bluuebunny/crossref_metadata_2025 huggingface dataset:
Distinguishing early liver fibrosis: integrating clinical and elastography features and radiomics signatures from Gd-EOB-DTPA-enhanced MRI
Authors: qiu Caixin , cao Yan , xie Shuangshuang , shen Wen
Published: November 2024

Motivation: Despite the effectiveness of elastography and serology tests in detecting liver fibrosis, diagnosing early-stage fibrosis remains challenging. Goal(s): Develop and validate a reliable radiomics model using Gd-EOB-DTPA-enhanced MRI for early liver fibrosis diagnosis. Approach: Create a radiomics model based on Gd-EOB-DTPA-enhanced MRI and establish a fused nomogram combining clinical characteristics and LSM. Compare the diagnostic performance of the fused model with single models for early-stage liver fibrosis. Results: Gd-EOB-DTPA-enhanced MRI radiomics model effectively diagnoses early liver fibrosis. The fusion model enhances diagnostic efficiency. Impact: To develop and validate a fusion model based on Gd-EOB-DTPA-enhanced MRI to identify early-stage liver fibrosis.
Optimization of cervical cord synthetic T1-weighted MRI for enhancing clinical application
Authors: Schading-Sassenhausen Simon , Seif Maryam , Weiskopf Nikolaus , Freund Patrick
Published: November 2024

Motivation: MRI acquisition time is a critical factor for patient comfort and motion artifacts. To reduce total acquisition time, synthetic MRI is a flexible solution. Goal(s): To optimize synthetic T1-w (synT1-w) MRI for increasing the accuracy (i.e. difference between synT1-w and MPRAGE) of spinal cord cross-sectional area measurements. Approach: We optimized and validated synT1-w for tracking neurodegeneration in the cervical cord after spinal cord injury and assessed the required sample size for detecting hypothetical treatment effects. Results: Accuracy of synT1-w improved considerably with a minor remaining bias of -0.5% compared to MPRAGE. 13.5% less participants are required when using synT1-w instead of MPRAGE. Impact: Synthetic MRI can help to optimize imaging protocols in clinical trials by reducing acquisition time and the number of required participants. By improving the accuracy of synthetic T1-weighted images, better comparability with different studies using acquired MRI can be achieved.
The value of synthetic MRI in differentiating metastatic and non-metastatic lymph nodes in squamous cell carcinoma of head and neck
Authors: Wei Haoran , Yang Fan , Yu Xiaoduo , Xie Lizhi , Lin Meng
Published: November 2024

Motivation: The presence of metastatic lymph nodes in HNSCC indicates a worse prognosis, biopsy is invasive and has a high incidence of false-negative results. The diagnosis value of synthetic MRI needs to be studied further. Goal(s): To explore the value of the features derived from synthetic MRI in distinguishing between benign and malignant lymph nodes. Approach: This study included lymph nodes of HNSCC which were confirmed by pathology, and utilized multiple methods to select meaningful features. Results: Features derived from synthetic MRI have satisfactory discrimination efficiency. Impact: Synthetic MRI may be able to be used as a method to help assemnet the lymph nodes in clinical practice.
Impact of Different Techniques for Slice Annotation Reduction on U-Net-Based Thigh Muscle MR Images Segmentation
Authors: Casali Nicola , Scalco Elisa , Taccogna Maria Giovanna , Porcelli Simone , Ciuni Andrea , Mastropietro Alfonso , Rizzo Giovanna
Published: November 2024

Motivation: Deep Learning (DL) for thigh muscle segmentation in MR images holds promise for musculoskeletal architectural assessment, however the process of generating annotated data in supervised approaches is time-consuming. Goal(s): This study evaluates the impact of scarce annotated data on DL segmentation performance, investigating optimal annotation strategies of thigh muscle MR images. Approach: Employing thigh MRIs from healthy subjects, the research compares the segmentation performance using various selection strategies and annotated data amount for training a U-Net. Results: Results reveal high segmentation accuracy (Dice > 0.81) even with minimal annotations (3% of total labels), when selecting the most informative slices for annotation. Impact: This research highlights the potential of significantly reducing the laborious task of annotating MR images for thigh muscle segmentation, while maintaining robust performance using DL. This efficiency enhancement could expedite the application of DL in muscle health assessment.
Making MR more accessible through precision quantitative MRI at 0.55T with optimized 3D MRF
Authors: Cao Xiaozhi , Liao Congyu , Zhu Zheren , Li Zhitao , Bhattacharjee Rupsa , Nishmura Mark , Wang Zhixing , Wang Nan , Majumdar Sharmila , Villanueva-Meyer Javier , Yang Yang , Setsompop Kawin
Published: November 2024

Motivation: To enable high-resolution quantitaive imaging in economy-friendly 0.55T scanners. Goal(s): MRF with 1.2-mm isotropic resolution implemented on a FreeMax 0.55T scanner. Approach: MRF, subspace reconstructio, locally-low rank constraints, CRLB-optimized FA pattern, attention-based deep learning network for denoisig, trajectory correction, motion correction. Results: The proposed method was validated on phantom, in-vivo human brain and knee. Impact: Our research highlights the potential of affordable MRI scanners to deliver high-quality imaging.
Differentiation of Brain Metastases from Different Pathological Types of Lung Cancers by Using Radiomic Features of the Edema and Tumor Region
Authors: Zhu Hanting , Xiong Rui fang , Li Chengyi , Hu Pengxin , Zou Yu , Tang Xiaoping
Published: November 2024

