bluuebunny/crossref_metadata_2025 huggingface dataset:
Transformation of the histopathological growth pattern of colorectal liver metastases after chemotherapy predicted by an MRI radiomics model
Authors: Wei Shengcai , Cheng Jing , Wang Yi
Published: August 2024

Our study used an MRI-based radiomics model to predict the transformation of the histopathological growth pattern (HGP) of colorectal liver metastases (CRLMs) before and after chemotherapy. After collecting data, drawing regions of interest and analyzing radiomics, we enrolled 152 patients and 299 liver metastases (99 pure desmoplastic (pdHGP) and 174 non-pdHGP). The pdHGP in the non-chemotherapy group and the post-chemotherapy group accounted for 28.3% and 42.5%, respectively (P=0.019). The fused MRI-based radiomics model demonstrated good predictive performance, and it could predict pdHGP before chemotherapy (25.3%), which was significantly different (p=0.034) compared with postoperative pdHGP after chemotherapy (39.1%).
A Personalized Federated Learning Approach for Multi-Contrast MRI Translation
Authors: Dalmaz Onat , Mirza Muhammad , Elmas Gokberk , Ozbey Muzaffer , Dar Salman , Ceyani Emir , Avestimehr Salman , Çukur Tolga
Published: August 2024

MRI contrast translation enables image imputation for missing sequences given acquired sequences in a multi-contrast protocol. Training of learning-based translation models requires access to large, diverse datasets that are challenging to aggregate centrally due to patient privacy risks. Federated learning (FL) is a promising solution that mitigates privacy concerns, but naive FL methods suffer from performance losses due to implicit and explicit data heterogeneities. Here, we introduce a novel FL-based personalized MRI translation method (pFLSynth) that effectively addresses implicit and explicit heterogeneity in multi-site datasets. FL experiments conducted on multi-contrast MRI datasets show the effectiveness of the proposed approach.
Evaluation of Cardiac Microstructure in Patients with Severe Aortic Stenosis Using Diffusion Tensor MRI with Submillimeter Resolution
Authors: Rock Christopher , Chen Iris , Philip Anne , Keil Boris , Nguyen Christopher , Sosnovik David
Published: August 2024

This study describes a method to acquire submillimeter resolution diffusion tensor MRI scans of the human heart in vivo. Imaging was performed in healthy controls and patients with severe high flow-high gradient aortic stenosis. The images were processed to generate helix angle maps of cardiomyocyte orientation and compared. The range of helix angles across the myocardium was higher in subjects with aortic stenosis and the relative helix angle slope was also increased in patients with aortic stenosis.
Free-breathing, Whole-heart, High-dynamic-range Quantitative Susceptibility Mapping (HDR-QSM) for Imaging Hemorrhagic Infarctions
Authors: Huang Yuheng , Guan Xingmin , Zhang Xinheng , Tang Liqi(Richard) , Bi Xiaoming , Han Fei , Lee HsuLei , Han Hui , Christodoulou Anthony , Li Debiao , Dharmakumar Rohan , Yang Hsin-Jung
Published: August 2024

Accurately detecting and quantifying intramyocardial hemorrhage(IMH) is critical for patient management. QSM has evolved into the standard method for iron imaging. However, obtaining cardiac QSM for IMH evaluation is difficult due to well-known technical challenges. Here, we developed a motion-robust 3D multi-echo GRE technique and combined them with a high-dynamic-range QSM algorithm to derive reliable QSM maps in IMH hearts. We tested and validated it in phantom, ex-vivo, and in-vivo IMH hearts in animal models. We demonstrated that the proposed method could accurately detect IMH and reliably quantify iron concentration with a free-breathing scan under 6 minutes.
Personalized Breast MRI Scanning Using Deep Learning
Authors: Eskreis-Winkler Sarah , Bhowmik Arka , Comstock Christopher , Sutton Elizabeth , Sevilimedu Vardan , Thakur Sunitha , Pinker Katja
Published: August 2024

Contrast-enhanced breast MRI exams typically last more than 20 minutes even though the dynamic contrast-enhanced sequences alone would be sufficient for interpretation in over 95% of cases. Thus, we present a personalized on-the-fly MRI protocoling paradigm, where a deep learning algorithm uses images acquired during the first few minutes of the exam to triage patients to an abbreviated versus full MRI protocol. We conduct a retrospective reader study to show that this personalized scanning paradigm decreases scan time and cost while maintaining diagnostic performance.
Deep learning improves breast IVIM estimation in better benign and malignant lesion differentiation
Authors: Shi Shuhao , Wang Lu , Bao Jianfeng , Wu Zhigang , Cai Congbo , Chen Zhong , Wang Jiazheng , Cai Shuhui
Published: August 2024

Intravoxel incoherent motion (IVIM) with multiple b-values, as an advanced diffusion model, provides accurate identification of breast cancer. However, the IVIM-derived parameters vary greatly depending on different fitting methods, especially for parameters D* and f. In this study, we proposed a method for high-quality breast IVIM reconstruction based on deep neural network. Data analysis shows that our proposed method improves the visual quality of breast IVIM parametric maps with better benign and malignant breast lesion differentiation ability compared to the traditional least-square fitting method.
Optimizing Reproducibility of White Matter Tractography in Longitudinal Imaging of a Large Sample of Developing Adolescents
Authors: Nedelec Pierre , Lashof-Regas Samuel , Rauschecker Andreas , Sugrue Leo
Published: August 2024

The best method for automatically producing replicable white matter (WM) tracts from diffusion imaging is not known. We use Automated Fiber Quantification (AFQ) to compare probabilistic and deterministic models’ performance generating 18 WM tracts for each of 1000 adolescents at baseline and 2-year follow up. We gauged performance on the correlation between fractional anisotropy (FA) values and degree of volumetric overlap (DICE score). Based on these measures we found that a probabilistic approach was able to more reliably generate tract profiles over time within individual subjects.
Separation of cortical and non-cortical networks to visual-somatosensory interaction by fMRI with optogenetic silencing
Authors: Dinh Thi Ngoc Anh , Kim Seong-Gi
Published: August 2024

