal(s): We investigated whether hyperpolarized [1-13C]pyruvate MRI can delineate alterations in cerebral glycolytic and oxidative metabolism between treatment naïve PPMS and healthy volunteers. Approach: Two treatment naïve PPMS patients and two sex matched healthy volunteers underwent [1-13C]pyruvate MRI to characterise cerebral glycolytic and oxidative metabolism. Results: A global increase in [1-13C]lactate: [1-13C]pyruvate was found in both PPMS patients relative to sex-matched healthy controls (0.23 ± 0.12 vs 0.16 ± 0.08). The 13C bicarbonate:[1-13C]pyruvate ratio was no different. Impact: These preliminary findings demonstrate a global increase in cerebral glycolytic metabolism in treatment naïve PPMS relative to age and gender matched healthy controls. This may reflect diffuse neuroinflammatory processes and suggests [1-13C]pyruvate MRI could be used to monitor disease activity.
Microstructure-informed brain segmentation in multiple sclerosis using CAQE
Authors: Balaji Sharada , Obajtek Marek , Vavasour Irene , Dvorak Adam , Johnson Poljanka , Gilbert Guillaume , Tam Roger , Laule Cornelia , Li David , Traboulsee Anthony , MacKay Alex , Kolind Shannon
Published: November 2024

Motivation: Progression in neurodegenerative diseases such as multiple sclerosis (MS) involves tissue damage invisible on conventional MRI scans. Quantitative measures of tissue microstructure may be more informative. Goal(s): To segment MS brain MRI data based on quantitative microstructural MRI measures without spatial input. Approach: 23 MS brain scans were segmented based on clustering healthy quantitative data using an unsupervised Clustering for Anatomical Quantification and Evaluation (CAQE) framework. Classifications of lesions and normal appearing tissue were compared to a healthy atlas segmentation. Results: MS brains showed several differences from healthy classification in normal-appearing regions on conventional MRIs. Periventricular lesions were generally classified consistently. Impact: Using only microstructural features, the CAQE framework can classify diseased tissue in more detail than conventional segmentation algorithms based on qualitative MRI scans, and provide useful information for improved diagnosis, follow-up and more personalized care.
Subtraction Map Pipeline to Assess Longitudinal Changes in Multiple Sclerosis at Portable Ultra-Low-Field MRI
Authors: Donnay Corinne , Okar Serhat , Poorman Megan , Reich Daniel , Nair Govind
Published: November 2024

Motivation: Ultra-low-field (ULF) MRI is more patient-accessible due to its cost effectiveness and portability and could in principle allow more frequent follow-up in disabling neurological diseases like multiple sclerosis (MS). However, using ULF MRI to track longitudinal changes is challenging due to reduced SNR and CNR. Goal(s): To develop an ULF longitudinal subtraction pipeline. Approach: We developed a subtraction pipeline for ULF images and assessed longitudinal changes in 14 scans from 12 MS participants. Results were compared with an MS neurologist’s impression of high-field images. Results: Our pipeline was able to detect new lesions and longitudinal changes in MS on par with high-field MRI. Impact: A longitudinal subtraction pipeline, implemented on ultra-low field MR images, was useful for monitoring interval changes in patients with MS, augmenting the clinical utility of follow-up MRI.
Pseudo-Label Assisted nnU-Net (PLAn) Enables Brain Segmentation at 7T
Authors: Donnay Corinne , Dieckhaus Henry , Tsagkas Haris , Gaitán María , Beck Erin , Reich Daniel , Nair Govind
Published: November 2024

Motivation: Brain segmentation is more challenging at 7T compared to 3T, primarily due to increased bias fields and other artifacts. Generating training data for 7T brain segmentation is tedious, making transfer learning based models a more feasible option. Goal(s): Brain and lesion segmentation algorithm for use with 7T images in multiple sclerosis. Approach: A 3T to-7T transfer learning algorithm (called PLAn) for skull stripping, lesion, and brain segmentation was trained and tested on participants clinically diagnosed with multiple sclerosis. Results: In both quantitative and qualitative analysis, PLAn significantly outperformed other segmentation methods including nnU-Net in lesion and brain segmentation. Impact: Brain volume is a commonly used marker of disease progression in various neurological and neuropsychiatric diseases; however it is more difficult to implement on 7T images. PLAn, a deep-learning algorithm, can produce fast and reliable whole-brain segmentations.
Lesion-Aware Myelin-Weighted Connectomics in the phase II SYNERGY Trial
Authors: Bosticardo Sara , Lu Po-Jui , Schädelin Sabine , Daducci Alessandro , Granziera Cristina
Published: November 2024

Motivation: Microstructure-informed tractography methods face challenges in presence of focal lesions. Goal(s): This study aims to apply an extension of Myelin Streamline Decomposition (MySD) which accounts for lesions to patients with multiple sclerosis (MS) enrolled in a completed phase 2 clinical trial (SYNERGY) with conventional DTI acquisitions. Approach: We applied our novel approach to perform network analysis in SYNERGY’s secondary progressive (SP) and relapsing-remitting (RR) patients. Results: Using MySD applied to patients with focal pathology, we showed that SPMS patients had increased alterations in myelin-weighted network properties compared to RRMS. Myelin-weighted networks also exhibited correlations with motor and cognitive impairment. Impact: For the first time, we applied our novel connectomics approach explicitly designed to cope with focal pathology to MS patients in a clinical trial, demonstrating its sensitivity and adaptability to real-world clinical data.
qMRI-based classification of active vs. inactive multiple sclerosis lesions – towards eliminating the need for contrast agent injections
Authors: Mehalev Hadas , Zlotzover Sharon , Helft Coral , Sahar Moni , Blumenfeld-Katzir Tamar , Khoury Stephani , Didi Shir , Nijim Ruba , Deeb Seham , Radunsky Dvir , Reichman Dominique Ben-Ami , Hoffmann Chen , Shrot Shai , Ben-Eliezer Noam
Published: November 2024

Motivation: The gold standard way for assessing Multiple sclerotic (MS) disease activity is by identifying new active lesions using contrast enhanced imaging. The repeated use of gadolinium injections for MS patients constitute a major concern due to long-term accumulation and even breakdown of this agent in the brain and body without efficient clearance. Goal(s): Classify active vs. inactive MS lesions using quantitative MRI (qMRI) without the need for contrast-enhanced imaging. Approach: Machine learning classifier trained on qMRI features of MS lesions. Results: qMRI profiling has the potential to classify MS lesions into active/inactive state with accuracy of 81.7 ± 10 %. Impact: Multiple sclerosis disease activity is assessed using contrast-enhanced MRI. Recently, concerns have been raised regarding the long-term accumulation and breakdown of contrast agents in the brain. This study introduces a qMRI-based and contrast-free approach for assessing multiple sclerosis disease activity.