Motivation: Prediction of brain metastases from different pathological types of lung cancers. Goal(s): To develop a radiomic model based on the peritumoral edema and tumor region for tumor type prediction of brain metastases from different pathological types of lung cancers. Approach: Collect lung cancer patients, establish radiomics models, and test the model's differentiation of lesions. Results: The radiomic model could effectively differentiate two pathological types of brain metastasis from lung cancer. Impact: The radiomic model based on the edema and tumor region could effectively differentiate two pathological types of brain metastasis from lung cancer. It is expected to provide an imaging basis for clinicians to evaluate prognosis and formulate personalized treatment plans.
Epilepsy and NANO scale are associated with pre-treatment glioblastoma lesion size and distinct brain regions
Authors: Yun Yeong Chul , Wolf Sabine , Garhöfer Freya , Holz Katharina , Vollmuth Philipp , Bendszus Martin , Schlemmer Heinz-Peter , Heiland Sabine , Wick Wolfgang , Venkataramani Varun , Kurz Felix
Published: November 2024

Motivation: Identifying brain regions where glioblastoma is associated with epilepsy or neurological deficits could help recognize patients with a higher risk of developing neurological symptoms based on MRI. Goal(s): We aim to correlate clinical and radiological findings to improve diagnostic evaluation of pre-treatment glioblastoma patients. Approach: MRIs from 557 patients with de-novo glioblastoma were analyzed retrospectively. We used NANO scale to report neurological deficits and analyzed lesion-frequency-maps for identifying deficits-associated regions. Results: There was a significant correlation between the NANO scale and lesion volume. For each investigated domain with the NANO scale, radiologically correlated brain regions could be identified. Impact: We showed that MRI examinations of pre-treatment glioblastoma patients can provide clinicians and patients valuable information regarding risk of developing certain neurological deficits and symptoms. Furthermore, NANO scale and epilepsy-status can provide information regarding the characteristics of the tumor lesion.
Radiomics nomogram based on multiparametric MRI features for preoperative prediction of MGMT promoter methylation status in glioblastomas
Authors: Lu Jun , Li Hailiang , Yang Zhenghan
Published: November 2024

Motivation: Noninvasive measurement of the MGMT methylation status has great clinical significance for making a tailored treatment plan and prognosis assessment. Goal(s): This study aimed to establish and validate a radiomics nomogram with robust radiomics features from ADC and ISO-CE-T1-weighted images. Approach: The radiomics features were selected using LASSO regression. A radiomics nomogram combined radiomics signature and clinical factors were established with multivariate logistic regression analysis. Results: The radiomics nomogram is a promising method. The Hosmer-Lemeshow test concluded that the radiomics nomogram showed goodness of fit. The decision curve showed that the addition of clinical characteristics to the nomogram showed incremental predictive value. Impact: The multiparametric MRI-based radiomics nomogram was a promising method to preoperatively predict the MGMT mpromoter ethylation status noninvasively. Besides, the nomogram transformed the prediction signature into a visual and readable graph, making it easier to understand.
Prediction of MGMT promotor methylation status in glioblastoma by contrast-enhanced T1-weighted intensity image
Authors: Sanada Takahiro , Kinoshita Manabu , Sasaki Takahiro , Yamamoto Shota , Fujikawa Seiya , Fukuyama Shusei , Hayashi Nobuhide , Fukai Junya , Okita Yoshiko , Nonaka Masahiro , Uda Takehiro , Arita Hideyuki , Mori Kanji , Ishibashi Kenichi , Takano Koji , Nishida Namiko , Shofuda Tomoko , Yoshioka Ema , Kanematsu Daisuke , Tanino Mishie , Kodama Yoshinori , Mano Masayuki , Kanemura Yonehiro
Published: November 2024

Motivation: Non-invasive prediction of GBM’s pMGMT methylation status is still challenging despite recent advancements of image analysis. Goal(s): This study explored a clinically feasible imaging biomarker that represents GBM’s pMGMT methylation status with external validation. Approach: Two qualitative imaging features, namely the “Thickened structure” and the “Methylated contrast phenotype,” were identified as valuable to this means. Results: GBMs presenting both imaging features exhibited a significantly high odds ratio, favoring pMGMT methylation in the exploratory and validation cohorts with a sensitivity and specificity of approximately 0.3-0.4 and 0.8. The easy clinical application of the proposed imaging features is expected to facilitate better preoperative GBM characterization. Impact: GBMs presenting both imaging features, namely the “Thickened structure” and the “Methylated contrast phenotype,” exhibited a significantly high odds ratio, favoring pMGMT methylation in the exploratory and validation cohorts with a sensitivity and specificity of approximately 0.3-0.4 and 0.8.
Brain age in healthy individuals and across multiple neurological disorders
Authors: Chai Li , Sun Jun , Zhuo Zhizheng , Zhang Xianchang , Liu Yaou
Published: November 2024

Motivation: Understanding brain age of healthy people and patients with neurological diseases is crucial for clinical application. Goal(s): To characterize people with advanced brain age and explore brain aging patterns across neurological disorders. Approach: Through a predicted brain age model using deep learning, we investigated the correlations between advanced brain aging and age-related deterioration in healthy individuals, and explored the correlation with clinical variables across neurological disorders. Results: Healthy individuals with advanced brain aging have higher white matter hyperintensity burdens and lower brain region volumes. Brain age increases in patients with neurological disorders and has more cognitive decline and physical disability. Impact: The brain age model using deep learning enables identifying individuals at risk for advanced brain aging in the normal-aging population and shows advanced brain aging across neurological diseases, which can be a biomarker for cognitive impairment and/or physical disability.
Normative age-trajectories of higher-order diffusion properties of major brain metabolites in cerebral and cerebellar gray matter using dMRS
Authors: Şimşek Kadir , Gallea Cécile Gallea , Genovese Guglielmo , Lehéricy Stephane , Branzoli Francesca , Palombo Marco
Published: November 2024