Multisensory integration is necessary for the animal to survive in the real world. Multiple studies with conventional methods established the multisensory integration process in multiple brain areas. This study aimed to investigate interactions between visual and somatosensory networks in a whole brain scale using 15.2T BOLD fMRI with the assistance of cortical silencing by optogenetic stimulation. Keywords: ultrahigh field fMRI, optogenetics, multimodal integration.
An improved sub-voxel QSM for susceptibility source separation
Authors: Li Zhenghao , Feng Ruimin , Wei Hongjiang
Published: August 2024

Conventional quantitative susceptibility mapping (QSM) reconstruction methods only provide voxel-averaged susceptibility value. We proposed a comprehensive complex signal model that describes the relationship between the 3D GRE signal and the contributions of opposing susceptibility to the frequency shift and $$$R_2^*$$$ relaxation. We used an iterative data fitting scheme to alternatively determine the sub-voxel susceptibilities and the voxel-wise proportionality coefficient between $$$R_2'$$$ and absolute susceptibility. Our experiments on in-vivo human brain data compared with histological staining images demonstrate that the proposed method provides more accurate results for quantifying brain iron and myelin than previous QSM separation methods.
Sequential Gradient Superposition (SGS) FREE
Authors: Jenkins Parker , Wu Xiaoping , Adriany Gregor , Garwood Mike
Published: August 2024

Frequency-modulated Rabi Encoded Echoes (FREE) is a RF encoding technique that has the potential reduce the overall costs of MRI by eliminating B0 gradient hardware and infrastructure. However, linear encoding quality has limited its efficacy. To address this problem, we present Sequential Gradient Superposition (SGS) FREE. With SGS-FREE we can decompose the net effective RF gradient into a series of sequentially applied RF gradient sets. This ultimately allows imcreased flexability in linear encoding. This simulation study explores the principle and demonstrates high resolution imaging potential with FREE technology.
Accelerated Imaging at Ultralow Magnetic Fields: A comparative study of traditional and neural-network-based reconstruction approaches
Authors: Waddington David , Shimron Efrat , Shan Shanshan , Koonjoo Neha , Shen Sheng , Rosen Matthew
Published: August 2024

Portable MRI scanners that operate at very low magnetic fields are increasingly being deployed in clinical settings. However, accelerated acquisition and reconstruction methods that boost the quality of low-field MR images are needed to improve the diagnostic accuracy of the modality. Here, we compare leading data-driven and model-driven deep learning frameworks to compressed sensing (CS) for the reconstruction of undersampled ultralow field MRI data, finding that neural network approaches can boost quantitative image reconstruction metrics.
Ultra-Low Field Quantitative T2 Mapping
Authors: Dvorak Adam , Balaji Sharada , Poorman Megan , Padormo Francesco , Teixeira Rui Pedro , Haskell Melissa , Ragunathan Sudarshan , Hollander William , MacKay Alex , Williams Steven , Deoni Sean , Ljungberg Emil , Kolind Shannon
Published: August 2024

We demonstrated quantitative T2 mapping at 0.064 T using a multi-echo spin-echo sequence and CALIPR subspace constrained image reconstruction. CALIPR reconstruction results had minimal evidence of undersampling, providing better artifact suppression and preservation of fine anatomical detail than compressed sensing reconstruction. Phantom validation work was performed using 14 vials doped with known concentrations of MnCl2 and showed a relationship between measured T2 and MnCl2 concentration in agreement with expectations from a mathematical model. In-vivo, we demonstrated short acquisition time (8m:40s or 5m:59s), strong agreement with a highly-sampled reference acquisition, and T2 maps with good contrast between tissue types.
Fast and High-resolution Luminal Water Imaging in the Prostate Based on Echo Merging and k-t Undersampling with Reduced Refocusing Flip Angles
Authors: Li Hao , Sushentsev Nikita , Kessler Dimitri , Li Shaohang , Lee Kang-Lung , Priest Andrew , Gallagher Ferdia , Barrett Tristan
Published: August 2024

The clinical application of luminal water imaging (LWI) has been restricted by its long acquisition time. We present a high acceleration method based on T2 mapping using echo merging plus k-t undersampling with reduced flip angles (TEMPURA), which enables a fast acquisition in 2.8 min as well as a high-resolution acquisition in 5.4 min. Their performance was evaluated on 13 patients with biopsy-proven prostate cancer. Compared with the standard acquisition in 8.3 min, both the fast and high-resolution methods showed a high correlation in LWI measurements and consistent diagnostic performance in detecting malignant lesions.
THE ADDED VALUE OF HYBRID MULTIDIMENSIONAL MRI TO MULTIPARAMETRIC MRI IN DIAGNOSING CLINICALLY SIGNIFICANT PROSTATE CANCERS
Authors: Lee Grace , Chatterjee Aritrick , Karademir Ibrahim , Engelmann Roger , Yousuf Ambereen , Giurcanu Mihai , Harmath Carla , Karczmar Gregory , Oto Aytekin
Published: August 2024

1. In a retrospective review of 61 men by four readers, two inexperienced-readers had higher accuracy using multiparametric MRI+Hybrid-Multidimensional MRI (mpMRI+HM-MRI)=82%,81% versus Multiparametric MRI=77%, 71% (p=.006,<.001) and higher specificity=89%,88% versus 84%,75% (p=.009, p<.001) on a per-sextant analysis. 2. Multiparametric MRI+Hybrid-Multidimensional MRI increased trainee specificity=46% versus Multiparametric MRI=7% (p<.001) on a per-patient analysis. 3. Multiparametric MRI+Hybrid-Multidimensional MRI increased interreader Kappa agreement=0.36 versus Multiparametric MRI (mpMRI) Kappa=0.17 (p=0.009) in diagnosing clinically significant prostate cancer (CS PCa).
Highly accelerated Cardiac CINE MRI using Neural Fields
Authors: Catalán Tabita , Courdurier Matías , Osses Axel , Botnar René , Costabal Francisco Sahli , Prieto Claudia
Published: August 2024

