bluuebunny/crossref_metadata_2025 huggingface dataset:
Beyond the Tumor Region: Peritumoral Radiomics Reshapes Prognostic Accuracy in Rectal Cancer
Authors: Liang Zhiying , Li Haojiang , Liu Lizhi , Deng Kan , Chen Biyun
Published: November 2024

Motivation: The prognostic value and importance of peritumoral radiomics remain underexplored in locally advanced rectal cancer (LARC). Goal(s): To investigate the prognostic significance of peritumoral versus intratumoral radiomic features in LARC. Approach: In a retrospective cohort of 409 patients with LARC, we extracted intratumoral and peritumoral radiomic features from pretreatment high-resolution small-field-of-view T2-weighted images. Various prognostic models incorporating clinicopathological and radiomic data were constructed and compared. Variable importance was analyzed. Results: Peritumoral features demonstrated equivalent or superior prognostic value than intratumoral features, significantly enhanced accuracy of models relying on intratumoral or intratumoral-clinicpathological features. One peritumoral feature emerged as the leading predictor. Impact: Peritumoral radiomics provides equal or even greater prognostic value compared to intratumoral radiomics, promising to enhance accuracy in prognostic estimation for locally advanced rectal cancer and thus facilitating personalized treatment strategies.
Handy Hepatic Veins Segmentation on MR Images using Foundation Models
Authors: Peng Haichao , Luo Jie , Luo Xiongbiao
Published: November 2024

Motivation: Accurate hepatic vessel segmentation can help to identify and avoid critical blood vessels during liver tumor ablation or resection. Existing methods are not accessible to most medical institutes, leading to questionable clinical relevance. Goal(s): we present a handy foundation model-based hepatic vessel segmentation approach crafted for straightforward integration into clinical applications. Approach: We employ a parameter-efficient few-shot learning strategy to fine-tune the foundation model, thereby enabling it to achieve competitive hepatic vessel segmentation performance with training on only five cases. Results: The proposed method is effective and easy-to-access, and it has the potential for a substantial impact on clinical practice. Impact: Existing hepatic vessel segmentation methods are not accessible to most medical institutes, leading to questionable clinical relevance. We present a clinically practical foundation model-based approach that achieves competitive performance with training on only five cases.
Accurate Estimation of Kidney Volume Growth Rates from abdominal MRI via Fitting to multiple Imaging Timepoints (FIT) in ADPKD
Authors: Hu Zhongxiu , Sharbatdaran Arman , He Xinzi , Zhu Chenglin , Dev Hreedi , Blumenfeld Jon , Prince Martin
Published: November 2024

Motivation: Height-adjusted total kidney volume (htTKV) growth rate measured on MRI or CT is a critical biomarker for monitoring autosomal dominant polycystic kidney disease (ADPKD) progression. Goal(s): This study aims to develop a tool for accurate calculation of the htTKV growth rate based on all available MRI scans. Approach: Accuracy of four MRI methods for calculating htTKV growth rate were assessed as compared to ground truth calculated from 10+ years of imaging follow up. Results: Using 2-parameter least squares fitting with 3+ scans or 5+ years of follow up reduce error 2-fold compared to the current clinical standard, Mayo Imaging Classification. Impact: Accurate estimation of kidney growth rate on abdominal MRI using FIT will enable better prediction of disease progression and response to therapy in patients with ADPKD.
fastMRI Prostate: A Public, Biparametric MRI Dataset to Advance Machine Learning for Prostate Cancer Imaging
Authors: Tibrewala Radhika , Dutt Tarun , Tong Angela , Ginocchio Luke , Lattanzi Riccardo , Keerthivasan Mahesh , Baete Steven , Chopra Sumit , Lui Yvonne , Sodickson Daniel , Chandarana Hersh , Johnson Patricia
Published: November 2024

Motivation: There is a lack of publicly available, raw k-space data for prostate MRI. Goal(s): To compile and release raw k-space data for clinical prostate MRI and demonstrate its utility for development of deep learning methods for image reconstruction and automated diagnosis. Approach: Biparametric MRI data from 312 patients with associated prostate cancer labels were added to the public fastMRI repository. Deep-learning models were trained on the data to reconstruct images from undersampled k-space and perform automated diagnosis of prostate cancer (PCa) on these images. Results: SSIM > 0.866 and AUC > 0.80 (test set) for the deep-learning reconstruction and automated PCa diagnosis respectively. Impact: Raw k-space data with clinical labels from fastMRI prostate will enable researchers to develop clinically relevant deep-learning reconstruction and automated diagnosis models which may ultimately advance the diagnosis and management of prostate cancer.
Synthetic derivative T2-weighted abdominal images from T1-weighted images using a generative adversarial network (GAN)
Authors: Zhang Shu , Martin Phillip , Gupta Nakul , Altbach Maria , Bilgin Ali , Martin Diego
Published: November 2024

Motivation: Either fast 2D T2-weighted abdominal imaging or 3D T2 MIP techniques have limitations. There remains a need for fast 3D T2 abdominal high-resolution imaging. Goal(s): To develop a conditional GAN model to synthesize T2-weighted images from 3D high-resolution T1-weighted abdominal images preserving spatial resolution of the source images. Approach: Abdominal images acquired from 39 volunteers were included for the study. A conditional GAN model was trained to generate T2-weighted images from T1-weighted images slice by slice. Results: Overall, the generated T2-weighted images were similar to the real T2-weighted images, though some contrast differences in the bowels and kidneys were seen. Impact: This proof of principle study shows the GAN model can be used to generate T2-weighted images from T1-weighted images, with the potential for rendering high quality volumetric 3D high-resolution abdominal T2-weighted images that is superior to current 3D MIP methods.
Fatty acid characterization of vertebral body marrow using MR Z-spectral imaging at 3 T
Authors: Kuang Junfeng , Qi Yulong , Wu Qiting , Zhou Yang , Cheng Guanxun , Zheng Hairong , Wu Yin
Published: November 2024