Motivation: Healthy brain aging involves intricate changes in both brain structure and function, including alterations in cellular composition and microstructure across various regions. We focus on microstructural changes by studying normative age-trajectories in cerebral and cerebellar gray matter in human brain. Goal(s): Investigating microstructural changes in cerebral and cerebellar gray matter in human brain and provide benchmarks for the normative age-trajectories of higher-order metabolite diffusion properties. Approach: Using DW-MRS and diffusion modelling for characterizing microstructural changes Results: Our findings provide benchmarks for identifying anomalies in the diffusion properties of major brain metabolites, stemming from pathological mechanisms altering both the brain microstructure and cellular compositionImpact: This work investigates microstructural changes with aging in gray matter cerebral and cerebellar cortex in human brain DW-MRS. Moreover, it offers the first normative age-trajectories of metabolite diffusion characteristics, setting benchmark for detection and characterization of microstructural anomalies.
Brain age pre-training for prediction of Alzheimer’s disease diagnosis and mild cognitive impairment progression
Authors: Nguyen Kim-Ngan , Tan Trevor Wei Kiat , Zhang Chen , Kong Ru , Cheng Susan , Ji Fang , Chong Joanna Su Xian , Chong Eddie Jun Yi , Venketasubramanian Narayanaswamy , Chen Christopher , Zhou Juan Helen , Thomas Yeo B. T.
Published: November 2024

Motivation: Literature suggests large multisite brain age pre-trained models (indirect models) hold significant promise for downstream prediction on small clinical samples via transfer learning. Goal(s): Our goal was to determine if such indirect models indeed outperform models trained-from-scratch (direct models), across varying training and validation set sizes, on two clinical prediction tasks: Alzheimer’s disease (AD) diagnosis, and mild cognitive impairment (MCI) progression. Approach: State-of-the-art brain age model pre-trained on n=53,542 diverse dataset was used as initialization for indirect models. Results: For AD Diagnosis, Direct model significantly outperformed feature extracted indirect model starting from 400 training and validation samples or more. Impact: The 400-training-and-validation-samples threshold encourages clinical institutions with limited computing resources and small sample sizes (n<400) to feature extract the brain age pre-trained model instead of training from scratch, potentially lowering healthcare costs and speeding up Alzheimer’s disease diagnosis and prognosis.
In vivo microstructural mapping of the hippocampus in older adults with familial risk for Alzheimer’s disease
Authors: Wearn Alfie , Tremblay Stéfanie , Leppert Ilana , Baracchini Giulia , Hughes Colleen , Turner Gary , Gauthier Claudine , Tardif Christine , Spreng R. Nathan
Published: November 2024

Motivation: Understanding prodromal Alzheimer’s disease is essential for treatment development. Hippocampal volume loss indicates significant atrophy and may occur too late to slow disease progression. Goal(s): We aimed to precisely map spatial variation in hippocampal microstructure in vivo using quantitative MRI in prodromal AD. Approach: We use multiparametric quantitative MRI to comprehensively map hippocampal microstructure in healthy older adults with first-degree family history of Alzheimer’s disease and correlate maps with demographic risk factors for Alzheimer's disease. Results: We identified two key contributors of microstructural variation (myelin content and free water). We revealed localized age-related demyelination and sex differences in hippocampal proton density. Impact: This research provides crucial insights into age-related hippocampal microstructural changes and their implications for Alzheimer's disease. It has the potential to advance early detection and intervention strategies, ultimately improving patient outcomes in Alzheimer's disease management.
Feasibility of local diffusion encoding targeting cortical surface
Authors: Ma Yixin , Nummenmaa Aapo , de Lara Lucia Navarro , Daneshzand Mohammad , Lee Hong-Hsi , Huang Susie , Stockmann Jason
Published: November 2024

Motivation: Mapping of gray matter microstructure by diffusion MRI is limited by gradient strength, which sets the resolution limit toward small cells and axons. Goal(s): To use a multi-channel 3-axis TMS coil array as a miniature gradient system and generate strong local gradients. Approach: We ran simulations and ex-vivo experiments using the TMS coil as a strong local gradient for diffusion encoding. Results: The measured gradient strength matched simulated values. The TMS-gradient system was used to estimate mean diffusivity in ex-vivo tissue. Simulations show it has the potential to generate even stronger gradient fields with higher current and more coil elements. Impact: Innovations in TMS-MRI gradient design could significantly boost the achievable gradient strength for diffusion MRI in the living human brain, advancing neuroscientific research within a cost-effective design.
Metabolic Imaging Biomarkers of Response to Signaling Inhibition Therapy in Melanoma
Authors: Gupta Pradeep Kumar , Orlovskiy Stepan , Arias-Mendoza Fernando , Nelson David S. , Pickup Stephen , Glickson Jerry D. , Nath Kavindra
Published: November 2024

Motivation: We use 1H/31P MRS to monitor the metabolic effects of dabrafenib therapy in four human melanoma models, an FDA-approved treatment for late-stage melanoma. Goal(s): Differences in relative levels of metabolites and bioenergetics between four melanoma models may produce differential therapeutic responses to BRAF inhibitors. Approach: Intratumor variations of lactate, alanine, and bioenergetics (β-NTP/Pi) measured by in-vivo and in-vitro 1H/31P MRS have the potential to become early and sensitive biomarkers of dabrafenib inhibition therapy in melanoma. Results: Changes in lactate, alanine, and bioenergetics response to targeted dabrafenib inhibitor therapy occur rapidly and are connected to the following tumor response. Impact: Dabrafenib blocks cell division by inhibiting the hyperactive BRAF protein. Differences in the predominance of metabolites (Lactate and Alanine) and bioenergetics may explain dabrafenib therapeutic responses in DB-1/WM983B (Sensitive mutant type), WM983BR (Resistant mutant type), and WM3918 (Wild type).
Ultra-high b-value DWI in Rectal Cancer: Image Quality Assessment and Prediction of Lymph Node Metastasis Based on Radiomic Features
Authors: Zhang Guangwen , Hao Yongfei , Zhang Jinsong
Published: November 2024

