bluuebunny/crossref_metadata_2025 huggingface dataset:
Intra-subject repeatability and initial clinical evaluation of approximate myocardial spin-lock dispersion mapping
Authors: Tourais Joao , Zhao Yidong , Pierce Iain , Nitsche Christian , Thornton George , Akcakaya Mehmet , Tao Qian , Treibel Thomas , Weingartner Sebastian
Published: August 2024

Approximate myocardial spin-lock (SL) dispersion mapping was obtained using simultaneous TRAFF2 and T2 mapping with good image quality and consistent repeatability among healthy subjects at 3T. Initial patient data indicates clinical feasibility and sensitivity to scar tissue, as identified by LGE. T2 and TRAFF2 are sensitive to distinct macromolecular interactions, showing differential contrast in disease. SL dispersion may exploit this and allow for isolating contrast components (e.g. chemical exchange effects). Our results suggest that these may allow for robust, complementary, and comprehensive clinical sensitivity to pathological remodeling. Future evaluation in a larger patient cohort with different myocardial injuries is warranted.
Influence of Temporal Sampling on Reproducibility of Radiomics Features in Cardiac Cine MRI
Authors: Laube Ann , Ivantsits Matthias , Hüllebrand Markus , Tautz Lennart , Ter-Minassian Léa , Zhang Hannu , Doeblin Patrick , Kelle Sebastian , Hennemuth Anja
Published: August 2024

Radiomics has been applied in cardiovascular imaging with promising results. Still, the effect of imaging parameters on the reproducibility of radiomics features needs to be well understood for these to be used in clinical diagnostics. We conduct a retrospective study on short-axis cine CMR images of healthy volunteers to assess the impact of different temporal sampling parameters on the reproducibility of shape, first-order, texture, and deformation field-based features, and contextualize the findings with inter- and intra-observer variability. We find that the reproducibility of features is dependent on temporal sampling, warranting the introduction of parameter standardization for radiomics analyses.
Cardiac MRI Quantitative Analysis of Native T1 and Enhanced T1 Maps in Patients with Acute Heart Failure
Authors: Hao Yanhui , Guo Jianxin , Zhu Yi , Zhang Rui , Chen Lihong , Liu Jiantao
Published: August 2024

The purpose of this study was to investigate the predictive value of native T1 and enhanced T1 in the diagnosis of acute heart failure. Using echocardiographic ejection fraction and clinical symptoms as reference standards, The statistical results of our data showed that the native T1 value was found to have high sensitivity and specificity in predicting acute heart failure.
Papillary Muscle Derived Radiomic Features Facilitate LVH Detection and Improve LVH Etiology Classification
Authors: Lu Qifan , Liu Qiming , Chai Yezi , Jiang Meng
Published: August 2024

Detecting left ventricular hypertrophy(LVH) is common in a clinical scenario, while a precise classification of LVH etiology is quite challenging. In this study, we applied radiomics methods to LVH detection and LVH etiology classification. Innovatively, we extracted radiomic features not only from the myocardium(MYO) but also includes features from the papillary muscle(PM). After strict feature selection and analysis, we found the incorporation of PM features improves our model performance compared with models that simply use MYO features.
Impact of mitral valve repair on left atrial fibrosis in severe mitral valve regurgitation patients: a 3D LGE pilot follow-up study.
Authors: El Mathari Sulayman , Kluin Jolanda , Hopman Luuk , Bhagirath Pranav , Oudeman Maurice , Vonk Alexander , Chamuleau Steven , Klautz Robert , Nederveen Aart , van Ooij Pim , Götte Marco
Published: August 2024

Timing of mitral valve repair (MVR) surgery in mitral valve regurgitation (MR) patients remains a subject of debate. Assessment of left atrial (LA) fibrosis might aid to improve clinical management of these patients. We assessed LA fibrosis extent using 3-dimensional late gadolinium enhanced cardiac magnetic resonance imaging prior to and after MVR surgery in MR patients (n=6). Our data demonstrates a decrease of LA volume, while a concomitant increase in LA fibrosis was observed. Further study is required to determine the potential relation between LA fibrosis, pre- and post-operative changes and long-term clinical outcome.
Radiomics-Based Classification of Severe Pulmonary Regurgitation in Patients with Repaired Tetralogy of Fallot by Cardiac Magnetic Resonance
Authors: Peng Jo-Hua , Wu Ming-Ting , Pan Nai-Yu , Huang Teng-Yi , Liu Yi-Jui , Weng Ken-Pen , Peng Hsu-Hsia
Published: August 2024

Cardiac magnetic resonance (CMR) radiomics is a novel technique for advanced cardiac image phenotyping. This study aimed to develop a radiomics-based classification model by CMR images and thereby to identify rTOF patients with severe pulmonary regurgitation (PR). In our results, the radiomics-based classification model can successfully identify rTOF patients with PR ≥25% by routine CMR cine images. The extracted features underlined the RV intracardiac flow alteration due to PR and the potential right ventricular remodeling in rTOF patients with severe PR. This radiomics-based information may be helpful in determining the appropriate timing for pulmonary valve replacement in the future.
Best Response Constraint Generative Adversarial Network for Diffusion MRI-based Estimation of Cortical micro-Architecture
Authors: Zhu Tianjia , Ouyang Minhui , Liu Xuan , Liu Risheng , Huang Hao
Published: August 2024