Motivation: MR Z-spectral imaging (ZSI) offers a new way to generate fat spectrum. However, its feasibility for fatty acid characterization remains to be elucidated. Goal(s): This study designed a ZSI protocol and investigated its capability in fat measurement. Approach: The designed ZSI protocol was tested on a fat-water phantom and vertebral body marrows in healthy volunteers and osteoporosis patients at 3 T. Results: ZSI-measured fat fraction (FF) significantly correlated with oil volumes in the phantom. Moreover, osteoporosis patients exhibited significantly higher normalized fat peak amplitudes and FF than healthy volunteers, indicating the ability of ZSI in revealing fatty acid differences under different pathological states. Impact: The designed ZSI protocol was feasible for fatty acid characterization. Significant differences of fatty acid metrics were detected between osteoporosis patients and healthy volunteers, suggesting the potentials of the designed ZSI protocol in facilitating fat-related disease diagnosis and evaluation.
Analysis of Generation of Arbitrary Spectral Profiles (GASP) for Water-Fat Separation
Authors: Mendoza Michael , McKibben Nicholas , Lally Peter , Bangerter Neal
Published: November 2024

Motivation: This research explores using multiple bSSFP images to generate arbitrary spectral profiles for use in water-fat separationGoal(s): Our goal is to analyze the generation of synthetic spectral profiles and use this technique for water-fat separation. Approach: We use computational simulations to analyze the effectiveness of generating arbitrary spectral profiles for filtering different tissues and validate our technique with an in-vivo knee experiment. Results: Our experiments demonstrate that the banding artifacts from multiple bSSFP acquisitions with varying TRs and phase cycles can be used to spectrally isolate water from fat signals. Impact: Multiple bSSFP images can be linearly combined to generate arbitrary spectral profiles and isolate tissues for water-fat separation
Water-fat content based electrical properties tomography using Dixon technique: a preliminary study
Authors: Ren Yinhao , Yuan Kecheng , Xu Guofang , Ye Chunyou , Qiu Bensheng , Nan Xiang , Han Jijun
Published: November 2024

Motivation: The research is motivated by the limitations of current EPT, either over-sensitive or over-simplified algorithm, prompting the development of a more accurate WF-EPT method. Goal(s): The study aims to enhance EPT by proposing WF-EPT with Dixon technique, seeking to improve accuracy in EPs mapping for clinical applications. Approach: We fit measured EPs data to generate the WF-EPs model, and validate our approach via human imaging. Results: Experiments showed relative errors of conductivity and relative permittivity of human liver were within 10.89% and 2.55% compared to literature values. Impact: WF-EPT offers new insights for clinical EPT, potentially enhance applications in disease diagnosis and SAR estimation.
A Joint 2.5D Physics-coupled Deep learning based Polynomial Fitting Approach for MR Electrical Properties Tomography
Authors: Jung Kyu-Jin , Meerbothe Thierry , Cui Chuanjiang , Park Mina , van den Berg Cornelis , Mandija Stefano , Kim Dong-Hyun
Published: November 2024

Motivation: Conductivity reconstructions based on polynomial fitting methods are mostly 2D leading to inaccurate reconstructions as information arising from the through-plane dimension is missing. Goal(s): To include conductivity contributions from three-dimensions for deep-learning patch-based polynomial fitting reconstructions. Approach: A DL-informed polynomial fitting reconstruction method including $$$B_{1}^{+}$$$ magnitude information is presented. This method leverages neural networks to jointly predict optimal fitting coefficients enabling joint 2D-polynomial-fitting in three-orthogonal-planes, hence we call it 2.5D. Results: The proposed method demonstrates superior-performance compared to fitting-based 2D/3D fitting approaches and is computationally efficient for 3D-reconstructions. Impact: A 2.5-dimensional neural network informed fitting approach is used for MR-based conductivity reconstructions. Conductivity reconstruction accuracy as well as structural information are improved compared to physics-based and deep learning-based fitting methods.
A multi-layer binary model with adaptive metabolite selection for multi-type brain tumour classification
Authors: Zhao Dadi , Avula Shivaram , Bailey Simon , Burling Sara , Jaspan Tim , MacPherson Lesley , Mitra Dipayan , Morgan Paul , Pizer Barry , Shen Rui , Wilson Martin , Worthington Lara , Arvanitis Theodoros , Peet Andrew , Apps John
Published: November 2024

Motivation: Accurate classification of multi-type brain tumours through in vivo proton magnetic resonance spectroscopy remains a significant challenge. Conventional machine learning classifiers consider all reliably observed metabolites as features and classify all brain tumours simultaneously, but their performance is limited for rare tumour types. Goal(s): This abstract presents a novel multi-layer classification model, binary adaptive metabolite selection (BAMS), for better identifying rare tumour types. Approach: BAMS generalises the problem by considering only one specific brain tumour type and selecting significant biomarkers in each layer iteratively and dynamically. Results: In comparison to classic models, BAMS showed significantly improved diagnostic performance for rare brain tumour types. Impact: A brain tumour classification method that can only work on main types and cannot determine rare types is unlikely to be useful for clinicians. This abstract introduces BAMS that can significantly improve diagnostic performance for rare brain tumour types.
Identifying NF-2 Mutations in Meningiomas Based on Susceptibility Weighted Imaging for Patient Prognosis Using Machine Learning
Authors: Azamat Sena , Danyeli Ayça Ersen , Ozcan Alpay , Pamir M.Necmettin , Dinçer Alp , Ozduman Koray , Ozturk-Isik Esin
Published: November 2024