Motivation: The potential benefits of ultra-high b-value DWI have not yet been elucidated for clinical values of rectal cancer. Goal(s): To evaluate image quality and lymph node metastasis in rectal cancer on multi-b value diffusion-weighted imaging (DWIb1000, DWIb2000 and DWIb3000). Approach: The image quality of three sets of DWI was measured by two radiologists independently. The radiomics model was trained on 70%, and tested on the remainderResults: The DWIb2000 exhibited excellent lesion conspicuity and was able to determine the outcome of of lymph node metastasis rectal with a predictive value of 0.728Impact: The DWIb2000 had great potential in improving detection of rectal cancer and was helpful to stratify the risk of lymph node metastasis of rectal cancer.
Quantitative MRI to evaluate the efficacy of immunotargeted therapy in unresectable hepatocellular carcinoma
Authors: Wei Yu-chen , Wei Yan-sha , Deng Kan , Liao Jin-yuan
Published: November 2024

Motivation: Quantitative MRI maybe helpful for evaluating the immunotargeted therapy efficacy in unresectable HCC. Goal(s): To investigate the feasibility of quantitative MRI parameters for evaluating the immunotargeted therapy efficacy in unresectable HCCApproach: Statistics analysis was used to study the alterations in patients with unresectable HCC at baseline and 2 months, 4 monthsafter immunotherapyResults: The ADC value of HCC increased,the longest diameter of HCC initially increased and then decreased. The ADC value and ΔT1% of HCC target lesions in partial response group increased significantly. In progressive disease group, the ΔT1% of HCC target lesions decreased significantly, and ADC values showed a downward trend. Impact: This is the first report to evaluate the immunotargeted therapy in unresectable HCC using quantitative MRI techniques.
Measurement of liver function with dynamic gadoxetate-enhanced MRI: a validation study in healthy volunteers
Authors: Min Thazin , Tibiletti Marta , Hockings Paul , Galetin Aleksandra , Gunwhy Ebony , Kenna Gerry , Melillo Nicola , Parker Geoff , Schuetz Gunnar , Scotcher Daniel , Waterton John , Rowe Ian , Sourbron Steven
Published: November 2024

Motivation: Interaction of drugs via inhibition of liver function affects their toxicity and efficacy, but this is currently difficult to assess clinically. Goal(s): To determine if a DCE-MRI measurement of liver function is sufficiently sensitive to detect drug-induced inhibition of liver function in humans. Approach: 10 healthy volunteers underwent a DCE-MRI measurement of their baseline liver function. The measurement was repeated on a second day after administration of rifampicin, a powerful inhibitor of liver function. Results: Rifampicin reduced the hepatocellular uptake rate by 93%, and the effect was consistent between volunteers. The biliary excretion rate reduced by 48% and the effect was more variable. Impact: Early clinical assessment of drug-drug interactions can significantly reduce the risk of expensive late-stage failures in drug development, potentially increasing the rate at which new drugs can enter the market, and reducing the risk to trial subjects and patients.
Longitudinal T2 MRI and spectroscopy are sensitive to neuronal damage in an inducible TDP-43 mouse model of ALS
Authors: Virgincar Rohan , Vito Stephen , Choy Man Kin , Leahey Becca , Meilandt William , Xie Luke
Published: November 2024

Motivation: MRI is commonly used to monitor different neuropathologies, but has not been widely used for ALS research. Goal(s): We sought to investigate the utility of longitudinal MRI and MRS to assess the course of disease progression in an inducible TDP-43 mouse model of ALSApproach: T2, volume and NAA/tCr (N-acetyl aspartate/total creatine) were measured longitudinally in disease-relevant regions. Results: These metrics significantly changed in the 6-week induction phase of the model and reversed in the 6-week recovery phase, which was consistent with Amino-Cupric-Silver staining for degenerating neurons. NAA/tCr exhibited the greatest change: −29±14% at peak disease. Impact: We have demonstrated that T2 mapping, volumetric MRI, and MRS are sensitive to neuronal damage in a mouse model of ALS. These non-invasive endpoints therefore have the potential to be used clinically to monitor ALS progression and therapeutic efficacy.
Using Amide Proton Transfer Imaging to Detect Alzheimer’s Disease Pathology in Mouse Models
Authors: Wu Jingpu , Wan Jieru , Zou Yunfan , Yang Chongjun , Wang Puyang , Liu Dapeng , Han Xiaoning , Jiang Shanshan , Zhou Jinyuan
Published: November 2024

Motivation: APTw imaging can detect abnormal proteins associated with AD, but the APT signal is confounded by the NOE signal, which affects the diagnostic performance. Goal(s): We want to separate APT and NOE signals and see if the clean signals are better biomarkers for AD diagnosis than APTw signal. Approach: EMR fitting was performed voxel-wise. Group-based analysis of fitted APT#, NOE# and APTw signal values was performed inside cortex and hippocampus. Results: APT# and NOE# provide better contrast than APTw for AD diagnosis. The impact of NOE may explain the discrepancy between animal and human studies for AD. Impact: The fitted APT# and NOE# signals provide better diagnostic values for AD compared to traditional APTw imaging. The impact of NOE may explain the discrepancy between animal and human studies for AD and is worthy of further study.
Experimental validation of simulated SNR loss due to noise coupling
Authors: Findeklee Christian , Vernickel Peter , Leussler Christoph
Published: November 2024