Neural fields cardiac MRI (NF-cMRI), a method for highly accelerated CINE reconstruction using deep learning, is proposed. NF-cMRI relies on an intensity network, based on neural fields with Fourier features to encode a continuous reconstruction. The network is trained with one undersampled radial k-space data set without the need of a fully-sampled reference image. Good image quality of the heart is achieved with 8 radial spokes/cardiac frame. Results are compared against GRASP. Future work will focus on reducing reconstruction time and evaluating the proposed approach in prospectively undersampled k-space data.
Retrospective motion correction and reconstruction for clinical 3D brain MRI protocols with a reference contrast
Authors: Rizzuti Gabrio , Schakel Tim , Huttinga Niek , van Leeuwen Tristan , Sbrizzi Alessandro
Published: August 2024

We discuss the application of a novel 3D retrospective rigid motion correction and reconstruction scheme to in-vivo data for clinical 3D brain protocols. The correction scheme leverages multiple scans contained in a typical MR session, of which some are corrupted by motion but at least one scan may be devoid of motion artifacts. The uncorrupted scan is then used as a reference to regularize a generalized rigid-motion registration problem. We discuss the potential of the proposed algorithm with a prospective in-vivo study.
Multi-echo functional MRS Glutamate measurements in visual cortex at 3T
Authors: Emeliyanova Polina , Parkes Laura , Williams Stephen , Lea-Carnall Caroline
Published: August 2024

fMRS studies have shown dynamic change in glutamate (Glu) concentrations during visual stimulation. Here, we examine the effect of TE from 9.3-280ms on the signal during fMRS acquisition and whether T2 relaxation parameters change during a visual task compared to rest. We observed a short Glu T2-compartment between 2.5-4.5ms arising from 48% (rest) and 16% (active) of the total signal with significant increase in Glu signal at TE =140ms during stimulation. These preliminary results indicate bi-exponential decay of Glu in the visual cortex at 3T encouraging future work with more robust fMRS paradigms.
Detecting bladder and pelvic floor motion during MRI using a small ultrasound-based sensor
Authors: Tibrewala Radhika , Keerthivasan Mahesh , Bruno Mary , Collins Christopher , Madore Bruno , Sodickson Daniel
Published: August 2024

Deep-seated motion due to digestion and bladder filling can be difficult to monitor during MR acquisition. In this study, we use a small ultrasound-based sensor to detect bladder and pelvic floor motion, with a real-time MRI acquisition and a pilot-tone motion-sensitive signal as a reference. A neural network is used to predict the bladder area at various times during the MRI scan by using the US signal features. Our results indicate that the correlations between the ultrasound sensor and the MRI are beneficial for detecting bladder motion over time.
Evaluation of Scout Accelerated Motion Estimation and Reduction (SAMER) MPRAGE for Visual Grading and Volumetric Measurement of Brain Tissue
Authors: Gil Nelson , Lo Wei-Ching , Clifford Bryan , Lang Min , Awan Komal , Splitthoff Daniel , Polak Daniel , Cauley Stephen , Huang Susie
Published: August 2024

Volumetric brain MRI using the T1-weighted-MPRAGE sequence is an important component of the clinical evaluation of dementia. However, MPRAGE has a long acquisition time and is especially prone to patient motion artifact. A recent development to address this issue is a technique called Scout Accelerated Motion Estimation and Reduction (SAMER). In this work, we used a set of 90 MPRAGE scans derived from 10 healthy volunteers to demonstrate that SAMER is effective at correcting various degrees of motion, including severe motion with non-diagnostic image quality, and greatly increases the accuracy of volumetric brain measurements.
Non-contrast assessment of blood-brain barrier permeability to water in mice: An arterial spin labeling study
Authors: Wei Zhiliang , Liu Hongshuai , Lin Zixuan , Yao Minmin , Li Ruoxuan , Liu Chang , Li Yuguo , Xu Jiadi , Duan Wenzhen , Lu Hanzhang
Published: August 2024

Blood-brain barrier (BBB) plays a critical role in brain health and diseases. However, there is a scarcity of tools to assess BBB integrity, particularly in mouse models. Here, we aimed to develop a non-contrast arterial-spin-labeling-based MRI technique to estimate BBB permeability in mice by measuring relative fractions of labeled water in cerebral veins. Systematic optimizations were performed to enhance signal sensitivity with a further study investigating reproducibility. The proposed method revealed significant BBB dysfunctions in a mouse model of Huntington’s disease, which were further validated with histology. Our method may open new avenues for preclinical mechanistic research or therapeutic trails.
Quantitative perfusion mapping by BOLD-DSC MRI with transient hypoxia in mouse under volatile anesthetics
Authors: LE THI THUY , IM GEUN HO , LEE CHAN HEE , KIM SEONG-GI
Published: August 2024

Transient hypoxia-induced BOLD-DSC perfusion imaging approach can noninvasively map cerebral blood flow (CBF) and cerebral blood volume (CBV) in animals with injectable anesthetics. Since animal models are frequently investigated in translational research under volatile anesthetics, we developed a simple gas delivery system for inducing transient hypoxia, and performed perfusion studies of mouse under isoflurane and dexmedetomidine. Highly sensitive and reproducible perfusion metrics were found, and regional impact of anesthesia to basal perfusion metrics was assessed.
Population Brain Perfusion Imaging: Arterial Spin Labeling in UK Biobank
Authors: Okell Thomas , Miller Karla , Craig Martin , Alfaro-Almagro Fidel , Thomas David , De Vita Enrico , Günther Matthias , Matthews Paul , Smith Stephen , Chapell Michael
Published: August 2024