Motivation: Molecular markers, like neurofibromatosis type-2 (NF-2) mutations, highly impact patient outcomes in meningiomas, but they could only be assessed in excised tissue. Goal(s): To develop a non-invasive approach for preoperatively identifying NF-2 mutations using susceptibility-weighted MRI (SWI) with radiomics and deep learning. Approach: Preoperative SWI of 92 meningiomas with NF-2 status data were analyzed. Radiomics and deep learning were used to extract features of SWI, which were classified using traditional machine learning. Results: Reduced tumor signal intensity, "en plaque" growth pattern, and intratumoral calcification were markers of NF2 mutation, which was identified with an accuracy of 74%. Impact: This study employed SWI to predict NF-2 mutation through radiomics and deep learning features with 74% accuracy. Preoperative identification of NF-2 mutations might allow for personalized treatment planning resulting in better patient outcomes.
T1 and T2 of the Human Brain at 64 mT: Measurement of 20 Participants
Authors: Ogier Stephen , Jordanova Kalina , Srivastava Deepansh , Luo Tianrui , Poorman Megan , Keenan Kathryn
Published: November 2024

Motivation: To enable quantitative techniques at low-field, it is necessary to establish expected T1 and T2 values within the brain for healthy adults. Goal(s): To measure and compare T1 and T2 of the healthy human brain at 64mT. Approach: 20 healthy volunteers were imaged at 64mT. The resulting images were segmented using SynthSeg, and average values for T1 and T2 were computed over gray matter, white matter, and cerebrospinal fluid regions. Results: Mean T1 and T2 were determined. Partial volume effects may limit the accuracy of the image segmentation and lead to large variations in gray matter and cerebrospinal fluid measurements. Impact: To establish normative values for quantitative MRI at low field, 20 healthy individuals were scanned at 64 mT. T1 and T2 are reported for gray matter, white matter, and cerebrospinal fluid.
Simultaneous and Robust Estimation of Cardiac-Induced 3D Brain Velocity and Diffusion Tensor Fields in the Human Brain
Authors: Magdoom Kulam Najmudeen , Avram Alexandru , Sarlls Joelle , Basser Peter
Published: November 2024

Motivation: Cardiac-induced brain pulsation is crucial for brain function, yet its measurement is challenging due to small displacements. Goal(s): The goal of this study is to develop a robust method to simultaneously measure both the cardiac-induced brain velocity vector and the corresponding diffusion tensor fields. Approach: We acquire DWIs with long diffusion times to enhance flow sensitivity and employ an outlier rejection method to eliminate inconsistent phase signals. Velocity vector and diffusion tensor fields were estimated from phase and magnitude images, respectively. Results: The high variability in the velocities across acquisitions was significantly reduced using our approach. The brain appeared still during late diastole. Impact: This study facilitates the measurement of intrinsic brain tissue motion with heartbeat compared to DENSE approaches, providing a new method for studying brain function.
Multi-echo phase-based quantitative T2 and T2* mapping - a feasibility study
Authors: Philippe Jocelyn , Bilgic Berkin , Gagoski Borjan
Published: November 2024

Motivation: To provide strategies for accurate, and high-fidelity quantitative T2 and T2* quantitative imaging. Goal(s): To study the feasibility of simultaneous quantification of T2 and T2* using gradient-echo readouts at 3T. Approach: We employed a multi-GRE-based acquisition with optimized TR and RF phase increments to estimate T2 and T2* maps from the acquired phase and magnitude images, respectively. We demonstrated the accuracy and feasibility of our methods, both in the ISMRM/NIST phantom and in vivo. Results: Phase-based T2 mapping method can be combined with T2* mapping while increasing the accuracy of the T2 estimation. Impact: This work shows that it is feasible quantify simultaneously T2 and T2* using a single multi-echo GRE acquisition.
In-vivo Validation of a Workflow to Predict Heating Around a Deep Brain Stimulation contacts
Authors: Zulkarnain-Lemke Nur Izzati Huda , Sadeghi-Tarakameh Alireza , Koski Dee , Harel Noam , Eryaman Yigitcan
Published: November 2024

Motivation: To develop a reliable method to predict heating around deep brain stimulation implants as a safety assessment prior to scanning patients. Goal(s): To investigate the accuracy of a heating prediction workflow in-vivo. Approach: We surgically inserted a DBS electrode into swine brains and predicted the heating around DBS contacts, with and without perfusion. Our heating prediction workflow uses a new MR-based current measurement, proposed for this work, as well as quasi-static electromagnetic and thermal simulations to predict heating around the electrical contacts of DBS electrodes. Results: The predicted temperature around electrical contacts agreed with the measurements (NRMSE ≤ 0.09). Impact: Our workflow predicts heating around the electrical contacts in-vivo without complex modeling and simulations. The results demonstrate the reliability of the workflow to assess heating risk before scanning patients with DBS implants.
Evaluating Specific Absorption Rate Effects of a Flexible Receive-Only Coil With Various Blocking Configurations via Simulation
Authors: Long Emily , Taracila Victor , Robb Fraser , Roemer Peter , Winkler Simone
Published: November 2024