Motivation: The aim of our study was to analyze the SNR loss due to noise coupling during reception with MRI coil arrays. Goal(s): In particular, we wanted to experimentally confirm the existing calculation methods. Approach: We compared the simulation predicted SNR loss due receiver noise against measurements for a single as well as for three coil elements. Results: A nice agreement was shown between theory and experiment. Impact: With our study, we also show how decisively noise coupling affects the SNR of an array.
Application of zero-echo time pulse sequence for functional MRI.
Authors: Imamura Ayako , Araki Rikita , Takahashi Yukari , Miyatake Koichi , Kato Fusao , Honjoh Sakiko , Tsurugizawa Tomokazu
Published: November 2024

Motivation: Zero-echo time (ZTE) sequence reduces susceptibility artifacts compared to echo planar imaging (EPI). However, the feasibility of ZTE for functional MRI (fMRI) has not been fully investigated. Goal(s): Our goal was to assess the feasibility of ZTE for fMRI. Approach: Time-course of ZTE, gradient echo (GE)-EPI, and spin echo (SE)-EPI signal changes by somatosensory stimulation was investigated. The functional connectivity in resting state ZTE, GE-EPI, SE-EPI was also compared. Results: The results indicate that ZTE is useful for fMRI. Impact: ZTE sequences realize the fMRI that is robust to susceptibility artifacts.
Glymphatic Diffusion-Weighted MRI in Awake Mice: Comparison to Anesthetized Brain States.
Authors: Gomolka Ryszard , Nedergaard Maiken , Mori Yuki
Published: November 2024

Motivation: This study investigates the glymphatic function in awake vs. anesthetized mice by means of diffusion-weighted (DW) MRI. Goal(s): Application of developed ‘silent’ DW-MRI protocol is presented for comparison of awake and anesthetized mice under ketamine-xylazine (K/X), and isoflurane. Approach: In total, n=17 C57BL/6 mice were habituated to awake MRI setup, and successfully underwent DW-MRI with monitored body temperature and respiratory rate. Results: Results showed that brain water mobility in awake mice was consistent, while K/X and isoflurane affected both slow and fast MR diffusivity measures differently. Awake DW-MRI offers a promising alternative to MRI under anesthesia, promoting increased biological relevance and translational potential. Impact: This research on glymphatic function using DW-MRI underscores the importance of non-invasive MRI in awake mice, emphasizing anesthesia's impact on physiological stability. It highlights the translational potential of awake DW-MRI and is of importance to neuroscientific and biomedical research communities.
Magnetic resonance elastography captures subtle changes in hippocampal CA1 stiffness between phases of the rat estrous cycle
Authors: Milbocker Katrina , Williams L , Wronski Alyssa , Zarate Emma , Grogin Melissa , Schwarb Hillary , Klintsova Anna , Johnson Curtis
Published: November 2024

Motivation: In rodent studies, the number of spines on hippocampal CA1 neurons decreases from proestrus to estrus, and complimentary reductions to hippocampal volume are observed in rodents and humans. It remains unknown if estrous-related changes to tissue microstructure alter the mechanical properties of CA1. Goal(s): Evaluate the mechanical properties of CA1 between proestrus and estrus in the adult rat brain. Approach: A novel rat Magnetic Resonance Elastography (MRE) protocol, measuring tissue deformation to determine stiffness and viscosity, was used to identify changes to the mechanical properties of CA1. Results: CA1 stiffness increases between proestrus and estrus phases in female rats. Impact: Results from this longitudinal study show that the stiffness of the hippocampal CA1 subregion increases between proestrus and estrus phases within-rat. We did not discover any changes to whole brain biomechanics between proestrus and estrus within-rat, consistent with the literature.
MRS4Brain Toolbox: an harmonized and accessible workflow for preclinical MRSI data processing
Authors: Briand Guillaume , Alves Brayan , Mosso Jessie , Pierzchala Katarzyna , Near Jamie , Lanz Bernard , Cudalbu Cristina
Published: November 2024

Motivation: Numerous software options exist for MRS processing, yet the preclinical domain lacks a harmonized multimodal toolbox featuring a graphical user interface (GUI). Goal(s): Our goal was to create a harmonized toolbox tailored to preclinical MRS processing, with a special focus on addressing MRSI-related challenges. Approach: The MRS4Brain Toolbox was designed to handle 1H and X nuclei data, and to allow brain segmentation of MRSI voxels in order to investigate brain regional differences. Results: Fast and user-friendly brain MRSI segmentation and metabolic mapping were achieved, promoting the study of brain-regional differences. Impact: The implementation of the open-source MRS4Brain Toolbox enables rapid and straightforward advanced preclinical MRS(I) data processing and quantification. It falls in line with the within- and across-site standardization effort launched by the MRS community.
MRI Characteristics of Invasive Intraductal Tubulopapillary Neoplasms versus Intraductal Papillary Mucinous Neoplasms of the Pancreas
Authors: Fang Xu , Bian Yun , Jiang Hui , Wang Li , Shao Chengwei , Lu Jianping
Published: November 2024

Motivation: Differentiation between ITPNs and IPMNs is important because even ITPNs with an invasive component are associated with a better prognosis than invasive IPMNs. Potentially targetable genetic alterations in ITPNs and reveal new therapeutic targets for patients with ITPN. Goal(s): Aimed to compare the MRI characteristics of ITPN and IPMN of the pancreas to differentiate between these two types of invasive carcinomas. Approach: Preoperative MR images of pathologically confirmed ITPN and IPMN were retrospectively reviewed. Results: Age, 2-tone duct sign and none cysitc lesion were independent predictors for invasive ITPN. Impact: The combined model based on MRI characteristics shows high diagnostic performance.
Prediction of varices risk in patients with liver cirrhosis based on the 4D Flow MRI technique
Authors: qin hu qin , hua yang , Lisha Nie
Published: November 2024

