bluuebunny/crossref_metadata_2025 huggingface dataset:
Perturbation Robust: Deep Adversarial-Equilibrium Unfolding Network for Magnetic Resonance Image Reconstruction
Authors: Zhou Tian , Cui Zhuoxu , Shang Kun , Liang Dong
Published: November 2024

Motivation: Deep unfolding neural networks had attained great success in solving tasks of magnetic resonance image (MRI) reconstruction. Goal(s): However, minor perturbation in MR signals can result in significant distortions such as some artifacts of the reconstructed images via previous deep unfolding methods. Approach: This paper proposes a deep equilibrium unfolding network based on adversarial learning to improve robustness of unfolding networks. Results: Experiment results demonstrate that the proposed method obtains better reconstructed MR images compared with baseline-networks when some artifacts exist in under-sampled multi-channel k-space data. Impact: We propose a robust method for MRI reconstruction against artefacts in k-space data.
Detail-preserving self-supervised federated learning for undersamped MR image reconstruction
Authors: Zou Juan , Li Cheng , Wu Ruoyou , Yang Jing , Fan Wenxin , Wang Shanshan
Published: November 2024

Motivation: Self-supervised learning-based MR image reconstruction learn a prior on the data distribution, but data in medical imaging settings are highly diverse, which consists of different corruptions or degradation factors. Existing methods need improvement in preserving details for undersampled image reconstruction with different degradation factors. Goal(s): Our goal is to preserve details for undersampled image reconstruction with different degradation factors. Approach: A detail-preserving self-supervised federated learning method is proposed to preserve details by employing personalized federated models to refine undersampled training data iteratively. Results: Experiments show that promising results are achieved by proposed method, and details are preserved and refined for undersampled image reconstruction. Impact: Detail-preserving self-supervised federated learning method can effectively preserve more details compared to self-supervised learning methods.
Low-rank regularized implicit neural representation for k-space completion in fast MRI reconstruction
Authors: Lao Guoyan , Feng Ruimin , Zhang Yuyao , Wei Hongjiang
Published: November 2024

Motivation: The highly reduced k-space measurements would induce noises and artifacts in the reconstructed image in parallel imaging. Goal(s): To effectively complete the undersampled k-space points for MRI acceleration and provide high-quality images. Approach: We developed a novel k-space completion framework based on implicit neural representation. The inherent low-rankness of k-space is incorporated into the model to capture the continuous representation in k-space. The proposed method was evaluated on the public dataset and compared with the image and k-space domain reconstruction methods. Results: The results show that our method can effectively complete the undersampled k-space points without any priors in the image domain. Impact: The proposed method leverages implicit neural representation in the k-space reconstruction, demonstrating the ability to complete the undersampled k-space points at high acceleration factor. This result implies our method can further reduce the measured k-space points and accelerate MRI acquisition.
Tumor-induced metabolic reprogramming and CD8+ T-cell exhaustion of the spleen
Authors: Barnett James , Penet Marie-France , Sharma Raj Kumar , Stanczak Michal , Yousf Saleem , Krishnamachary Balaji , Mironchik Yelena , Pearce Erika , Bhujwalla Zaver
Published: November 2024

Motivation: Metastasis is the leading cause of cancer-related mortality worldwide. We must pave new avenues for cancer treatment by interrogating the pro-tumorigenic properties of the tumor macroenvironment. Goal(s): We seek to investigate how tumorigenesis metabolically impacts the spleen microenvironment and how it contributes to immune evasion. Approach: Proton magnetic resonance spectroscopy was used to identify aqueous spleen metabolites during tumorigenesis. Flow cytometric analyses were conducted to immunophenotype splenic CD8+ T cells and to quantify MDSC and T-cell frequencies. Results: Tumorigenesis induced common, distinct metabolite changes in mouse spleens. Flow cytometric analyses revealed splenic CD8-T-cell exhaustion and reduced cytotoxic T-cell effector function. Impact: Tumors drive metabolic spleen alterations that may contribute to reduced CD8+ T cells and their exhaustion even before reaching the tumor, contributing to immune suppression and poor prognosis. This may provide metabolism-targeted strategies to improve immune surveillance and immunotherapy.
Quantitative MRI Measurements Record Pancreatic Cancer and Stroma Reactions to New KRAS Inhibitor
Authors: Gupta Mamta , Choi Hoon , Kemp Samantha , Furth Emma , Joaquim Miguel , Pickup Stephen , Clendenin Cynthia , Orlen Margo , Karasic Thomas , Song Hee , Fan Yong , Dwyer Peter O’ , Vonderheide Robert , Rosen Mark , Stanger Ben , Zhou Rong
Published: November 2024