UK Biobank is an ongoing study which will acquire brain imaging in 100,000 participants, along with detailed lifestyle, biochemical and genetic information, and long-term health records. Here we describe the recent addition of arterial spin labeling (ASL) perfusion imaging to UK Biobank, with preliminary analyses of the first ~3,500 subjects. These revealed a range of highly significant associations between ASL metrics and non-imaging measures (e.g., blood pressure), as well as other imaging measures (e.g., white matter hyperintensity volume). This should become a useful resource for studying perfusion differences across a population and prior to disease onset.
Deep Learning based MR Image Reconstruction from Uniformly Undersampled MR Data
Authors: Xiao Linfang , Liu Yilong , Zhang Zhizun , Zhu Ruixing , Chen Weijun , Ma Zeyao , Mao Congying , Wang Ke
Published: August 2024

MR Image reconstruction of uniformly undersampled data often relies on prior information estimated from additional calibration data, leading to compromised acquisition efficiency and flexibility. Here, we propose a joint multi-slice deep learning strategy for MR image reconstruction from uniformly undersampled data with complementary undersampling across adjacent slices. Specifically, we design a slice fusion block to fully exploit the structural and phase similarity in adjacent slices and a slice shift block to further suppress the aliasing artifacts introduced by uniform undersampling. Consequently, the proposed strategy enables accurate MR image reconstruction for both image magnitude and phase without additional calibration information.
Image reconstruction based on a physics-informed reverse diffusion model trained with magnitude-only data
Authors: Wech Tobias , Schad Oliver , Kleineisel Jonas
Published: August 2024

We trained a score-based generative diffusion model with cardiac MR images, which allows generating new, randomized instances of the given data distribution. By conditioning each step of the underlying reverse time stochastic differential equation with a physics-informed data consistency step, undersampled MR data can be reconstructed. An initial estimation of the complex phase, which slowly transfers into the actual phase of the image, allows to train the diffusion model with magnitude data only. The approach was evaluated in fast spiral dynamic cardiac MRI at 1.5T, where it provided superior SNR-levels compared to alternative acceleration techniques.
SPIRiT Diffusion: SPIRiT-driven Score-Based Generative Modeling for Vessel Wall imaging
Authors: Cao Chentao , Cui Zhuo-Xu , Cheng Jing , Jia Sen , Zheng Hairong , Liang Dong , Zhu Yanjie
Published: August 2024

Diffusion model is the most advanced method in image generation and has been successfully applied to MRI reconstruction. However, the existing methods do not consider the characteristics of multi-coil acquisition of MRI data. Therefore, we give a new diffusion model, called SPIRiT-Diffusion, based on the SPIRiT iterative reconstruction algorithm. Specifically, SPIRiT-Diffusion characterizes the prior distribution of coil-by-coil images by score matching and characterizes the k-space redundant prior between coils based on self-consistency. With sufficient prior constraint utilized, we achieve superior reconstruction results on the joint Intracranial and Carotid Vessel Wall imaging dataset.
Single breath-hold full abdominal T1 mapping using a CNN based short inversion-recovery sampling technique
Authors: Ahanonu Eze , Goerke Ute , Johnson Kevin , Toner Brian , Martin Diego , Deshpande Vibhas , Bilgin Ali , Altbach Maria
Published: August 2024

Comprehensive liver evaluation with T1 mapping requires full abdominal coverage with sufficiently high spatial resolution for detection of pathology. Existing methods for abdominal T1 mapping are only able to achieve partial coverage, primarily limited by the breath hold and the time required to sample the T1 recovery curve (T1RC) for accurate T1 estimation. We present a radial Look-Locker T1 mapping framework which utilizes short T1RC sampling combined with deep learning based T1 estimation to achieve full abdominal coverage within a single 20s breath hold period.
White Matter Neurometabolite Vulnerability Predicts Cognitive Decline in Alzheimer’s Disease: A High-Resolution 3D 1H-MRSI Study
Authors: Wang Danni , Zhang Miao , Zhao Yibo , Li Yudu , Jin Wen , Hu Jialin , Zhang Yaoyu , Li Biao , Liu Jun , Li Binyin , Liang Zhi-Pei , Li Yao
Published: August 2024

White matter (WM) damage plays an important role in AD and different mechanisms have been suggested for different brain areas from postmortem studies. Understanding the spatial patterns of pathological changes in WM is of great importance in AD diagnosis. Using a high-resolution 3D MRSI technique, we investigated the spatial patterns of neurometabolic changes in WM regions. We firstly derived neurometabolite vulnerability maps in AD, showing spatially varying patterns of NAA reduction and mIn elevation in WM regions, in distinct association with gray matter volume or Aβ deposition, respectively. The neurometabolic biomarkers showed improved prediction of cognitive decline of AD patients.
Semi-Supervision for Clinical Contrast-Weighted Image Synthesis from Magnetic Resonance Fingerprinting
Authors: Yurt Mahmut , Alkan Cagan , Schauman Sophie , Cao Xiaozhi , Liao Congyu , Iyer Siddharth , Cukur Tolga , Vasanawala Shreyas , Pauly John , Setsompop Kawin
Published: August 2024

Previous works have introduced deep models to synthesize clinical contrast-weighted images from magnetic resonance fingerprinting (MRF). Although these models achieve high synthesis accuracy, they demand full-supervision from fully-sampled training data of clinical contrasts which might become difficult to acquire across diverse sets due to scan costs. To eliminate undesirable reliance on full-supervision, we introduce a semi-supervised model, ssMRF, that allows training using accelerated references. ssMRF introduces a semi-supervised loss function based only on collected k-space samples of clinical contrasts, and further leverages complementary Poisson disc masks, via a multi-task learning protocol to synergistically synthesize multiple contrasts.
MR Motion Fingerprinting
Authors: Feng Li
Published: August 2024