Motivation: The effects of flexible receive-only coils on Specific Absorption Rate (SAR) are understudied and poorly understood. Goal(s): The goal was to examine, in the pediatric case, SAR effects in the presence of a receive coil with various blocking impedance characteristics. Approach: Finite Difference Time Domain analysis was applied to evaluate SAR for pediatric human models wrapped in a flexible 16-channel coil with three blocking impedance configurations. Results: Results demonstrated that local and whole-body SAR decreased upon inclusion of a wrapped surface coil when blocking impedance was resistive or inductive and increased when capacitive. Impact: The demonstration of SAR effects in the presence of flexible receive-only coils and the indication of SAR configurability via blocking impedance control informs coil design to lower SAR and facilitate safety in high thermal-risk applications such as pediatric imaging.
A faster VOP post-processing algorithm and its impact on supervision complexity
Authors: Orzada Stephan , Fiedler Thomas , Ladd Mark
Published: November 2024

Motivation: The complexity of SAR calculation increases dramatically with the number of channels in a parallel transmit system. Goal(s): The goal of this work is to improve VOP post processing and investigate the impact of post processing on the complexity of SAR calculation with increasing number of channels. Approach: An existing post-processing algorithm was improved by introducing a new criterion for upper boundedness from the literature. The new algorithm was used to investigate the increase in the number of VOPs with the channel count when median relative overestimation was kept constant. Results: The number of VOPs increases logarithmically with the number of channels. Impact: VOP compression is important for SAR supervision and constraint RF-pulse calculation. Using an improved post-processing algorithm, we show that the increase in the number of VOPs when going to higher channel count Nch can be reduced from Nch2.3 to log(Nch).
An MRI-based nomogram predicts intracranial recurrence of brain metastases after surgery in lung cancer patients: A multicenter study
Authors: Li Baoxun , Mao Jiaji , Li Haojiang , Chen Junwei , Xiao Fang , Fang Xuewen , Guo Ruomi , Liang Manqiu , Luo Xiaowen , Wu Zhiyuan , Li Jianing , Hu Zhixuan , Wen Qin , Shen Jun
Published: November 2024

Motivation: There is currently no reliable tool to predict postoperative recurrence for patients who undergo surgery for brain metastases (BrMs). Goal(s): This study aimed to develop and externally validate a prognostic model to predict intracranial recurrence and recurrence-free survival (RFS) for lung cancer patients receiving BrM surgery. Approach: A combined prognostic model-based nomogram was developed by incorporating clinical and structural MRI predictors, radiomics and deep signatures extracted from MR images. Results: The nomogram predicted accurately for RFS and intracranial recurrence prediction, both in the training and test sets .Impact: The combined prognostic model-based nomogram can be used as a preoperative tool to predict intracranial recurrence and recurrence-free survival after surgical resection of brain metastases in lung cancer patients.
Machine Learning for Preoperative Prediction of EGFR Mutation in Lung Cancer Brain Metastasis
Authors: Ko Ching-Chung , Fan-Chiang Yang , Hsieh Hsun-Ping
Published: November 2024

Motivation: Lung cancer is the most common primary tumor showing brain metastasis (BM). Epidermal growth factor receptor (EGFR) mutations are detected in a significant proportion of lung cancer patients. Goal(s): However, a subset of patients may show discordance in EGFR mutation status between the primary lung tumor and the corresponding BMs, which may affect decision-making in treatments.Approach: We used machine learning (ML) based on pretreatment brain MRI and clinical data for prediction of EGFR mutation status in BMs of lung cancer.Results: Among various ML algorithms, the best predictive performance with accuracy of 89%, precision of 88%, and AUC of 0.97 were obtained.Impact: Machine learning based on pretreatment clinical data and brain MRI provides the potential to predict the EGFR mutation status in brain metastasis of lung cancer, and may affect decision-making in treatments.
Quantitative Physiologic MRI Parameters Combined with Innovative Machine Learning to Distinguish Glioblastoma from Solitary Brain Metastases
Authors: Hosseini Seyyed Ali , Servaes Stijn , Rosa-Neto Pedro , Mohan Suyash , Chawla Sanjeev
Published: November 2024

Motivation: There is an unmet need to develop advanced MRI based prediction models to distinguish glioblastomas (GBMs) from solitary brain metastases (BMs) with high-accuracy, as conventional MRI-techniques often yield ambiguous results. Goal(s): The objective is to discriminate GBMs from solitary BMs using physiologic MRI parameters and machine-learning based novel-methods. Approach: Employing diffusion-tensor-imaging (DTI) and dynamic-susceptibility contrast-perfusion weighted imaging (DSC-PWI), the study uses a novel machine-learning approach that integrates multiple features with hyper-tuned models to enhance pattern-recognition and prediction. Results: The innovative-method combining interacted and non-interacted features via hyper-tuned machine-learning models significantly outperformed traditional-methods, thus achieving high accuracy and reliability in differentiating GBMs from BMs. Impact: The integration of quantitative and physiologically-sensitive MRI-parameters with novel machine-learning based algorithms may be promising in distinguishing glioblastomas from solitary brain metastases. This approach may be useful in making prognostication and guiding optimal, personalized patient-treatments in the era of personalized-medicine.
Radiomics Features on Magnetic Resonance Images Can Predict C5aR1 Expression Levels and Prognosis in High-Grade Glioma.
Authors: Wu Zijun , Yang Yuan , Zha Yunfei
Published: November 2024