Motivation: The gold standard for clinical diagnosing varices is invasive gastroscopy. However, there is a need for a non-invasive method to accurately assess the risk of varices in cirrhotic patients. Goal(s): Exploring the potential of 4D Flow MRI flow quantification technique in predicting the risk of varices in patients with cirrhosis. Approach: Fifty-three patients with liver cirrhosis and twenty-seven healthy volunteers underwent clinically relevant examinations and 4D Flow MRI imaging. The hemodynamic parameters obtained from the imaging data were analyzed. Results: The combined index of main portal vein and splenic vein hemoflow effectively predicted the occurrence of varices in cirrhotic patients. Impact: The use of 4D Flow MRI for predicting varices risk in liver cirrhosis patients offers a non-invasive alternative to invasive gastroscopy, improving patient comfort and reducing risks. Additionally, this study advanced non-invasive diagnostics and personalized treatment strategies.
Towards robust high-quality abdominal diffusion weighted imaging: comparison of different reconstruction and signal averaging methods
Authors: Ogawa Takumi , Nagao Michinobu , Yoneyama Masami , Peeters Johannes , Bajaj Naveen , Neelavalli Jaladhar , Goto Yasuhiro , Shiina Isao , Morita Yasuyuki , Hamatani Yutaka , Kodaira Kazuo , Kato Mana , Sakai Shuji
Published: November 2024

Motivation: One of the limitations of current abdominal DWI is to obtain robust image quality, especially for SNR and image sharpness. Goal(s): Our goal was to compare a combination of different reconstruction and averaging methods for abdominal DWI and to explore which combination is the best. Approach: We compared five combinations of reconstruction and averaging methods and image quality was evaluated by visual evaluation and quantitative evaluation. Results: We demonstrated the image quality of abdominal DWI could be improved by using either Ssh-DWI or CS-Complex averaging with comparable ADC values to conventional methods. Impact: For the next step, a combination of CS reconstruction and Ssh-IRIS would be desirable for further improvement of image quality
Prediction of hemifacial spasm (HFS) re-appearing phenomenon after surgery in patients with HFS using DSC perfusion MRI
Authors: Jahng Geon-Ho , Lim Seung Hoon , Guo Xiao-Yi , Kim Hyug-Gi , Park Soonchan , Ryu Chang-Woo , Jin Wook
Published: November 2024

Motivation: DSC MRI may provide clues to predict hemifacial spasm (HFS) re-appearing phenomenon after surgery (HFSrapas) after microvascular decompression (MVD). Goal(s): To predict HFSrapas with DSC parameter indices using a machine learning analysisApproach: Sixty patients who underwent MVD for HFS were enrolled. DSC parameters were used to predict HFSrapas using a ROC curve and machine learning methods. Results: The rCBF value was significantly decreased in the reappearing group. The extraction fraction parameter was best predicted by the Navie Bayes (NB) model. Impact: DSC perfusion MRI is a useful tool to predict HFS recurrence before intra-operation and helps neurosurgeons anticipate possible problems during MVD surgery.
Volumetric Assessment of Brain MRIs of Fetuses with Spina Bifida
Authors: Ahmad Sahar , Hung Sheng-Che , Yap Pew-Thian
Published: November 2024

Motivation: Spina bifida occurs during the first gestational month and causes lifelong disabilities. If fetuses with spina bifida are left undiagnosed or untreated, spinal cord defects are translated into brain abnormalities. Goal(s): Our goal is to annotate brain regions in fetal MRIs to study typical and atypical fetal brain development in spina bifida. Approach: We developed a deep learning fetal brain MRI segmentation method and modeled growth to statistically compare brain volumes of normal and pathological cohorts. Results: Our segmentation method reliably annotates fetal brain MRIs. We observed significant increase in the ventricles and significant reduction of the cerebellum in fetuses with spina bifida. Impact: Fetal brain MRI segmentation with our segmentation model enables precise delineation of brain tissues and anatomical structures, allowing early detection of aberrant brain development due to congenital defects such as spina bifida.
Edited MRS of Glutamate
Authors: Saleh Muhammad , Prescot Andrew , Chang Linda , Cloak Christine , Cunningham Eric , Subramaniam Punitha , Renshaw Perry , Yurgelun-Todd Deborah , Zöllner Helge , Roberts Timothy , Edden Richard , Ernst Thomas
Published: November 2024

Motivation: Spectral editing methods are widely used to measure γ-aminobutyric acid (GABA), which also co-edits glutamate (Glu) at 2.34 ppm in the sum spectrum (sum=ON+OFF). However, the co-detection of Glu at 2.34 ppm has not been assessed. Goal(s): We demonstrate the co-editing of Glu without glutamine using HERMES of GABA and glutathione. Approach: Simulations of HERMES and 1D J-resolved of Glu, glutamine, and GABA, followed by in vivo experiments on 137 participants. Results: Simulations and in vivo experiments show a Glu-edited signal without overlapping glutamine and GABA signals from both methods. In vivo quantification of Glu show that the two methods are significantly correlated. Impact: Our study demonstrates a purer measurement of Glu using HERMES without needing a separate acquisition. HERMES provides an opportunity to study Glu/GABA/glutathione concurrently to understand their relationships under homeostasis or drug interventions that might affect the glutamatergic/GABAergic/antioxidant systems.
3D Dual-Polarity GRAPPA for Ghost Correction of Volumetric Echo-Planar Imaging Data
Authors: Hoge W. , Chang Yulin , Hu Zhangxuan , Poser Benedikt , Polimeni Jonathan
Published: November 2024