This work proposes a new framework, called MR motion fingerprinting, for fast real-time 3D MRI with an imaging latency<500ms. MR motion fingerprinting has two stages of imaging. The first stage, called the off-view mode, acquires and reconstructs a 4D motion database consisting of hundreds of real-time 3D images and associated 2D motion fingerprints. The second step, called the live-view mode, only acquires 2D motion fingerprints, which are used to search for matched 3D images from the motion database. This new approach enables efficient, accurate and robust real-time imaging with ultralow latency, which could be used for adaptive radiotherapy on MRI-Linac.
Characterization of age- and gender-dependent differences in the inter-vertebral disc using MR-Fingerprinting and Textural Analysis
Authors: Menon Rajiv , Monga Anmol , Kijowski Richard , Regatte Ravinder
Published: August 2024

The study investigated age and gender differences in intervertebral disc (IVD) of the lumbar spine using a multiparameter MR fingerprinting (MRF) technique that simultaneously quantify T1, T2 and T1ρ. Seventeen healthy subjects were evaluated. There was a significant positive association between age and T1ρ of the IVD and between age and various gray level co-occurrence matrix features of T1 maps on texture analysis. There were no significant differences between males and females in T1, T2 and T1ρ of the IVD. The results demonstrate that multi-parameter MRF can effectively characterize early age-related IVD degeneration.
Inspecting placental microstructure in pregnancies affected by fetal congenital heart disease
Authors: Cromb Daniel , Slator Paddy , Price Anthony , Hall Megan , Alexander Daniel , Hajnal Joseph , Rutherford Mary , Hutter Jana , Counsell Serena
Published: August 2024

Congenital heart disease (CHD) is common and associated with abnormal placental development. We used novel diffusion MRI acquisition and analysis techniques to inspect placental microstructure in pregnancies affected by congenital heart disease (CHD), highlighting the potential value of this approach for in-utero identification of how placental development may deviate from normal in CHD.
Influence of White Matter Microstructure on Interhemispheric Processing Speed After Mild Traumatic Brain Injury Using Advanced Diffusion MRI
Authors: Chung Sohae , Bacon Tamar , Rath Joseph , Alivar Alaleh , Coelho Santiago , Amorapanth Prin , Fieremans Els , Novikov Dmitry , Flanagan Steven , Bacon Joshua , Lui Yvonne
Published: August 2024

The corpus callosum (CC) is especially vulnerable to mild traumatic brain injury (MTBI). Since it connects left and right cerebral hemispheres, damage to the CC or neighbor white matter (WM) pathways may specifically disrupt interhemispheric communication. Here we employ a mediation framework to study the collaboration between tissue microstructure of the CC and neighbor WM pathways that may influence interhemispheric processing. Our results show different patterns in individuals with recent MTBI compared to healthy controls in terms of the relationships between callosal microstructure and interhemispheric communication mediated by other pathways, highlighting tracks specifically related to primary visual and language processes.
Removal of Water and Lipid Signals from J-resolved 1H-MRSI Data Using an FID Reference and Physics-Based Subspaces
Authors: Jin Wen , Zhao Yibo , Li Yudu , Guo Rong , Liu Ruihao , Luo Jie , Li Yao , Liang Zhi-Pei
Published: August 2024

J-resolved 1H-MRSI provides a unique capability to quantify both brain neurotransmitters and metabolites. This paper presents a novel method to effectively remove water and lipid signals from J-resolved 1H-MRSI data with limited spatial encodings and without lipid suppression. The proposed method has been validated using data from a healthy subject and a brain tumor patient, producing impressive results.
Ultra-High b-Value DWI in Predicting Progression risk of Locally Advanced Rectal Cancer: A Comparative Study with Routine DWI
Authors: Zhang Guangwen , Zhang Jinsong , Xu Ziliang , Wei Xiaocheng , Ren Jialiang
Published: August 2024

The prognosis prediction of locally advanced rectal cancer (LARC) was important to individualized treatment, we investigated the performance of ultra-high b-value DWI (UHBV-DWI) in progression risk prediction of LARC and compare with routine DWI. It was found that ADCuh derived from UHBV-DWI performed better than ADC based on routine DWI in predicting prognosis of LARC. The model based on combination of ADCuh, TNM-stage and extramural venous invasion (EMVI) could help to indicate progression risk before treatment.
Relationship between muscle fat,vertebral density and ectopic fat deposition in patients with T2DM using IDEAL-IQ
Authors: an qi , zhang qinhe , liu ailian , lin liangjie , xie lizhi
Published: August 2024

This study was carried out to evaluate the correlations between abdominal fat, vertebral density and ectopic fat deposition patients with T2DM using IDEAL-IQ. We found that SAT area,VAT area,Pancreatic FF and Abdominal wall muscle FF was positively correlated with BMD in patients with T2DM.
Mesoscale distortion-free in-vivo dMRI at 7T using ROtating-view Motion-robust supEr Resolution EPTI (Romer-EPTI)
Authors: Dong Zijing , Polimeni Jonathan , Wald Lawrence , Wang Fuyixue
Published: August 2024

Pushing the spatial resolution of diffusion MRI to mesoscale is technically challenging due to the low SNR efficiency and severe image artifacts. Last year, we presented a novel ROtating-view Motion-robust supEr Resolution Echo Planar Time-resolved Imaging method (Romer-EPTI), providing significant gains (~3.5×) in SNR efficiency and high-quality images free from distortion with minimized motion artifacts. In this work, we further developed Romer-EPTI at 7T for in-vivo mesoscale dMRI. By exploiting the higher signal of 7T and the short-TE, high-SNR, distortion-free acquisition provided by Romer-EPTI, mesoscale whole-brain dMRI at an unprecedented 485-μm isotropic resolution was acquired.
Deep learning-based Motion-corrected Image Reconstruction for High-resolution Spiral First-pass Myocardial Perfusion Imaging
Authors: Awad Marina , Wang Junyu , Salerno Michael
Published: August 2024