Motivation: High-grade glioma is a complex disease characterized by genome instability caused by the accumulation of genetic alterations. Identifying and evaluating the oncogenes involved is crucial for determining treatment strategies and evaluating prognosis. Goal(s): We sought to explore whether radiomics models based on MRI features can noninvasively predict C5aR1 expression and the prognosis of patients with high-grade glioma. Approach: This study uses machine learning approaches based on paired MRI and RNA sequencing data. Results: The radiomics models yield satisfactory performances in predicting C5aR1 expression. Our findings also reveal associations between MRI radiomics and immune-related features. Impact: As an effective and reproducible tool, our radiomics model may support clinical decision making and individualized treatment.
Automatic classification of Cine MRI images using CNN: Apical-to-Basal vs Extreme slices
Authors: Kumar Sandeep , Jafari Raufiya , Kandpal Ankit , Gupta Rakesh , Singh Anup
Published: November 2024

Motivation: Manual segmentation of cardiac MRI images is a time-consuming and laborious task prone to observer bias. Automatic segmentation approaches provide poor results in extreme slices. A slice classification step applied before automatic segmentation will lead to better results and reduced variability. Goal(s): To develop a classifier model with high classification performance on short-axis(SA) cine MRI images for slice selection. Approach: We trained and compared 2 CNN models for classifying SA cine MRI images into Apical-to-Basal vs Extreme slices. Results: Xception model had better classification accuracy (0.90) and F1- score (0.93) when compared to InceptionV3 (0.87 and 0.89, respectively). Impact: The proposed model will provide automatic, fast and accurate classification of MRI cine images, which will improve the accuracy of automatic segmentation of myocardium and its assessment.
RegGAN-CBAM based virtual LGE and its application on hypertrophic cardiomyopathy patients
Authors: Sun Longyu , Yu Mengyao , Wang Shuo , Li Qing , Sun Mengting , Hu Xumei , Liu Meng , Zhang Xinyu , Chen Weibo , Wang Chengyan
Published: November 2024

Motivation: LGE CMR, the standard clinical non-invasive characterization, is limited by its reliance on intravenous contrast agents and long waiting time. Therefore, developing a contrast agent-free technology is essential for achieving fast and cost-effective CMR scans. Goal(s): To evaluate the reproducibility and reliability of the virtual LGE based on Cine, and compare it with native LGE to assess its efficiency in diagnosing HCM in clinical context. Approach: RegGAN was employed to forecast LGE imaging and rectify the outcomes. And CBAM was utilized to quantify the influence of diverse components in LGE. Results: RegGAN-CBAM demonstrates favorable performance in both image and enhancement prediction of LGE. Impact: The performance of virtual LGE based on Cine exhibits a notable level of diagnostic efficiency and reliability. This approach serves as a non-invasive myocardial tissue characterization method with practical applicability in the clinical assessment of HCM.
Model-based deep learning reconstruction by SmartSpeed AI for head and neck contrast-enhanced 3D-T1 weighted imaging
Authors: Fujima Noriyuki , Nakagawa Junichi , Kwon Jihun , Yoneyama Masami , Kudo Kohsuke
Published: November 2024

Motivation: Fat-suppressed (Fs) contrast-enhanced (CE) three-dimensional (3D) T1-weighted imaging (T1WI) enables the clear visualization of head and neck structures; however, it requires a long scanning time to obtain high quality images. Goal(s): To demonstrate the utility of model-based deep learning (DL) reconstruction, named SmartSpeed AI, for the acquisition of Fs-CE-3D T1WI of the head and neck. Approach: Three reconstruction techniques were compared for head and neck Fs-CE-3D T1WI: 1) conventional compressed-sensing sensitivity-encoding (CS), 2) CS followed by end-to-end DL reconstruction, and 3) SmartSpeed AI. Results: SmartSpeed AI provided the superior image quality than other two reconstruction techniques. Impact: SmartSpeed AI, a model-based deep learning deep learning reconstruction technique, demonstrated improved image quality in head and neck Fs-CE-3D T1WI, even with a high reduction factor of 12, compared to conventional CS and CS followed by end-to-end deep learning reconstruction.
Combing CT coronary artery calcification score and deep learning MR late gadolinium enhancement to detect unrecognized myocardial infarction
Authors: Lu Xuefang , Yan Yuchen , Liu Weiyin , Zha Yunfei
Published: November 2024

Motivation: Coronary artery calcification score (CACS) is currently a common and widely-accepted indication of UMI, but it itself fails to accurately reflect myocardial ischemia in patients with unrecognized myocardial infarction(UMI). Goal(s): To establish a UMI-screening workflow for a cohort who receive a physical examination. Approach: To explore the detection rate of myocardial infarction (MI) using CACS only, Parea only, CACS in combination with Parea using different thresholds. Results: The AI-CACS combined with Parea had higher diagnostic performance on differentiating UMI from non-UMI groups than AI-CACS or Parea alone, especially AI-CACS combined with Parea-DL-5SD with AUC of 0.914. Impact: Patients with UMI usually do not have typical symptoms of cardiogenic chest pain. CACS-Parea-DL-5SD can detect unrecognized myocardial infarction in the outpaitnets, and increased the diagnostic confidence of UMI, providing an important reference for UMI risk stratification and follow-up recommendations.
Towards online 3D MR-STAT reconstructions
Authors: van der Heide Oscar , Heldens Stijn , Sclocco Alessio , Liu Hongyan , Fuderer Miha , van den Berg Cornelis , Sbrizzi Alessandro
Published: November 2024