Motivation: KRAS mutations occur in 90% of pancreatic ductal adenocarcinoma(PDA) with G12Dmutation being the most common.Recent KRAS(G12D) inhibitors have unraveled an exciting therapeutic opportunity for this deadly cancer,as they are being tested in clinical trials. Goal(s): However, the prior use of KRAS(G12C)inhibitors in lung cancer treatment showed mere 50% patient response,despite the accurate genetic mutation,calling for biomarkers which can assess the drug-target engagement early on and predict treatment outcome. Approach: To test the utility of translational MRI markers in a clinically relevant PDA model for early responses to KRAS(G12D) inhibitor,MRTX1133. Results: ADC,Ktrans and MTR captured MRTX1133 induced early cancer cell death and stroma change. Impact: Our study in genetically engineered mouse model of pancreatic cancer supports that clinical translatable MRI metrics (ADC, Ktrans and MTR) are promising for capturing early pharmacodynamic responses to KRAS inhibitor MRTX1133.
Prediction model based on MRI morphological features for distinguishing benign and malignant thyroid nodules
Authors: Zheng Tingting , Song Bin
Published: November 2024

Motivation: The low specificity of many Thyroid Imaging Reporting and Data Systems (TI-RADSs) lead to a large number of unnecessary biopsies. Goal(s): This study developed and validated a predictive model based on MRI morphological features to improve the specificity. Approach: A retrospective analysis was conducted on 825 thyroid nodules pathologically confirmed postoperatively. Univariate and multivariate logistic regression was used to obtain β coefficients, construct predictive models and nomogram incorporating MRI morphological features in the training cohort, and validated in a validation cohort. Results: Compared with the TI-RADSs, predictive models have better specificity along with a high sensitivity and can reduce unnecessary biopsies. Impact: predictive models have better specificity along with a high sensitivity and may avoid numerous invasive needle biopsies
Comparison of reduced field-of-view DWI and conventional DWI in the assessment of lumbar bone marrow infiltration in patients with acute leukemia
Authors: Bian Wenjin , Niu Jinliang , Li Jianting , Wang Luyao
Published: November 2024

Motivation: To investigate whether reduced field-of-view diffusion-weight imaging (r-FOV DWI) is superior to conventional DWI for the evaluation of bone marrow infiltration in acute leukemia (AL). Goal(s): To compare the imaging quality, apparent diffusion coefficient, and value of assessing bone marrow infiltration between r-FOV DWI and conventional DWI in the lumbar spine of AL. Approach: The image quality and the performance in assessing bone marrow infiltration of the two sequences in AL were compared qualitatively and quantitatively. Results: Compared with conventional DWI, r-FOV DWI provides superior image quality of the lumbar spine in AL patients, thus yielding better performance in assessing bone marrow infiltration. Impact: The findings suggest that r-FOV DWI is a promising technique for reducing image artifacts, improving image quality, and assessing bone marrow infiltration in AL. r-FOV DWI can be proposed as a common sequence for evaluating bone marrow infiltration in AL.
Score-based Diffusion Models with Self-supervised Learning for Accelerated 3D Multi-contrast Cardiac MR Imaging
Authors: Liu Yuanyuan , Cui Zhuo-Xu , Liu Congcong , Zhu Qingyong , Cheng Jing , Liang Dong , Zhu Yanjie
Published: November 2024

Motivation: Long scan time significantly hinders the widespread applications of three-dimensional multi-contrast cardiac magnetic resonance (3D-MC-CMR) imaging. Goal(s): This study aims to accelerate 3D-MC-CMR acquisition by a novel method based on score-based diffusion models with self-supervised learning. Approach: We first establish a mapping between the undersampled k-space measurements and the MR images, utilizing a self-supervised Bayesian reconstruction network. Secondly, we develop a joint score-based diffusion model on 3D-MC-CMR images to capture their inherent distribution. The 3D-MC-CMR images are finally reconstructed using the conditioned Langenvin Markov chain Monte Carlo (MCMC) sampling. Results: This approach enables accurate reconstruction with high acceleration rates up to 14. Impact: The proposed method is trained in a self-supervised manner and therefore particularly suited for 3D CMR imaging that lacks fully sampled data.
Accelerated T1rho mapping for knee cartilage Using Denoising Diffusion Probabilistic Model (DDPM)
Authors: Liu Ruiying , Zhang Zhiyuan , Huang Peizhou , Kim Jee Hun , Li Xiaojuan , Ying Leslie
Published: November 2024

Motivation: Deep learning methods have achieved superior reconstruction in MR quantitative T1ρ imaging due to their ability to learn the non-linearity relationship between the undersampled k-space data and corresponding quantitative maps. Goal(s): In this study, we investigate the use of DDPM for highly accelerated T1ρ imaging. Approach: The DDPM learns the image properties from fully acquired images during training without the knowledge of the subsampling patterns used for the accelerated scans. This is advantageous to most existing models that need to be retrained every time for a new sampling scheme. Results: Our results demonstrate that DDPM can achieve superior T1ρ-weighted images and T1ρ map. Impact: The proposed DDPM can achieve superior T1ρ map then compressed sensing and other learning methods.
Quantitative differences in the heart habitus between breath-hold and free-breathing states, measured using a 3D survey in cardiac patients
Authors: Vineeth VS , Raj Vimal , Mattathodi Razeem Ali , John Jeenamol , E Gomathi Vengatesan , Edavana Sasi , Narayanan Prasad , Neelavalli Jaladhar
Published: November 2024