Cardiovascular magnetic resonance (CMR) is susceptible to motion-induced artifacts from cardiac and respiratory motion, leading to poor image quality. The inter-frame motion artifacts make quantitative analysis for cardiac function evaluation difficult. Hence motion correction is an important pre-processing step before robust quantification of myocardial perfusion. We developed a deep learning-based framework for rapid and accurate motion correction of CMR perfusion imaging using a 2D U-Net that estimates the deformation field from a moving frame to a fixed frame.
Spinal cord MS lesion detection at 7T - Added value of MP2RAGE imaging and requirement for automatic lesion segmentation
Authors: MEDINA Nilser LAINES , TESTUD Benoit , DEMORTIERE Sarah , MCHINDA Samira , LE TROTER Arnaud , CALLOT Virginie
Published: August 2024

In this exploratory study focusing on MP2RAGE and MS lesions, we demonstrated the added value of 7T imaging for lesion detection, with +20-to-30% of additional lesions seen compared to 3T. We also demonstrated that both currently available automatic segmentation model in SCT and an exploratory model build and trained based on MP2RAGE 3T images were not optimal for detecting lesions in 7T images (although a small contribution with slightly higher PPV was obtained with our model). As 7T opens great perspectives, further study should now focus on specific training and analysis tools to deal with 7T resolution and contrasts.
Application of Direct Signal Control with Variable Excitation and Refocusing for T2-w TSE Musculoskeletal Imaging at 7 Tesla
Authors: Kraff Oliver , Schulz Jenni , May Markus , Scheenen Tom , Meurs Thomas , Schmitter Sebastian , Theysohn Jens , Quick Harald
Published: August 2024

First experiences with direct signal control with variable excitation and refocusing (DiSCoVER) for T2-weighted TSE imaging in 7 Tesla musculoskeletal (MSK) applications of hip, shoulder, and ankle are presented. Images were compared to static RF shimming aiming at a homogeneous excitation by using the MR system’s framework as well as optimizing for a B1+-efficient shim in an offline calculation. DiSCoVER yielded satisfying results within the ROI defined for signal optimization but showed more pronounced signal dropouts outside the ROI compared to static RF shimming. Workflow improvements were particularly noted for DiSCoVER as it calculates pTx scale factors under SAR constraints.
Multivariate association between brain functional networks and symptoms and psychosocial environment in adolescent depression
Authors: Gao Yingxue , Feng Ruohan , Li Yang , Zhou Zilin , Liang Kaili , Bao Weijie , Zhuo Lihua , Huang Guoping , Huang Xiaoqi
Published: August 2024

The current study investigated the multivariate correlations between resting-state functional network connectivity and symptoms and environmental measures in adolescents with major depressive disorder (MDD) using the sparse canonical correlation analysis. We identified one stable association mode which showed primary correlation of environmental stressors, especially the interpersonal stress, with functional connectivity of networks that support salience processing, reward and sensory processing. Based on this brain-behavior association, we were able to categorize adolescents with MDD into two subgroups and delineate how psychosocial factors contributed to the neurobiological mechanism underlying adolescent depression.
Normative Modelling of White Matter Microstructure in Early Psychosis and Schizophrenia
Authors: Pavan Tommaso , Alemán-Gómez Yasser , Jenni Raoul , Cleusix Martine , Alameda Luis , Cuenod Kim Do , Conus Philippe , Klauser Paul , Hagmann Patric , Jelescu Ileana
Published: August 2024

The great majority of studies in schizophrenia (SCHZ) failed to achieve recognizable spatial patterns of the disease due to its heterogeneity. With this abstract, the authors aim to understand WM microstructure patterns from an individual perspective in early psychosis and SCHZ using advanced diffusion MRI metrics, namely diffusion kurtosis imaging (DKI), the WM microstructure model White Matter Tract Integrity – Watson (WMTI-W) and normative modelling, a statistical method for studying individual differences as extreme deviations from the normality. Overall, deviations in SCHZ subjects were twice as frequent when compared to EP or CTRL while EP displayed more spatial heterogeneity.
Improved T1 and T2 mapping in 3D-QALAS using temporal subspaces and Cramer-Rao-bound flip angle optimization enabled by auto-differentiation
Authors: Arefeen Yamin , Jun Yohan , Gagoski Borjan , Bilgic Berkin , Adalsteinsson Elfar
Published: August 2024

3D-QALAS utilizes an interleaved Look-Locker acquisition with T2-preparation pulses for full brain quantification of T1 and T2. The sequence applies constant flip-angles and suffers from blurring due to k-space modulation from signal evolution during the lengthy echo-train. This abstract improves 3D-QALAS by (1) resolving the full temporal signal with subspace reconstructions to eliminate blurring, (2) optimizing acquisition flip angles with the Cramer-Rao-Bound using simulations compatible with auto-differentiation, and (3) decreasing the number of acquisitions within a repetition time, which could enable up to 40% reduced scan time. Simulation, phantom, and in-vivo results demonstrate the efficacy of the proposed sequence improvements.
Rapid 3D T1 Mapping Using Deep Learning-Assisted Look-Locker Inversion Recovery MRI
Authors: Pei Haoyang , Xia Ding , Xu Xiang , Yang Yang , Wang Yao , Liu Fang , Feng Li
Published: August 2024