Advanced diffusion MRI (dMRI) has enabled noninvasive assessment of conventional cortical histological measures. However, analytical models are limited by their restrictive model assumptions and lack of validation from quantitative histology. We have developed a Diffusion-MRI based Estimation of Cortical micro-Architecture (DECAM) method using a novel deep learning technique Best Response Constraint Generative Adversarial Network (BRC-GAN) for accurately estimating cortical soma density (SD) leveraging rich dMRI data information. By providing high-fidelity, reproducible whole-brain estimated SD maps validated with histology, DECAM paves the way for data-driven noninvasive virtual histology for potential applications such as Alzheimer’s diseases.
Assessing Sex Differences in the Brain’s White Matter Microstructure during Development using the Tensor Distribution Function
Authors: Benavidez Sebastian , Lawrence Katherine , Laltoo Emily , McCracken James , Thompson Paul
Published: August 2024

Diffusion magnetic resonance imaging (dMRI) can detect sex differences in developmental populations, as shown previously using the conventional diffusion tensor imaging (DTI) model. However, DTI cannot model complex brain fiber orientations, which the advanced tensor distribution function (TDF) model does. Here we compared the two models’ sensitivity in detecting sex differences, with the goal of improving our understanding of WM sex differences during development. We discovered WM microstructure sex effects, with the TDF model detecting more regions with significant differences and yielding larger effect sizes. Our results suggest that TDF is better able to detect developmental sex effects on WM.
Evaluating frequency offsets due to local dephasing in magnetically inhomogeneous tissue
Authors: Shaw Ross , Bowtell Richard , Gowland Penny
Published: August 2024

Through simulation the frequency offsets due to signal dephasing around biologically relevant local regions of high magnetic susceptibility are explored. Monte Carlo simulations are used to characterise the frequency offsets produced by spherical inclusions of varying radius and susceptibility in the presence of diffusion. The results indicate that heterogeneity of non-haeme iron concentration produce $$$\delta\Omega R^{2}/D$$$ values associated with local frequency offsets of up to $$$ -0.053 \gamma B_{0}\chi_{av}$$$: such offsets would be a confound for QSM measurements that merits further investigation.
Optimization of non-uniform oscillating gradient spin echo sequences for selective microstructure-size imaging
Authors: Capiglioni Milena , Zwick Analía , Álvarez Gonzalo
Published: August 2024

Diffusion-weighting imaging is a promising method for quantitative imaging of tissue microstructure features that may identify early stages of pathologies. Non-uniform oscillating gradient spin-echo (NOGSE) sequences are a novel technique to study diffusion spectra and quantitatively characterize tissue. The sequence contrasts the signal between two modulating gradient frequencies to filter the signal originating from molecules diffusion in specific restriction sizes. We determine the range of control parameters that maximizes NOGSE contrast for filtering the restriction sizes. We assess the restriction sizes that this sequence can filter with current technologies.
The effect of variable compressed sense (CS) undersampling patterns in hyperpolarized Xenon (129Xe) diffusion-weighted MRI
Authors: Tavakkoli Mitra , Svenningsen Sarah , Friedlander Yonni , Konyer Norm , Nair Parameswaran , Noseworthy Michael
Published: August 2024

Compressed sensing is a fast-imaging technique capable of making high quality images with undersampled data. However, its application in hyperpolarized 129Xe diffusion-weighted MRI needs to be optimized. In this study, diffusion weighted images undersampled identically and differently, were compared with SNR, SSIM, mean ADC using repeated measures ANOVA. The application of different undersampling patterns for both diffusion-weighted and baseline images can result in ADC maps with higher quality, as compared to fully sampled equivalent parametric images. Thus, the baseline and diffusion-weighted images do not necessarily have the same optimum undersampling pattern, and optimization of each needs investigation separately.
Mapping transmit and receive B1 using variable flip angle acquisition on a person-by-person basis for hyperpolarized Carbon-13 and Xenon-129 MRI
Authors: Yeung Kylie , Ravetz Zack , Ng Kher Lik , AbuEid Gabriele , Hickes William , Jacob Kenneth , Danisa Mitchel , Durrant Marianne , Mills Rebecca , Shinozaki Ayaka , McGing Jordan , Axford Aaron , Birkhoelzer Sarah , Schulte Rolf , Rider Oliver , McIntyre Anthony , Fraser Emily , Tyler Damian , Gleeson Fergus , Grist James
Published: August 2024

Hyperpolarized MRI allows the imaging of processes such as gas exchange and metabolism. Calibration of transmit and receive (Tx/Rx) B1 is required to account for coil inhomogeneities in reconstruction. A variable flip angle (VFA) method, which is fast and readily implemented, allows for a simultaneous B1/T1 measurement. In this study, a mathematical model of VFA calibration was developed, tested, validated in a hyperpolarized Carbon-13 (13C) phantom and in vivo with hyperpolarized Xenon-129 imaging of human lungs.
Impact of Label-Set on the Performance of Deep Learning-Based Segmentation of the Prostate Gland and Zones on T2-Weighted MR Images
Authors: Meglič Jakob , Sunoqrot Mohammed R. S. , Bathen Tone , Elschot Mattijs
Published: August 2024

Prostate segmentation is an essential step in computer-aided diagnosis systems for prostate cancer. Deep learning (DL)-based methods provide good performance for prostate segmentation, but little is known about the impact of ground truth (manual segmentation) selection. In this work, we investigated these effects and concluded that selecting different segmentation labels for the prostate gland and zones has a measurable impact on the segmentation model performance.
AI algorithms for classification of the mpMRI image sequences and segmentation of the prostate gland: an external validation study
Authors: Wang Kexin
Published: August 2024