Motivation: 3D-EPI suffers from inherent sampling errors, similar to conventional 2D-EPI, which result in Nyquist ghosting and shading artifacts that arise from non-linear phase errors in the sampled data. Goal(s): To demonstrate that the Dual-Polarity GRAPPA (DPG) reconstruction method can be extended and applied effectively to 3D-EPI data. Approach: We modified the DPG kernel to extend to all three sampling directions, to better accomodate 3D-EPI data. Results: Phantom and in-vivo data demonstrate that DPG provides reconstructed images with lower levels of ghost artifacts and reduced artifacts from non-linear phase errors. This extension to DPG will enable high-fidelity reconstructions in future 3D-EPI applications. Impact: Our extension of Dual-Polarity GRAPPA to a 3D reconstruction kernel can improve image quality for 3D-EPI applications, providing higher fidelity and fewer artifacts than current conventional methods for improved imaging performance in emerging high-resolution fMRI and ultra-high-field imaging applications.
RESOLVE for simultaneous mapping high-frequency conductivity and micro-structure parameters in conductivity tensor imaging (CTI)
Authors: Sun Tong , Wu Songxiong , Wu Nashan , Sun Qingjun , Qin Haodong , Wu Guangyao , Chen Xin , Chang Chunqi
Published: November 2024

Motivation: Conventional two sequences acquisition in conductivity tensor imaging (CTI) can cause geometric mismatch between two acquisition data which may influence subsequent reconstruction. Goal(s): We aim to simultaneously reconstruct the high-frequency conductivity and to fit microstructure parameters with data from single sequence. Approach: We propose a novel data acquisition strategy which reconstructs the high-frequency conductivity from the phase of non-diffusion weighted data of RESOLVE. Results: The reconstructed high-frequency conductivity and fitted microstructure parameters has matched geometric images, and the low-frequency conductivity can be successfully reconstructed. Impact: We show the conventional two sequence acquisition is reduced to the single sequence acquisition which avoids the geometric mismatch and obtains higher precision of low-frequency conductivity.
Automated patient registration in low field MRI using deep learning-based height and weight estimation with 3D camera
Authors: Shahzadi Iram , Tamersoy Birgi , Frohwein Lynn Johann , Subramanian Sesha , Moenninghoff Christoph , Niehoff Julius , Kroeger Jan , Surov Alexey , Borggrefe Jan
Published: November 2024

Motivation: Automated patient positioning and Specific Absorption Rate (SAR) estimation in MRI is crucial for optimized image quality. Achieving these objectives necessitates precise patient parameter estimation. Typically, manual estimation of patient parameters, such as height and weight, is error-prone and time-intensive. Goal(s): To assess the 3D camera's potential for acquiring depth images suitable for deep learning (DL)-based estimation of patient height and weight. Approach: We employed 3D camera technology to capture depth images of patients on MRI tables, enabling DL-based height and weight estimation. Results: Our evaluation study demonstrated the 3D camera's effectiveness in acquiring depth images for accurate patient height and weight estimation. Impact: Current deep learning-driven 3D camera methods enhance MR imaging workflows with the goal of achieving standardized and higher-quality image acquisition by accurately predicting patient height and weight.
Automatically Quantitative Intra-tumoral susceptibility Signal and R2 star in discriminating subtype of ovarian cancer
Authors: Song Qingling , Liu Ailian , Li Ye , Hao Li , Shi Yuting , Song Qingwei , Wang Hongkai , Zhuang Mingrui
Published: November 2024

Motivation: It is important to quantitatively analyze the subtype of epithelial ovarian cancer using oxygen content, neovascularization, intra-tumoral hemorrhage. Goal(s): To investigate the R2* value of ESWAN and quantitative ITSS obtained automatically in discriminating subtype of epithelial ovarian cancer. Approach: R2* value of ESWAN sequence and ITSS ratio of ovarian cancer lesion were measured, and the value of R2* and ITSS in discriminating subtype of EOC was analyzed. Results: The area ITSS ratio and volume ITSS ratio of type II ovarian cancer were significantly higher than those of type I EOC. There was no significant difference between type I and type II EOC. Impact: The area ITSS ratio and volume ITSS ratio have certain value in discriminating type I from type II EOC. The value of R2* value in the differentiation of subtype of epithelial ovarian cancer need to be further explored.
Quantitative Assessment of Perineural Invasion in Cervical Carcinoma using IVIM Combined with mDixon-Quant Multiparameter Imaging
Authors: Zongyuan Xie , Shifeng Tian , Ailian Liu
Published: November 2024

Motivation: Perineural invasion (PNI) is a significant prognostic factor in cervical carcinoma（CC）. The utilization of quantitative parameters derived from IVIM and mDixon-Quant MRI offers a new approach for evaluating PNI in CC. Goal(s): This study aimed to investigate the potential value of IVIM and mDixon-Quant multiparameter imaging in quantitatively assessing PNI in CC. Approach: MRI scans were conducted on 36 patients with CC to explore the correlation and diagnostic efficacy between MRI quantitative parameters and PNI in CC. Results: The combined assessment of IVIM and mDixon-Quant quantitative parameters demonstrated a correlation with PNI in CC ,could determine the status of CC PNI. Impact: The quantitative parameters derived from IVIM and mDixon-Quant MRI are significantly associated with PNI in cervical carcinoma. These parameters hold promising clinical application prospects for evaluating neural invasion in cervical carcinoma.
Better diagnostic value and feasibility of Deep Learning DWI in uterine malignant neoplasms
Authors: Li Jian , Song Ling , Jiang Yueluan , Benkert Thomas
Published: November 2024