Look-Locker inversion recovery (LLIR) imaging is an easy, accurate and reliable MRI method for T1 mapping. For 3D acquisition, LLIR imaging is usually performed with multiple repetitions, and additional idle time is placed between consecutive receptions. This idle time allows for signal recovery to improve SNR and ensures robustness to B1 inhomogeneity, but it also prolongs scan time. Simply eliminating the idle time reduces the accuracy of T1 quantification. In this work, a novel deep-learning approach was proposed to address this challenge, so that accurate 3D T1 maps can be generated from continuous 3D LLIR imaging without idle time.
Frequency-dependent diffusion-relaxation multidimensional MRI of the human brain
Authors: Johnson Jessica , Ross Thomas , Yang Yihong , Topgaard Daniel , Benjamini Dan
Published: August 2024

Using an integrative acquisition and processing pipeline that joins concepts from oscillating gradients, tensor-valued encoding, and diffusion-relaxation correlation, we comprehensively explored microstructure and local chemical composition in the human brain. Using both frequency-dependent and tensorial aspects of the encoding spectrum b(ω), we designed an in vivo, whole brain, 40-min 7D D(ω)-R1-R2 distribution acquisition protocol at 2mm isotropic resolution. Scanning eleven healthy participants, we demonstrated frequency/time-dependent changes of diffusion-relaxation correlations measures in the human brain. Finally, intra-scan test–retest repeatability of a range of reconstructed parametric maps was investigated.
Accelerated intracranial time-of-flight magnetic resonance angiography using wave-encoding
Authors: Ji Yang , Wu Wenchuan , Buck Matthijs , Okell Thomas , Jezzard Peter
Published: August 2024

3D time-of-flight (TOF) can be used to acquire a volume with high spatial resolution, making it a preferable choice for depicting smaller vascular structures. However, 3D TOF requires long acquisition times when acquiring multiple slabs and covering a large field of view, especially for high spatial resolution imaging. To accelerate acquisition and to improve image quality of TOF MRA, we developed an accelerated 3D intracranial TOF MRA sequence with wave-encoding (referred to as 3D wave-TOF) and evaluated two variants – wave-CAIPI and compressed-sensing wave (CS-wave).
Sparse Annotation Deep Learning for Prostate Segmentation of Volumetric Magnetic Resonance Images
Authors: Osman Yousuf Babiker M. , Li Cheng , Huang Weijian , Elsayed Nazik , Xue Zhenzhen , Zheng Hairong , Wang Shanshan
Published: August 2024

Deep neural networks (DNNs) have achieved unprecedented performances in various medical image segmentation tasks. Nevertheless, DNN training requires a large amount of densely labeled data, which are labor-intensive and time-consuming to obtain. Here, we address the task of segmenting volumetric MR images using extremely sparse annotations, for which only the central slices are labeled manually. In our framework, two independent sets of pseudo labels are generated for unlabeled slices using self-supervised and semi-supervised learning methods. Boolean operation is adopted to achieve robust pseudo labels. Our approach can be very important in clinical applications to reduce manual effort on dataset construction.
Development for hyperpolarized [1-13C]alpha-ketoglutarate MRI for metabolic imaging of normal volunteers and mutant IDH glioma patients
Authors: Kim Yaewon , Dang Duy , Slater James , Riselli Andrew , Gordon Jeremy , Chang Susan , Li Yan , Escobar Evelyn , Chen Hsin-Yu , Tan Chou , Suszczynski Chris , Bok Robert , Vigneron Dan
Published: August 2024

Hyperpolarized [1-13C]alpha-ketoglutarate (aKG) is a metabolic probe that can provide direct access to the metabolic pathway for glutamate production or 2-HG production in altered alpha-ketoglutarate metabolism via IDH mutations. With its potential to serve as a biomarker of IDH1 mutational status in glioma and a measure of aKG metabolism in normal healthy subjects, we developed a new chemistry preparation and standard operating procedure (SOP) for on-site production of hyperpolarized GMP [1-13C]alpha-ketoglutarate and characterized the probe to investigate the feasibility of this probe in clinical setting. Following process qualification trials with HP [1-13C]aKG, an FDA IND application was prepared and submitted.
The role of FK506 binding protein 5 in the early development of ischemic lesion using multi-parametric MRI
Authors: Lai Zih-Rong , Kang Yu-Ping , Lu Chia-Feng , Hsieh Bao-Yu , Lee Yi-Hsuan , Kao Yu-Chieh
Published: August 2024

To explore how the FK506 binding protein 5 (FKBP5) affect the ischemic stroke outcome, we employed Fkbp5-knock out (KO) mice and evaluated the ADC- and CBF-deficit volume, as well as angiography in the acute phase and the final lesion volume at 24 hours after the occlusion of middle cerebral artery (MCAO). The discent lesion volume and loss of collaterals after MCAO in the Fkbp5-KO animals suggested the early determination of the stroke outcome in the Fkbp5-KO animals after ischemic insult.
Pseudo-continuous arterial spin labeling evaluation of collateral circulation at 7T and 3T MRI in Moyamoya disease
Authors: Lyu Jinhao , Duan Qi , Duan Caohui , Bian Xiangbing , Wang Danny , Zhao Chenyang , Qu Jianxun , Xin Lou
Published: August 2024

We compared the performance of pseudo-continuous arterial spin labeling (ASL) between ultrahigh field 7T MRI and 3T MRI in evaluating collateral circulation based on arterial transit artifact (ATA) in Moyamoya disease (MMD). 7T ASL shows more subtle hypoperfusion and more prominent ATA as compared with 3T ASL. The performance of collateral circulation assessment is more favorable by 7T ASL. These findings render 7T ASL a competing none-invasive approach in the management of MMD.
Mapping the vascular reserve in white matter and basal ganglia using Ferumoxytol-enhanced MRI at 7T
Authors: Li Chenyang , Jiang Li , Sun Zhe , Muccio Marco , Chen Yongsheng , Buch Sagar , Haacke E.Mark , Zhang Jiangyang , Ge Yulin
Published: August 2024