In this study we evaluate the generalization of the AI algorithms for the classification of the mpMRI image sequences and the segmentation of the prostate gland with multicenter external dataset. A total of 719 patients who underwent multiparametric MRI (mpMRI) of the prostate were collected retrospectively from two hospitals. AI algorithms were tested for classification of the image type and segmentation of the prostate gland. The AI models demonstrated good performance in the external validation in the task of image classification and prostate gland segmentation.
Full and weak supervision networks for meniscus segmentation and multi classification based on MRI: data from the Osteoarthritis Initiative
Authors: Jiang Kexin , Xie Yuhan , Zhang Zhiyong , Hu Jiaping , Chen Shaolong , Zhang Zhongping , Qiu Changzhen , Zhang Xiaodong
Published: August 2024

Quantitative MRI of meniscus morphology (such as MOAKS system) have shown clinical relevance in the diagnosis of osteoarthritis. However, it requires a large workload, and often lead to deviation due to reader’s subjectivity. Therefore, based on automated segmentation of six horns of meniscus using fully and weakly supervised networks, we established two-layer cascaded classification models that can detect the meniscal lesions and further classify them into three types, and finally achieved excellent performance. This can improve the efficiency and accuracy of using quantitative MRI to study KOA in the future.
Ensemble Multi-Path U-net Segmentation Algorithm for Breast Lesion based on Multi-Modality Image
Authors: Yu Hang , Xie Zichuan , Xie Lizhi , Liu Zhiheng , Zhang Lina , Du Siyao , Yin Xiangjie , Li Chenyang , Jiang Wenhong , Guo Yuru , Kang Zhongqi
Published: August 2024

The multimodal MRI data is often ultilized for breast cancer analysis, and by now still difficult and inefficient to explored by segmentation algorithms. In this paper, we propose a MP-Unet based on U-net convolutional neural network, which can effectively ensemble multiple inputs of modal data and obtain accuracy segmentation results at the end. In MP-Unet, we reused some good quality modal data for training. The multiple MP-Unet models are further integrated based on Bagging algorithm to improve the segmentation accuracy of lesions. Experiments suggest that our proposed method has a huge performance improvement.
Volumetric vocal tract segmentation using a deep transfer learning 3D U-Net model
Authors: Erattakulangara Subin , Gerard Sarah , Kelat Karthika , Burnham Katie , Balbi Rachel , Meyer David , Lingala Sajan
Published: August 2024

The investigation into the 3D airway area is the prerequisite step for quantitatively studying the anatomical structures and function of the upper airway. Segmentation of upper airway can be considered as one of the stepping stones for this investigation. In this work, we propose a transfer learning-based 3D U-Net model with a ResNet encoder for vocal tract segmentation with small datasets training. We demonstrate its utility on sustained volumetric vocal tract MR scans from the recently released French speaker database.
Correlation of R2* with fat fraction and bone mineral density and its role in quantitative assessment of osteoporosis
Authors: Liu zhenghua
Published: August 2024

In this study, we investigated the relationship between vertebral R2* and FF based on IDEAL-IQ sequences and BMD based on QCT, and then performed diagnostic experiments. The results revealed a definite but weak linear relationship between R2* with FF and BMD, which has limited value as a diagnostic indicator for OP and osteopenia, but has some potential as a complement to FF and BMD for fine quantification of bone marrow conversion and bone mineral loss.
A clinical application of ZTE-MRI in diagnosis of knee using joint space and angles
Authors: Wang Hui , Liu Weiyin , Chen Wen , Jiang Jingyu , Sang Ling , Zhang Peng , Chen Hu
Published: August 2024

X-ray showed no significant abnormalities at early stage of OA, but obvious manifestation appeared in the middle and late stages. Our study demonstrated that joint space and joint angle can be measured on ZTE-MRI in evaluation of knee mechanical function and further support anatomical evaluation using Kellgren-Lawrence grading system that is the most widely used method for osteosurgeons in diagnosis of knee osteoarthritis. We also found both JS and CD were correlated with KLZTE score and both showed significant difference between mild/moderate and advanced groups. Therefore, ZTE-MRI has great potential in displaying structure abnormality.
Quantification of the effects on proximal femur trabecular bone biometrics by parallel imaging and compressed sensing reconstruction
Authors: Vu Brian-Tinh , Jones Brandon , Deshpande Rajiv , Lee Hyunyeol , Chan Trevor , Kamona Nada , Ripsman Sabrina , Ranaweera Dilini , Rajapakse Chamith
Published: August 2024

The high resolution needed for quantification of trabecular bone biometrics in the proximal femur results in a prohibitively long scan time for clinical screening. Parallel imaging and compressed sensing (PICS) can reduce scan times by undersampling k-space while producing images comparable in quality to that of the Nyquist-sampled scan. Here, we show PICS recovers information on the trabecular microarchitecture at a quality comparable to that of Nyquist-sampled 3D balanced steady-state free precession. We quantify several parameters of trabecular bone for various acceleration factors to investigate the upper limit of PICS acceleration for femoral imaging.
Evaluation of the application of integrated high-quality intervention mode in large-aperture MRI examination of patients with knee joint injury
Authors: Zhang Jun , Zhuang Lina , Wang Nan , Chen Lihua , Zhang Qinhe , Zhang Shuheng , Man Yuyin , Song Qingwei , Liu Ailian
Published: August 2024

The integrated high-quality intervention mode is adopted for patients with knee joint injuries, combined with super large aperture magnetic resonance equipment, to effectively relieve the negative emotions of patients, relieve pain, improve scanning efficiency and safety, and enhance scanning efficiency and safety.
Application value of compressed sensing-based MR FRACTURE sequence in bone and joint
Authors: Wang Nan , Song Qingwei , Liu Ailian , Wang Jiazheng , Lin Liangjie
Published: August 2024