Motivation: MR-STAT is a transient-state qMRI technique that can deliver T1, T2 and proton density maps from a single short scan, even when Cartesian trajectories are used. The non-linear model-based MR-STAT reconstruction procedure is challenging and current state-of-the-art reconstructions take multiple hours for a 3D dataset. Goal(s): In this work we aim to accelerate the MR-STAT reconstructions to make the technique more valuable for clinical practice. Approach: A highly-optimized CUDA EPG simulator was developed and an optimization for signal computations for Cartesian trajectories has been discovered. Results: High-resolution 3D knee and brain MR-STAT datasets can now be reconstructed in 12.5 and 25 minutes, respectively. Impact: The tools are part of the COMPAS Toolkit that is available online. These tools are relevant to other researchers or educators that require high-performance Bloch/MRI simulations.
Rapid $$$T_{2}^{*}$$$ and Susceptibility Mapping using Poisson Wave Encoding and Model-Based Reconstruction
Authors: Wang Xiaoqing , Cho Jaejin , Jun Yohan , Bilgic Berkin , Marques José
Published: November 2024

Motivation: Harmonization of quantitative $$$T_{2}^{*}$$$ and susceptibility mapping is of critical interest in research and clinical studies, yet there are no available open-source, harmonized acquisition/reconstruction strategies. Goal(s): To develop open-source sequences and reconstructions for quantitative $$$T_{2}^{*}$$$ and susceptibility mapping with high acceleration. Approach: A 3D multi-echo GRE sequence with wave encoding was implemented on Pulseq. Model-based reconstruction was employed to estimate quantitative maps directly from undersampled k-space. Results: Wave encoding improves quantitative mapping at R=12. While SENSE is more flexible with the choice of sampling patterns, model-based reconstruction performs best with wave-poisson sampling. The latter further improves $$$B_{0}$$$ mapping with less phase warps. Impact: Open-source acquisition with advanced wave encoding and reconstruction have been implemented, which has the potential to facilitate the harmonization of highly-accelerated quantitative $$$T_{2}^{*}$$$, $$$B_{0}$$$ and susceptibility mapping.
Whole-brain Morphological Network Alterations in Patients with H3K27M-altered Diffuse Midline Glioma
Authors: Chen Di , Zhang Simin , Gong Qiyong , Yue Qiang
Published: November 2024

Motivation: H3K27M-altered diffuse midline glioma (DMG) is a highly infiltrative and deadly form of brain cancer with a dismal prognosis. The mechanisms underlying its aggressiveness remain elusive. Goal(s): To elucidate alterations in whole-brain morphological networks in diffuse midline glioma patients and assess whether such alterations are associated with the H3K27M mutation. Approach: A whole-brain structural covariance network (SCN) was constructed based on cortical thickness. Results: The topological characteristics of structural covariance network (SCN) were severely disrupted in DMG. Additionally, H3K27M mutations may induce more aggressive whole-brain network damage. Impact: The research offers novel insights into the mechanisms of progression in H3K27M-altered DMG, which may help in developing new treatment protocols.
Arterial Spin Labeling measured baseline perfusion stratifies survival in glioblastoma
Authors: Zhou Limin , Pinho Marco , Wang Yiming , Mostardeiro Thomaz , Youssef Michael , Maldjian Joseph , Udayakumar Durga , Madhuranthakam Ananth
Published: November 2024

Motivation: Quantitative measurements at baseline, prior to chemoradiation, can inform prognosis and optimal treatment strategies for patients with glioblastoma (GBM), however, such measurements using ASL are currently lacking. Goal(s): To investigate ASL measured quantitative perfusion prior to chemoradiation for survival prediction in GBM patients. Approach: Twenty-three newly diagnosed GBM patients were enrolled in this prospective IRB-approved study. Baseline scans with ASL and survival information were obtained. Results: ASL measured baseline perfusion aids in survival prediction and stratification between groups with long (mean: 717 days) and short (mean: 361 days) survival in GBM. Impact: Early and accurate survival stratification by ASL measured perfusion prior to chemoradiation treatment provides valuable opportunities for therapeutic interventions, including personalized and biologically driven radiation treatment planning and strategic discontinuation of traditional treatments for enrollment in promising clinical trials.
Decoding the Heterogeneity of Glioma IDH Genotyping by DCE-MRI of Spatial Habitat Analysis: A Feasibility Study
Authors: Song Dandan , Jiang Yueluan , Song Yang , Chang Miao , Fan Guoguang
Published: November 2024

Motivation: To evaluate dynamic contrast enhanced (DCE) MRI for assessing heterogeneity in glioma IDH genotyping and derive a combined map. Goal(s): To predict specific tumor areas and to guide biopsy and precision molecular typing therapy. Approach: Whole tumor volumes were delineated on DCE images, and voxel-wise clustering of each quantitative imaging map identified five combined physiologic MRI habitats. Results: DCE-Ktrans within necrosis subregion (mask1) emerged as the best parameter to identify IDH status (AUC=0.824, p＜0.001), while DCE-Ktrans within mild reinforcement region (mask 2) was positively correlated with Ki-67(r=0.473, p=0.001) and DCE-Ve of middle reinforcement (mask 4) was positively correlated with microvessel density (MVD)(r=-0.549, p＜0.001). Impact: Based on the habitat analysis of MR perfusion imaging, glioma was divided into different sub-regions, which reflected biological information such as necrosis, hypoxia, and angiogenesis, predicted molecular classification and guided clinical biopsy or surgical sampling accurately to guide precision therapy.
Unravelling microstructural age-related changes in the acoustic radiations through advanced diffusion MRI analysis
Authors: Andersson Mariam , Fuglsang Søren , Hjortkjær Jens , Dau Torsten , Siebner Harwtig , Dyrby Tim
Published: November 2024