In this work, we use high resolution susceptibility weighted imaging (SWI) and ΔR2* to investigate the cerebral blood volume (CBV) in white matter tracts and basal ganglia after the administration of blood-pool contrast agent Ferumoxytol. Utilizing the merit of ultrahigh field and strong T1 and T2* shortening contrast agent, we further probe the effect of microvascular density on capillary CBV in white matter tracts and basal ganglia, which provides additional insights to the vascular reserve at capillary level in white matter and basal ganglia.
Amide proton transfer image corrected by apparent diffusion coefficient improved diagnostic accuracy in grading brain tumors on a PET/MR system
Authors: Sagiyama Koji , Togao Osamu , Kamitani Takeshi , Yamashita Koji , Yamasaki Yuzo , Kikuchi Kazufumi , Hino Takuya , Ishigami Kousei
Published: August 2024

Amide proton transfer (APT) imaging is useful for grading brain tumors; however, its diagnostic accuracy is compromised by the heterogeneity of the tumor's structure. In this study, we investigated using a PET-magnetic resonance (PET/MR) system whether dividing the APT by the apparent diffusion coefficient (ADC) improves the diagnostic ability of high-grade and low-grade brain tumors by correcting for the effects of tissue structure. The corrected APT (APT/ADC) showed the best diagnostic performance in grading brain tumors compared to APT, ADC, and standardized uptake value (SUV) alone.
Dynamic glucose e­nhanced MRI of gliomas: a preliminary clinical application.
Authors: Mo Jianhua , Xu Xiang , Ma Andong , Lu Mingjun , Wang Xianlong , Rui Qihong , Zhu Jianbin , Wen Haitao , Lin Genyun , Zhao Chen , Knutsson Linda , Zijl Peter , Wen Zhibo
Published: August 2024

In this study, we explored the feasibility of dynamic glucose enhanced (DGE) MRI technology in the clinical application of gliomas. 20 glioma patients underwent pre-operative DGE-MRIs before clinical intervention. We observed a significant increase in DGE area under the uptake curve (AUC) signal in tumors compared to the white matter. In some cases, we found enhancement in DGE MRI in histopathological confirmed tumor region that was not enhanced by Gd T1w MRI. These findings provide a new perspective for the further exploration and analysis of the D-glucose delivery, uptake and metabolism in brain tumors.
Direct detection of neural activity in humans by ultrafast high-sensitivity SLAM MRI at 3T
Authors: Zhang Yi , Liu Peiying , Wang Wenqi , Mao Deng , Bottomley Paul
Published: August 2024

The direct MRI detection of neural activity in humans is a long-sought goal that has been attempted by many. We posit that a key reason for prior failures is inadequate sensitivity. Spectroscopy with linear algebraic modeling (SLAM) is a compartmental imaging method that provides enormous gains in speed and signal-to-noise ratio (SNR). Here we use it to directly obtain MRI signals from arbitrarily-shaped brain regions with arguably the highest possible SNR efficiency. We successfully detect neural responses elicited by rhythmic finger-tapping at the tapping frequency in humans at 3T, as confirmed by simultaneously-acquired EEG data.
2.5D Flow MRI: 2D phase-contrast of the tricuspid valvular flow with automated valve-tracking
Authors: Lamy Jerome , Xiang Jie , Seemann Felicia , Gonzales Ricardo , Huber Steffen , Steele Jeremy , Heiberg Einar , Peters Dana
Published: August 2024

Tricuspid regurgitant velocity is a crucial biomarker in identifying pressure overload in the right heart, associated with diastolic dysfunction and pulmonary hypertension. 2D phase-contrast cannot quantify this flow, and echocardiography is used clinically. We developed a phase-contrast method which utilizes deep-learning algorithms to track the valvular slice in a cardiac phase-dependent manner, which we call 2.5D flow. We studied its performance in nine healthy subjects and patients with tricuspid regurgitation. RV stroke volumes correlated better to forward flow volumes by 2.5D flow vs. static 2D phase-contrast (ICC=0.88 vs. 0.62). 2.5D flow characterized regurgitation in a patient.
Minimizing Background Phase in PC-MRI with the Gradient Impulse Response Function (GIRF) and Gradient Optimized (GrOpt) Velocity Encoding
Authors: Loecher Michael , Ennis Daniel
Published: August 2024

Background phase errors in PC-MRI flow measurements are caused by eddy currents and mechanical vibrations that produce unwanted magnetic fields, thereby reducing velocity measurement accuracy. In this work, we demonstrate that a GIRF measurement can be used to predict the PC-MRI background phase, then used to design gradient optimized (GrOpt) velocity encoding waveforms that minimize these errors. The method is tested in static phantoms and in vivo, where 4.8x to 6.4x reductions in background velocity are seen. Phantom results showed a reduction from 0.74±0.22 cm/s to 0.15±0.12 cm/s, and in vivo results showed reductions from 0.93±0.10 cm/s to 0.13±0.08 cm/s.
MR Spatiospectral Reconstruction using Plug&Play Denoiser with Self-Supervised Training
Authors: Zhao Ruiyang , Li Yahang , Wang Zepeng , Anderson Aaron , Arnold Paul , Huesmann Graham , Lam Fan
Published: August 2024

We introduced a data-driven denoiser trained in a self-supervised fashion as a novel spatial-temporal constraint for MRSI reconstruction. Our proposed denoiser was trained using noisy data only via the Noise2void framework that trains an interpolation network exploiting the statistical differences between spatiotemporally correlated signals and uncorrelated noise. The trained denoiser was then integrated into an iterative MRSI reconstruction formalism as a Plug-and-Play prior. An additional physics-based subspace constraint was also included into the reconstruction. Simulation and in vivo results demonstrated impressive SNR-enhancing reconstruction ability of the proposed method, with improved performance over a state-of-the-art subspace method.