FRACTURE sequence does not need to control TE time within 1ms, but uses multi-echo three-dimensional fast field echo, FFE) sequence to perform magnetic resonance imaging with fixed echo interval. After scanning, bone images with high contrast and high signal-to-noise ratio can be obtained through echo accumulation, subtraction and gray scale inversion.
Continuous wave radar for carotid pulse sensing in Magnetic Resonance Imaging
Authors: Kuo Renesmee , Pauly John , Robb Fraser , Vasanawala Shreyas , Scott Greig
Published: August 2024

Non-contact synchronization MRI with heart motion is still an open challenge. Inaccurate cardiac gating often complicates image reconstruction and leads to poor or non-diagnostic images. Continuous wave doppler radar is a non-contact, noninvasive novel systems technology that could monitor cardiac motions without distortion from the MRI electromagnetic environment while offering an alternative to state-of-the-art (ECG) triggering.
Monitoring performance of a GaN HEMT switch across different magnetic field strengths for implementation of On-Coil Tx Amplifiers
Authors: Setera Brett , Christou Aristos , Gudino Natalia
Published: August 2024

We present a method for monitoring multiple GaN HEMT device signals at different orientations with B0 during RF switching to determine possible magnetic field impacts on device performance and assist the development of an optimized GaN based device for on-coil amplification. Reduction in output current with increased magnetic field strength were not detected and device orientation did not impact signal amplitude.
MRI IN RADIATION THERAPY: VALIDATION OF THE EFFECT FROM BLANKET MRI-COIL ON THE PELVIS REGION OF HEALTHY VOLUNTEERS
Authors: Al-Abasse Yosef , Rung Tova Roman , Larsson Peter , Josefsson Dan , Lundkvist Lena , Redelius Kristina , Hultman Kristina , Nilsson Maria , Rzepecki Jan , Lundberg Peter
Published: August 2024

Conventional coils for dose planning purposes cannot be placed on the patient as the outer body contour then will be deformed. Coils are therefore placed on a special holder which creates a distance between coil and patient, but this leads to less SNR. The blanket-coils are significantly lighter and can be placed directly on the patient. Three similarity comparison methods were used here, Hausdorff distance, Dice similarity coefficient (DSC) and Surface DSC. Eight of eleven comparisons were within 4 mm difference for Hausdorff distance; Surface DSC was >99%, at a 3 mm tolerance, and DSC >98.5%.
Silicon Carbide (SiC) Based Flexible and Stretchable Dielectric Pad for B1 Field Enhancement
Authors: Hashemi Seyedamin , Christopher Kezia Sharon , Kandala Sri Kirthi , Song Kwanjoon , Bartelle Benjamin , Sohn Sung-Min
Published: August 2024

We propose a stretchable, flexible, and MRI invisible dielectric pad to enhance the quality of acquired MR images. A silicon-based elastomer is mixed with Silicon Carbide (SiC) to develop this proposed dielectric pad. Preclinical in-vivo imaging of a mouse head was conducted at 9.4T to validate the performance of the dielectric pad. 2.54% (in dB) increase in the central SNR of the brain was observed after using the dielectric pad and peripheral brain locations have shown improved contrast. The flexible and stretchable dielectric pad could be used as a substitute for conventional dielectric pads, especially in irregular sample shapes.
Developing an In-Bore MRI-Compatible Syringe Pump
Authors: Holland Martin , Lee Seth , Kim Harrison
Published: August 2024

Delivering a contrast (or therapeutic) agent to a subject inside an MRI bore requires an expensive electronically programmed syringe pump and a long tube filled with the agent. We developed an inexpensive 3D-printed MRI-compatible syringe pump that can be used inside an MRI bore. This syringe pump yields a highly reproducible infusion rate, which can be readily adjusted using gears with different gear ratios.
Open-Bore Hybrid MPI-MRI Scanner: A Proof-of-Concept Simulation Study
Authors: Karaca Sefa , Soydan Damla Alptekin , Saritas Emine , Top Can
Published: August 2024

Magnetic particle imaging (MPI) is a novel technique that images background-free spatial distribution of magnetic nanoparticles inside the body. The lack of signal from the background tissue provides high sensitivity to MPI. However, anatomical information is also required in many applications. Here, we present an open-bore hybrid preclinical MPI-MRI system, in which the coils can be utilized interchangeably between MPI and MRI data acquisitions. We analyze the imaging performance for both modalities using a simulation model based on our in-house prototype MPI system that can generate 0.5 T/m selection field gradients for MPI and 50 mT B0 for low-field MRI.
Characterizing gradient performance and estimating Maxwell fields at 0.55T.
Authors: West Daniel , Leitão David , Tomi-Tricot Raphael , Wood Tobias , Hajnal Jo , Malik Shaihan
Published: August 2024

In this work we use a general sequence to characterize gradient imperfections at low field (0.55T) by measuring the gradient impulse response function (GIRF). Maxwell fields become non-negligible at lower field strengths and so we incorporate these into our GIRF calculation to form a single processing pipeline. Once the predictive ability of the GIRF was confirmed, we use our data to estimate the Maxwell phase and compare this to an analytic approach; good agreement is observed. These results will inform future low-field investigations and enable an improved image quality for sequences that are particularly prone to gradient imperfections.
Diffusion Weighted Imaging using a Prostate Nonlinear Gradient Coil
Authors: Elsaid Nahla , Nixon Terence , Dewdney Andrew , De Simone Andrea , Fettahoglu Ates , Galiana Gigi
Published: August 2024