Motivation: Age-related hearing loss is widespread, but the impact of aging on the central auditory pathway's structure and function is poorly understood. Goal(s): This study aims to characterise the microstructural signatures of aging in the acoustic radiations. Approach: Forty-five participants between 18-76 years underwent diffusion weighted MRI. Tractography was used to delineate each subject's acoustic radiations, and maps of diffusion MRI metrics and biophysical model parameters were computed. Results: Mean isotropic kurtosis and axonal volume were found to increase with age in the bilateral acoustic radiations. The increase in apparent axonal volume fraction contradicts previous studies and expectations of decreased fibre integrity with age. Impact: The aging-related microstructural changes to the central auditory pathway shown here may have functional consequences in terms of hearing ability and hearing rehabilitation strategies among the elderly. Future studies could incorporate electrophysiological measurements to assess this microstructure-function relationship.
Changes of auditory γ-aminobutyric acid and cerebral blood flow in presbycusis and their relationship with cognitive impairment
Authors: Wang Shuya , Wang Yao , Edden Richard A.E. , Chen Weibo , Ren Fuxin , Gao Fei
Published: November 2024

Motivation: Studies have shown that presbycusis is associated with cognitive impairment. Goal(s): To study the pathophysiological mechanism of presbycusis and its potential association with cognitive impairment. Approach: To study the levels of auditory γ-aminobutyric acid (GABA) and glutamate (Glu), cerebral blood flow (CBF) and their relationship with auditory and cognitive function in patients with presbycusis and healthy controls. Results: The results showed that the decrease of auditory GABA, Glu levels and CBF in presbycusis was related to the decrease of speech perception or cognitive impairment, and mediated the association between hearing loss and impaired information processing speed in presbycusis. Impact: The decrease of GABA and CBF mediates the correlation between hearing loss and impaired information processing speed in presbycusis, which helps us to reveal the relationship between hearing loss and cognitive impairment in presbycusis.
Characterizing the age-related changes of hippocampal arterial transit time and perfusion across adult lifespan
Authors: Li Chenyang , Sun Zhe , Rusinek Henry , Zhang Jiangyang , Wisniewski Thomas , Ge Yulin
Published: November 2024

Motivation: Age-related changes of arterial transit time (ATT) and perfusion (CBF) measurement in hippocampus subfields is still under-investigated. Goal(s): To quantitatively characterize the age-related hippocampal perfusion changes in a subfield-specific manner to better understand its involvement in neurodegenerative changes and dementia. Approach: T1-weighted images and mbPCASL data from the Human Connectome Project-Aging (HCA) was analyzed to obtain subfield-specific measurements of ATT and CBF in hippocampus. Results: The lowest perfusion measurement was observed in CA1 region across all age groups. Age trajectories of CBF and ATT were demonstrated in different subfields with female showing a more significant decrease of hippocampal perfusion. Impact: Using Human Connectome Project–Aging (HCA) dataset, this study revealed age-related subfield-specific changes in hippocampal ATT and CBF across the adult normative lifespan, including subiculum, CA1-CA4 and the dentate gyrus.
Gas-free cerebrovascular reactivity predicts cognition in older individuals
Authors: Donaldson Lori , Hu Beini , Mayeli Mahsa , Liang Huajun , Jin Yuecen , Hazel Kaisha , Pottanat George , Jones Ebony , Chang Linda , Lu Hanzhang , Liu Peiying
Published: November 2024

Motivation: Small-vessel-disease (SVD), a contributing risk factor in vascular cognitive impairment and dementia (VCID), needs sensitive biomarkers to assess the brain. Goal(s): Our goal is to evaluate whether gas-free cerebrovascular reactivity (CVR) can predict cognitive function in older individuals. Approach: We performed gas-free CVR mapping with intermittent breath modulation in two studies of elderly individuals and evaluated the relationship between gas-free CVR and global cognition. Results: Higher whole-brain gas-free CVR was correlated with better MoCA scores. Impact: Gas-free cerebrovascular reactivity (CVR) using intermittent breath modulation can be used as a practical tool to evaluate cerebrovascular function and probe vascular pathology in small-vessel-disease (SVD) and vascular cognitive impairment and dementia (VCID).
Neurovascular coupling dysfunction in type 2 diabetes patients
Authors: Du Wei , Wang Weiwei , Miao Yanwei
Published: November 2024

Motivation: The neurovascular coupling is the basic structure of neurovascular unit. Various pathological injuries lead to abnormalities in neuronal activity and blood flow resulting in impaired function. There is still not much research on the coupling pattern. Goal(s): We hypothesize that there exists an abnormal NVC pattern for T2DM. Approach: We recruited 40 patients clinically diagnosed with T2DM, healthy controls and underwent BOLD fMRI scanning and image analysis. Results: Results found signiﬁcant decreased CBF-ALFF ratio in supramarginal and frontal gyrus, and increased values in middle frontal gyrus of T2DM. The ALFF and CBF was found to be reduced or elevated in several brain regions. Impact: In addition to the frontal and parietal gyrus, the cerebellum is also involved in the mechanisms of brain function impairment in T2DM. The different coupling results indicate the existence of mutually constraining balancing mechanism between neural activity and blood perfusion.
Quantitative Imaging Report Framework for the Cleveland Alzheimer’s Disease Research Center (CADRC) Neuroimaging Core (NIC)
Authors: Lin Jian , Sakaie Ken , Shin Wanyong , Koenig Katherine , Ma Dan , Oh Sehong , Durgerian Sally , Nemani Ajay , Pillai Jagan , Appleby Brian , Lerner Alan , Leverenz James , Lowe Mark
Published: November 2024