Motivation: Conventional diffusion-weighted imaging (c-DWI) of the uterus is time-consuming, and the lesion details are not well-defined. Goal(s): To introduce a deep learning (DL) DWI sequence in uterine MRI and compare it with conventional DWI (c-DWI) to investigate its impact on examination time, image quality, lesion significance, diagnostic reliability, as well as contrast ratio (CN), signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR). Approach: 10 patients with uterine malignancy disease were included in this study. Results: There is no significant difference in objective assessment between the two techniques, while the overall image quality of DL-DWI is better than c-DWI (p < 0.01). Impact: The research investigated the utilization of DL-DW in the uterus, which led to shorter examination times and significantly improved image quality. This analysis has the potential to examine other pelvic organs, such as the prostate, to assess pelvic lesions.
Vasculature Assessment of Rhesus Macaque Placental Injury using Variable Flip Angle T1-Mapping and Dynamic Contrast Enhanced MRI
Authors: Chen Ruiming , Seiter Daniel , Vazquez Jessica , Keding Logan , Antony Kathleen , Simmons Heather , Basu Puja , Mejia Andres , Johnson Kevin , Stanic-Kostic Aleksandar , Liu Ruo-Yu , Shah Dinesh , Golos Thaddus , Wieben Oliver
Published: November 2024

Motivation: A combination of DCE and fractional blood volume analysis could enable better evaluation of local placental injury. Goal(s): To generate and assess local placental injury for rhesus macaques using Tisseel (fibrin sealant) injections and co-registered contrast arrival time maps and fractional blood volume maps. Approach: Three rhesus macaques received intraplacental injections of Tisseel to potentially cause tissue ischemia. DCE imaging and variable flip angle T1 mapping were registered and compared qualitatively and quantitatively on a local level. Results: Possible local placental ischemia observed in both the DCE signal and the blood volume maps. Lower circulating blood volume corresponded regionally to longer arrival times. Impact: This study successfully creates local placental ischemia that potentially mimics clinical cases seen with fetal growth restriction. The local ischemia could be better visualized using two co-registered MRI methods, thus helping future clinical diagnosis.
Characteristics of renal amide proton transfer imaging in a healthy population
Authors: Zhang Sheng , Wang Xia , Liu Xiaofan , Yu Chanjuan , Tian Gang , Yue Xiuzheng , Han Yuedong
Published: November 2024

Motivation: The changes in renal protein levels are closely related to physiological and pathological functions. Amide proton transfer weighted (APTw) imaging has the function of non-invasive and targeted detection of tissue-free proteins or peptides and is of great significance in the preliminary study of acute and chronic kidney diseases. Goal(s): The study of APT imaging in normal kidneys still needs to be completedApproach: The APT images study of 49 volunteers in this study. Results: The APT values of the renal cortex and medulla were consistent, and the medulla was significantly higher than the cortex. Impact: APT imaging is initially applied to the urinary system, such as chronic kidney disease, kidney cancer, etc. In this study, the application value of APTw imaging in renal dermal medulla was investigated
Physics-Driven Learned Deconvolution of Multi-Spectral Fluorine-19 MRI with Multiple Agents Using Radial Sampling
Authors: Chen Jiawen , Pal Piya , Ahrens Eric
Published: November 2024

Motivation: Detection of multiple cell targets separately labeled with different chemically-shifted, paramagnetic 19F tracers can benefit from radial k-space sampling pulse sequences; however, radial sampling can lead to non-linear smearing chemical shift artifacts. Goal(s): Our goal is to develop suitable modeling of the radial chemical shifts and a physics-informed deconvolution scheme to unmix multi-spectral components. Approach: We proposed a novel Radon transform modeling of forward operator for radial chemical shifts and introduced machine learning based method for multi-spectral deconvolution. Results: Radial chemically-shifted artifacts are significantly reduced via the deep unrolling learned deconvolution algorithm, especially for low signal-to-noise-ratio (SNR) and highly undersampled acquisitions. Impact: Unlike Cartesian chemical shifts that result in image displacements, radial chemical shifts produce more complex artifacts. To effectively unmix the multi-spectral 19F components, we developed an analytical model using Radon transform and a data-driven deconvolution method based on deep unrolling.
Optimization of Deep Learning-Accelerated 3D-T1-MPRAGE and Quantitative Assessment of Regional Brain Volumes Compared to Wave-CAIPI MPRAGE
Authors: Lo Wei-Ching , Gil Nelson , Tabari Azadeh , Nickel Dominik , Lang Min , Vedjani-Jahromi Maryam , Clifford Bryan , Conklin John , Huang Susie
Published: November 2024

Motivation: Deep-learning-accelerated MPRAGE holds potential to enhance image resolution, reduce acquisition times, and improve diagnostic precision. However, there is currently a lack of clinical validation regarding its performance in neuroimaging. Goal(s): To optimize and assess the performance of deep-learning-accelerated MPRAGE in comparison to Wave-CAIPI MPRAGE for non-contrast T1-weighted volumetric brain imaging. Approach: In this prospective clinical study, we systematically optimized and implemented a novel deep-learning-accelerated MPRAGE sequence and compared against Wave-CAIPI MPRAGE, a state-of-the-art acceleration method. Results: Deep-learning-accelerated MPRAGE enhances resolution and grey-white matter differentiation compared to Wave-CAIPI MPRAGE, with equivalent volumetric estimation in most brain regions. Impact: Deep-learning-accelerated MPRAGE yields sharper, higher-resolution images while preserving equivalent volumetric estimations. This technique holds significant potential for deploying deep learning across various medical imaging disciplines, potentially enabling faster and more precise disease characterization.