This work presents the early performance characteristics of an inside-out prostate nonlinear gradient (NLG). Diffusion-weighted imaging (DWI) could detect prostate cancers non-invasively. However, it requires high b-values, which in turn require a gradient pulse with a long duration, increasing the echo time. A longer echo time means a lower SNR and lower contribution from short T2 components. NLG coil circumvents this issue by having a gradient with a high amplitude within a limited field of view, which applies to prostate imaging.
Slice-wise z-shimming for spinal cord fMRI with Shimming Toolbox
Authors: D'Astous Alexandre , Kaptan Merve , Papp Daniel , Dabbagh Alice , Finsterbusch Jürgen , Eippert Falk , Alonso-Ortiz Eva , Cohen-Adad Julien
Published: August 2024

Spinal cord fMRI is challenging, in part due to B0 inhomogeneities. An EPI, z-shimming signal intensity based approach was implemented to automatically and dynamically shim the spinal cord. The technique was implemented in the open-source Shimming Toolbox. Z-shimming was tested on a healthy participant and showed a 14% increase in signal intensity as well as 19% increase in tSNR. The technique was generalized to allow for the use of any shim coil geometry as well as non-EPI data.
Compact 7T: Progress in Construction and Assembly of a Low-Cryogen, High-Performance, Head-only High-Field MRI System
Authors: Foo Thomas , Vermilyea Mark , Conte Gene , Van Epps Chris , Ricci Justin , Xu Minfeng , Wu Anbo , Huang Shike , Lee Seung-Kyun , Stautner Wolfgang , Ye Bai , Kelley Doug , Huston John , Bernstein Matt , Shu Yunhong , Hess Christopher , Xu Duan
Published: August 2024

Steps in the construction of an 8 metric ton, 7.0 T MRI scanner for imaging the brain are described. The system nearing completion uses only 12 liters of liquid helium and has outside dimensions comparable to a clinical whole-body 3.0 T scanner, with a similar footprint. As such, the new Compact 7.0 T system fits into any whole-body 3.0 T room, simplifying installation. The system is in the final stages of assembly.
Improved multi-channel pTx B1+ mapping in the head and neck at 7T by combining RF shims and transmit voltages
Authors: de Buck Matthijs , Jezzard Peter , Hess Aaron
Published: August 2024

Acquiring robust multi-channel B1+ maps in both the head and neck using pTx head coils at 7T can be challenging due to the combination of low RF penetration into the neck, the limited dynamic range of multi-channel B1+ mapping techniques, and B0 sensitivity. We present a pipeline for large dynamic range pTx field mapping along with a publicly available 10-subject database of head and neck field maps for a commonly used multi-channel head transmit coil. The pipeline is evaluated by comparing the predicted B1+ to the measured B1+ for random transmit shim configurations in the head and neck.
Susceptibility-weighted imaging (SWI) of the kidney at 5T – initial results
Authors: Zheng Liyun , Yang Chun , Dai Yongming , Zeng Mengsu
Published: August 2024

At high magnetic field strengths (B0 ≥ 3 T), susceptibility-weighted imaging (SWI) is an appealing approach for increased signal-to-noise ratio (SNR). To our knowledge, implementation of SWI on the kidney at higher field MRI (> 3T) has not been attempted yet. This initial study proved that it is possible to obtain SWI at 5T for renal with sufficient image quality. For 5T SWI, SNR of cortex and medulla and CNR of cortex/medulla were significantly higher than at 3T, leading to improved vessel conspicuity and corticomedullary discrimination.
A Comparison Between Shielded and Unshielded RF Antennas for 7T Body MRI
Authors: Haluptzok Tobey , Schmidt Simon , Lagore Russell , Metzger Gregory
Published: August 2024

To more fully realize the benefit of UHF MRI, coil designs must be optimized to for pTx and intrinsic SNR performance. In this abstract we compare 5 RF coil array block systems. The first comparison ran is pTx performance, the second comparison is of intrinsic array SNR, and the third tests robustness to top loading. The shielded and unshielded three-loop dipole elements performed better than the single-loop dipole and the shieled helped with top load insensitivity. From these results we conclude that the three-loop dipole coil blocks have superior performance and that the shield doesn’t hurt performance.
Estimation of the Spatial Gradient of the MR Image from the Diffusion Profile
Authors: Aganj Iman , Vera Matthew , Feiweier Thorsten , van der Kouwe Andre , Kirsch John , Fischl Bruce
Published: August 2024

In the course of diffusion, water molecules experience varying values for the relaxation-time property of the underlying tissue, a factor that has not been accounted for in diffusion MRI (dMRI) modeling. Accordingly, we derive a relationship between the diffusion profile measured by dMRI and the spatial gradient of the image, and subsequently estimate the latter from the former. We test our hypothesized relationship via dMRI of the human brain (a public in vivo image and an acquired ex vivo stimulated-echo image), showing statistically significant results that may be due to our model and/or the confounding factor of “fiber continuity”.
Impact of free-water correction on white matter changes measured by diffusion tensor imaging in migraine
Authors: Guadilla Irene , Fouto Ana , Planchuelo-Gómez Álvaro , Tristán-Vega Antonio , Ruiz-Tagle Amparo , Esteves Inês , Caetano Gina , Silva Nuno , Vilela Pedro , Gil-Gouveia Raquel , Aja-Fernández Santiago , Figueiredo Patrícia , Nunes Rita
Published: August 2024