Motivation: We have developed an approach to provide a quantitative summary of regional brain measures from an advanced imaging protocol in AD and ADRD subjects as part of the CADRC’s Neuroimaging Core. Goal(s): To establish a panel of advanced imaging markers that will supplement data acquired by the ADRC network. Approach: Structural, functional and quantitative imaging has been implemented, along with procedures for quality assurance and summary outcome measures. Results: 86 subjects have been scanned to date, including those with normal cognition, mild cognitive impairment (MCI) and dementia. Subtypes of impairment include typical and atypical AD, dementia with lewy bodies (DLB). Impact: Although AD is the most common cause of dementia, related and overlapping dementias are common. An inclusive approach to both recruitment and imaging have been implemented to explore new tools for informing patient care in dementia.
Calculation of Eddy Power Losses Within the Cryostat Generated by Array or Conventional Gradient Assembly for Arbitrary Pulse Sequences
Authors: Takrimi Manouchehr , Atalar Ergin
Published: November 2024

Motivation: Existing eddy current calculation methods aren't suitable for fast eddy power loss calculations. Goal(s): We propose a quick calculation to estimate the time-average eddy power loss within the cryostat, generated by gradient assembly (array or conventional) and arbitrary pulse sequences. Approach: The frequency response of the fields generated by the gradient assembly elements (array or conventional) on the cryostat's surface is combined with the harmonic components of the driving pulse sequences. Results: A typical pulse sequence feeds a whole-body conventional x-gradient coil, and the proposed method estimates the time-average dissipated power on the fly, comparable with simulation results reported by commercial software. Impact: Our method optimizes gradient assembly (array/conventional) tuning, empowering MRI engineers with fast and precise dissipated power estimates. This approach sparks novel research paths, enhancing MRI system efficiency and enabling tailored pulse sequences for gradient coils, advancing the field significantly.
In-vivo Imaging of the adult Zebrafish in an unmodified pre-clinical MRI System at 11.7T
Authors: Schüle Julian , Gahr Bernd , Just Steffen , Rasche Volker , Speidel Tobias
Published: November 2024

Motivation: The zebrafish is an important model organism for the study of vertebrate biology with its genomes showing significant parallels to human genomes. Goal(s): To show the feasibility of in-vivo imaging at a pre-clinical 11.7T MRI without the need for dedicated hardware components. Approach: A budget-friendly zebrafish holder was created using a syringe barrel and needle adapters, with a rotating cylinder to maintain water flow. It was positioned on a Bruker Mouse Brain Surface Coil. Results: The use of standard imaging equipment in combination with a low-cost and easy to built animal holder is sufficient to facility high-resolution imaging of the adult zebrafish. Impact: This research enables cost-effective, high-resolution MRI for zebrafish, enhancing its potential as a pre-clinical model for vital research in vertebrate biology, ultimately advancing our understanding of human genomics.
An in-cell NMR strategy optimized for proteins with poor solubility and low thermal stability
Authors: Tanaka Yui , Yoshinaga Sosuke , Udatsu Haruka , Sato Takafumi , Takeda Mitsuhiro , Terashima Yuya , Toda Etsuko , Matsushima Kouji , Terasawa Hiroaki
Published: November 2024

Motivation: The in-cell NMR method allows direct observations of proteins in cultured human cells, to evaluate protein conformations and interactions in an intracellular environment. Currently, target proteins must be highly soluble and thermally stable. Goal(s): A technical in-cell NMR strategy is required for proteins with poor solubility and low thermal stability. Approach: Protein concentration-dependent NMR analyses revealed the self-association sites of proteins. Site-directed mutants gained higher solubility. Cell treatment conditions at lower temperature were established. Results: We successfully constructed an in-cell NMR protocol that improves protein solubility and cell treatment conditions at 25°C, the lowest temperature established so far. Impact: By performing experiments according to our strategy, in-cell NMR can be applicable to more types of proteins with poor solubility and low thermal stability. Furthermore, we developed an in-cell system for evaluating therapeutic candidate compounds against target proteins.
Value of orthogonal axial MR images in T-staging for gastric cancer: a retrospective comparison
Authors: Liu Quan-meng , Chen Yan , Fan Wen-jie , Wu Xue-han , Zhang Zhi-wen , Lu Bao-lan , Ma Yu-ru , Liu Yi-yan , Wu Yun-zhu , Yu Shen-ping , Wen Zi-qiang
Published: November 2024

Motivation: Orthogonal axial images (OAI), which may improve staging performance, had been ignored in previous studies about gastric cancer (GC) T-staging using MRI. Goal(s): To assess the value of MR OAI in GC preoperative T-staging. Approach: Diagnostic performance metrics, over- and understaging fractions, overall accuracy (compared by McNemar test) for MRI lacking or incorporating OAI and MDCT were quantified referring to pathological T-stage. Results: The overall accuracy was significantly higher for MRI with OAI than those without OAI, while marginally better than MDCT. Both MRI with OAI and MDCT exhibited superior staging performance than MRI without OAI. Impact: By incorporating orthogonal axis images, MRI exhibited diagnostic performance marginally better than MDCT in preoperative T-staging of gastric cancer, thereby offering a superior, non-invasive and radiation-free protocol to determine prognosis and guide appropriate treatment strategies.