Menstrual migraine affects about 25% of female migraine patients. However, the diagnosis of migraine is particularly difficult because the brain changes associated with migraine are challenging to detect with imaging techniques. Diffusion-weighted MRI (dMRI) permits the detection of alterations in the microenvironment of the brain tissues. We investigate whether removing the contribution of the free water component from the diffusion-signal can provide increased sensitivity to identify white matter changes in migraine using diffusion tensor metrics.
Self-Supervised Model Fitting of VERDICT MRI in the Prostate
Authors: Sen Snigdha , Singh Saurabh , Pye Hayley , Moore Caroline , Whitaker Hayley , Punwani Shonit , Atkinson David , Panagiotaki Eleftheria , Slator Paddy
Published: August 2024

Microstructure models are traditionally fitted via computationally expensive non-linear least squares. Recent model fitting techniques use supervised deep learning algorithms trained on synthetic datasets, however the training data distribution affects parameter estimates. Self-supervised learning can address this by extracting training labels directly from the input data. We introduce a self-supervised machine learning algorithm for fitting the VERDICT MRI model for prostate to diffusion-weighted MRI. On simulated data, our approach improves parameter estimation compared to non-linear least squares and supervised machine learning. We also reveal plausible tumour contrast on in-vivo prostate data.
Denoising Magnetic Resonance Spectroscopy Signals with Stack Auto-encoder Networks
Authors: Wang Jing , Ji Bing , Lei Yang , Liu Tian , Mao Hui , Yang Xiaofeng
Published: August 2024

This abstract reports a novel and efficient self-supervised deep learning autoencoder network for denoising MRS data and improving signal-to-noise ratio (SNR) of spectra, which may enable rapid MRS data acquisition and improving its clinical workflow and applications.
Cluster Based Sparse Variational Minimization for Multi-Compartment Dictionary Fitting to bSSFP Signal Profiles
Authors: Acikgoz Berk , Mackowiak Adèle , Plähn Nils , Safarkhanloo Yasaman , Peper Eva , Hilbert Tom , Rossi Giulia , Bastiaansen Jessica
Published: August 2024

A novel cluster-based sparse total-variation minimization (CSTV) dictionary matching algorithm for multi-compartment parameter mapping applied to a phase-cycled balanced steady state free precession (PC-bSSFP) signal profile is presented. A dictionary encoding off-resonance frequency and relaxation time ratio is matched to the measured profiles with the proposed method to recover the underlying fat fraction, proton density, and water-fat-separated images simultaneously. The performance of the proposed method is tested and compared with a Laplacian regularized non-negative least squares dictionary matching method in phantom and in vivo experiments.
Non-Linear Reconstruction for Coil Sensitivity Calibration from Cartesian and non-Cartesian Data
Authors: Holme H. Christian M. , Uecker Martin
Published: August 2024

We compare the current de-facto standard for coil sensitivity calculation, ESPIRiT, to a non-linear reconstruction method, ENLIVE. While ENLIVE normally produces both images and coil sensitivies, we focus here on using it for calibration of the coil sensitivities. We applied ESPIRiT and ENLIVE to low-resolution subsets of a 3D Cartesian and a non-Cartesian spiral acquisition, using the resoluting coil profiles in a linear parallel imaging reconstruction. We showed that ENLIVE is substantially faster for a high number of channels and provides improved sensitivities in the non-Cartesian example.
Joint K-b space reconstruction of under-sampled diffusion-weighted MRI
Authors: Dai Yan , Deng Jie , Jia Xun
Published: August 2024

We developed a joint image reconstruction method in both k-space and b-space to reconstruct the under-sampled diffusion-weighted images acquired at different b-values and generate the corresponding apparent diffusion coefficient map simultaneously by solving an optimization problem. This method improved SNR in both diffusion weighted images and apparent diffusion coefficient map compared with the conventional method and allows 50% k-space data undersampling that has the potential of reducing image distortion and shortening acquisition time.
GRAPPA Interpolation with Unity Acceleration Enhances Signal-to-Noise Ratio in Fully Sampled Acquisitions
Authors: Nencka Andrew
Published: August 2024

Significant efforts are focused on maximizing SNR in high spatial and temporal resolution image acquisitions. We present a method of utilizing GRAPPA to de-noise k-space observations made with a multi-channel array. By replacing a fully sampled observed k-space with a k-space in which each observation is inferred from a weighted average of neighboring observations across all coils, an increase of SNR on the order of 5% is achieved.
New Theory and Faster Computations for Subspace-Based Sensitivity Map Estimation
Authors: Lobos Rodrigo , Chan Chin-Cheng , Haldar Justin
Published: August 2024

Sensitivity map estimation is important in many multichannel MRI applications. Subspace-based sensitivity map estimation methods like ESPIRiT are popular and perform well, though can be computationally expensive and their theoretical principles can be nontrivial to understand. In this work, we derive a new theoretical framework for sensitivity map estimation from a structured low-rank modeling perspective. This results in an estimation approach that is equivalent to ESPIRiT, but with theory that may be more intuitive for some readers. In addition, we propose a set of computational acceleration techniques that enable substantial (~25-fold) improvements in computational time for subspace-based sensitivity map estimation.
Cramér-Rao Bound Optimized Linear Bases for Low-Rank Subspace Reconstruction
Authors: Mao Andrew , Flassbeck Sebastian , Gultekin Cem , Asslaender Jakob
Published: August 2024

This works extends the traditional framework for estimating low-rank bases that maximize preserved signal energy to additionally preserve the Cramér-Rao bound of the biophysical parameters in quantitative imaging. To this end, we orthogonalize the signal's derivatives wrt. the model parameters and incorporate them into the basis estimation process. We demonstrate in silico an improvement in the Cramér-Rao bound of all biophysical parameters with negligible cost to signal energy preservation, which translates to improved image quality and SNR in vivo.
The Problem of Hidden Noise in MR Image Reconstruction
Authors: Wang Jiayang , An Di , Haldar Justin
Published: August 2024

The performance of modern image reconstruction methods is commonly judged using quantitative error metrics like mean squared-error and the structural similarity index, where these error metrics are calculated by comparing a reconstruction against fully-sampled reference data. In practice, this reference data contains noise and is not a true gold standard. In this work, we demonstrate that this “hidden noise” can confound performance assessment methods, leading to image quality degradations when typical error metrics are used to tune image reconstruction performance. We also demonstrate that a new error metric, based on the non-central chi distribution, helps resolve this issue.
K-space Based Motion Estimation for polar fMRI using Transfer learning
Authors: makhsousi Faeze , Ghodrati Vahid , Homayounfar Morteza , ghaffarzadeh sina , Nasiraei-Moghaddam abbas
Published: August 2024

The motion of the head during functional MRI is an unavoidable issue that adversely affects brain mapping. Radial reading of the k-space reduces the problem to some extent but not completely. Residual motion, even at a partial pixel level, has a measurable effect on the spatial frequencies and so can be estimated directly from the k-space data. This work uses a transfer learning-based approach to estimate the head motion from radially acquired k-space information. Results showed a good agreement with the statistical parametric mapping (SPM) package.
Comparison of coil combination technique performance for phase preservation
Authors: Balaji Krithika , Lally Peter , Huo Zimu , Mendoza Michael , Hoff Michael , Bangerter Neal
Published: August 2024

MRI images are typically acquired using multiple receive coils, which introduce spatially varying phase offsets to each coil image. Many techniques have been developed to combine these coil images. For a variety of reconstruction techniques, phase preservation is necessary post-combination but it is unclear which method best achieves this. This work compared ESPIRiT, Simple Phase RCC, Full Phase RCC, 3T Siemens’ Adaptive Combine and IMPA using phase-cycled bSSFP images. Phase preservation was evaluated using the elliptical signal model theory and characteristic bSSFP phase plots. ESPIRiT consistently produced combined images with phase characteristics most similar to those from a single coil.
FSE Dixon with bandwidth-matched asymmetric readouts tailored for real-valued fat/water estimates
Authors: Rydén Henric , Skorpil Mikael , Borbas Matea , van Niekerk Adam
Published: August 2024

A novel asymmetric readout waveform for Dixon FSE imaging is presented for efficient sampling, tailored for real-valued estimation of fat/water estimates.
SuperMRF: Deep Robust Acceleration for MR Fingerprinting
Authors: Li Hongyu , Eck Brendan L. , Yang Mingrui , Kim Jeehun , Liu Ruiying , Huang Peizhou , Liang Dong , Li Xiaojuan , Ying Leslie
Published: August 2024

We propose a novel, deep learning-based method, “SuperMRF”, for the reconstruction of MR Fingerprinting (MRF) parametric maps that enables rapid image reconstruction. Built upon a convolutional neural network, SuperMRF uses three loss functions to incorporate additional information from the Bloch equations, estimated maps, de-aliasing, and data consistency losses. We investigate the use of SuperMRF for further acceleration of data acquisition by reducing the number of MRF time frames. Our results demonstrate that proposed SuperMRF is robust to noise and can achieve a 20x reduction in acquired MRF time frames. Tissue property maps can be reconstructed in less than one second.
The effect of acceleration factor on brain magnetic resonance imaging based on artificial intelligence compressed sensing technology
Authors: hu shuai , zhang haonan , wang nan , song qingwei , liu ailian
Published: August 2024

ACS based cranial MRI has good feasibility in clinical use. Scanning time is reduced by 37%, 40%, and 71% for recommended AF in T1-FLAIR, T2WI, and T2-FLAIR, while the image quality meets the requirement of diagnosis.
Effect of regularization parameter on model-based deep learning framework for accelerated MRI
Authors: Bhave Sampada , Sajib Saurav , Pramanik Aniket , Jacob Mathews , Sharma Samir
Published: August 2024

Model-based deep learning algorithms offer high quality reconstructions for accelerated acquisitions. Training the regularization parameter λ can lead to instabilities during training. In this work, we evaluated effect of fixing λ parameter while training. We observed no difference in image quality when the network was trained with a fixed λ parameter when the fixed value was equal to the value learned from training. We observed that IQ is dependent on the fixed λ values used during training. Furthermore, we observed that tuning the λ parameter during inference adapts the framework to the SNR of the testing dataset, yielding improved performance.
Acceleration of Phase-contrast Magnetic Resonance Venogram by Compressed SENSE
Authors: Zhang Yukun , Liu Na , Hu Geli , Lin Liangjie , Miao Yanwei , Song Qingwei
Published: August 2024

The clinical application of phase-contrast magnetic resonance venogram (PC-MRV) is limited by the long scan time. This study aims to accelerate the acquisition of PC-MRV by the compressed sensitivity encoding (CS-SENSE) technique and find the optimal acceleration factor. Results show that the image quality of fast PC-MRV based on CS-SENSE was significantly higher than that based on conventional sensitivity encoding (SENSE) technology, and the CS-SENSE factor 9 was recommended for mild to moderate patients, and CS AF=12 for critical patients.
Development of a self-supervised machine learning algorithm for automatic MRI sequence-type classification
Authors: Na Seongwon , Ko Yousun , Ham Su Jung , Kim Mi-Hyun , Shin Youngbin , Sung Yu Sub , Huh Jimi , Jung Seung Chai , Kim Kyung Won
Published: August 2024

For automatic sequence-type classification of brain MRI, we developed the self-supervised machine learning (ML) algorithm, named ImageSort-net, using a rule-based labeling system based on metadata of Digital Imaging and Communications in Medicine (DICOM) image files. Our rule-based labeling system and ImageSort-net showed high classification performance to predict brain MRI sequence type. ImageSort-net showed reliable performance by appending a new dataset to an existing dataset and without human labeling of the whole dataset. This result indicates that sustainable self-learning ML algorithms using the rule-based virtual label in the new datasets are feasible.
Same-day Comparison of 129Xe MRI Ventilation Across Two MRI Manufacturers
Authors: Eddy Rachel , Diamond Vanessa , Bhatnagar Girija , Schmidt Alexandra , Leipsic Jonathon , Sin Don , Quon Bradley , Rayment Jonathan
Published: August 2024

Hyperpolarized 129Xe gas MRI (XeMRI) is gaining traction globally as a primary endpoint in clinical trials, however it is unknown how images and measurements directly compare across different MRI scanner platforms. Here we compared same-day ventilation XeMRI across two MRI manufacturers at two sites in Vancouver Canada in patients with cystic fibrosis and healthy controls, to determine the interoperability across MRI platforms. Between-site ventilation images showed strong spatial overlap, and quantitative ventilation defect percent measurements showed strong agreement across all participants. These results support the use of XeMRI in multi-center studies irrespective of MRI platform.
Cine magnetic resonance images: relationship to therapeutic effect evaluation and prognosis in light-chain amyloidosis
Authors: Wang Keyan , Zheng Jie , An Jing , Cheng Jingliang
Published: August 2024

The study evaluated left ventricular (LV), right ventricular (RV), and left atrial (LA) myocardial strain and heart function derived from cine magnetic resonance (MR) imaging for chemotherapeutic efficacy and predicted their prognostic value in patients with light-chain (AL) amyloidosis. Our results demonstrated that these parameters may be noninvasive imaging markers allowing assessment of the chemotherapeutic effect and may offer independent prognostic information for all-cause mortality in patients with AL amyloidosis.
Using cardiac magnetic resonance imaging to assess advantages of cardiac-gated radioablation
Authors: Poon Justin , Thompson Richard , Deyell Marc , Schellenberg Devin , Kohli Kirpal , Thomas Steven
Published: August 2024

Cardiac magnetic resonance (CMR) images and tracked left ventricle (LV) contour points were used to investigate the advantages of a cardiac-gated radioablation treatment for ventricular tachycardia. Contour points were divided into 17 segments of the LV. Individual segments were treated as hypothetical targets, and an optimal treatment window was determined where cardiac motion was minimal. Target centroid displacement and treatment area were quantified for the treatment window and the full cardiac cycle. Using a cardiac-gated radioablation treatment would allow for reduced treatment margins and minimize the dose delivered to surrounding normal tissue.
Entropy as a Novel Predictor of Cardiovascular Events in Patients with Left Ventricular Noncompaction
Authors: Gao Yan , Liu Mengxiao , Ju Zhiguo , Wang Ximing
Published: August 2024

The prognosis of LVNC is remarkably heterogeneous, with heart failure (HF), ventricular arrhythmias (VAs) and systemic embolisms (SEs) being the most frequent cardiovascular complications but no specific recommendations are available at present. In the present study, we aimed to investigate whether LV entropy could efficiently predict major adverse cardiac events (MACEs) in patients with LVNC incremental to established clinical and imaging risk markers.
23Na Triple Quantum (TQ) Signal Estimation from Single-Pulse Sequence with Single Quantum (SQ) Time Efficiency
Authors: Reichert Simon , Schepkin Victor , Kleimaier Dennis , Schad Lothar
Published: August 2024

This study demonstrates a method to estimate the biological relevant sodium TQ signal with a single-pulse sequence. Comparison of this method with theory and experiments using TQTPPI sequence showed a close agreement of the TQ signal. Therefore, the TQ signal can be estimated on the time scale of sodium SQ signal acquisition using the proposed sequence.
Skin Sodium Concentration Measurement Increased with Surface-Coils, Higher Resolution Voxel Reshaping, and Lower Flip-Angles
Authors: Zhu Jingxuan , Beaulieu Christian , Damji Karim , Stobbe Rob
Published: August 2024

Non-proton (23Na) MRI studies have measured greater body tissue (skin) sodium concentration in disorders such as hypertension, diabetes, and chronic kidney disease, but literature values of ~15 mM are well below the 77 mM from atomic absorption spectroscopy. Previous studies have used gradient-echo imaging with long TE=2 ms, knee volume-coils, and large 3×3×30=270 mm3 voxels. Here, short TE=0.1 ms twisted projection imaging is used, and signal loss is progressively lowered with surface coil imaging, reshaped 0.4x4x4 = 6.4 mm3 voxels, and lower 60o flip-angle excitation, ultimately yielding 42 mM calf skin measurement in 10 young adults.
Optimisation of Simultaneously Acquired Single and Triple Quantum Filtered Sodium MRI for Relaxometry at 7T
Authors: Worthoff Wieland , Chen Qingping , Shah N. Jon
Published: August 2024

Sodium relaxometry has great potential value for research and clinical practice. Enhanced Simultaneously Acquired Single and Triple Quantum Filtered Imaging of Sodium (eSISTINA) can yield information on both fast and slow transversal relaxation times. The eSISTINA sequence is optimised by adding an early echo in the multiple-quantum-filtered readout train using FLORET k-space trajectories. The optimised sequence is compared and verified against a conventional, Cartesian multi-echo gradient echo based enhanced eSISTINA sequence on a group of four healthy volunteers. The experimental results show that the optimised sequence has better in vivo relaxometry performance than the conventional sequence.