Motivation: Quantitative understanding of the differences in the heart’s habitus (position, orientation and size) between free-breathing (FB) and breath-hold (BH) mode of cardiac MR imaging (CMR) is lacking. Goal(s): To quantitatively measure the differences in the heart’s position, orientation and size between free-breathing and breath-hold mode in cardiac patientsApproach: Used an accelerated 3D GRE survey, acquired in BH (cardiac gated) and FB modes (cardiac and respiratory gated) in a large cohort of cardiac patients and quantified the location, orientation and size differences. Results: We find that quantitatively heart’s habitus differs significantly, in all three factors, between the two modes of imaging. Impact: We have, for the first time, quantified the extent to which the heart’s habitus (position, orientation and size) changes between breath-hold and free-breathing modes of Cardiac MRI (CMR). CMRI planning, whether manual or AI based, needs to account for this.
Translating 3D stack-of-spirals lung water MRI from 0.55T to 1.5T
Authors: Seemann Felicia , Javed Ahsan , Fyrdahl Alexander , Morgan Eric , Benton Charles , Ramasawmy Rajiv , Weissman Gaby , Carlsson Marcus , Campbell-Washburn Adrienne
Published: November 2024

Motivation: Our recently developed 0.55T MRI sequence to measure lung water dynamics has clinical utility in the evaluation of heart failure, but cardiac MRI is more widely performed at 1.5T. Goal(s): To translate a 3D stack-of-spirals lung water MRI sequence from 0.55T to 1.5T. Approach: We optimized sequence parameters through Bloch equation simulation, phantom experiments, and in vivo imaging in 10 healthy volunteers, acquired at two different centers. Results: The sequence parameters TE/TR/FA/readout duration=0.70ms/9.0ms/1°/1.5ms at 1.5T yielded proton density weighted images with apparent SNR 13.5±2.2, limited image blur, and quantified a lung water density 20±3.2%. Impact: Lung water quantification has emerged as a promising method to monitor and predict outcomes in heart failure. Translation of a lung water MRI sequence from 0.55T to 1.5T enables a more widespread adoption of this tool.
3D Quantitative BOLD MRI in Patients with Unilateral Carotid Occlusion
Authors: Dennison Jeffrey , Langham Michael , Christopher Favilla , Lee Hyunyeol , Wehrli Felix
Published: November 2024

Motivation: Recently developed constrained quantitative BOLD (c-qBOLD) offers the ability to quantify local cerebral metabolic rate of oxygen (CMRO2). However, it has yet to be applied in a clinical setting. Goal(s): By imaging patients with carotid occlusions we expect to observe specific disruptions to cerebral blood flow (CBF) and oxygen extraction fraction (OEF) i.e. neurometabolism. Approach: Patients (N=4) presenting with unilateral carotid occlusions were examined with a 3D constrained qBOLD protocol to map CBF, OEF, and cerebral blood volume, allowing for calculations of local CMRO2. Results: Data show both global and hemispheric differences in oxygen metabolism compared to healthy subjects. Impact: The novel c-qBOLD protocol has potential clinical utility as the degree and character of metabolic dysfunction, but can be performed quickly, offers fine spatial resolution (5.8mm3), and avoids uncomfortable calibration procedures.
Revolutionizing 3T Animal Imaging: Metamaterials' Advantages in Enhancing Contrast
Authors: Wu Shiman , Li Qing , Zhang Qi , Fu Caixia , Wu Yue , Yao Zhenwei
Published: November 2024

Motivation: To achieve high-quality animal imaging, metamaterials can offer an array of electromagnetic properties that can enhance 3T MRI by improving signal-to-noise ratio (SNR), image quality, and reducd scan times. Goal(s): Achieve superior imaging quality in 3T MRI by implementing metamaterial coils, thereby improving animal imaging capabilities. Approach: Utilized metamaterials on a 3T MRI to scan rats and mice, comparing image resolution, SNR, and CNR against traditional coils. Images were assessed by radiologists for quality. Results: Metamaterial coils provided markedly better resolution and contrast, with significantly higher SNR and shorter scan times, enabling clearer anatomical detail. Impact: The study's findings pave the way for enhanced diagnostic precision in veterinary and preclinical studies, raise questions about broader applications in small animal research.
Line-Scan Gradient Echo Sampling of Turbo Spin Echo (GESTSE) Enables Quantitative Relaxometry in a “Virtual Biopsy” with 150 µm Resolution
Authors: Pandey Shraddha , Yao Michael , Balasubramanian Mukund , Tisdall M. Dylan
Published: November 2024

Motivation: Need for quantitative relaxometry at the spatial scale of cortical laminae to detect layer-specific pathology. Goal(s): Demonstrate feasibility of Line-Scanned Gradient Echo Sampling of Turbo Spin Echo (GESTSE)Approach: Novel 1D GESTSE sequence was developed, and data acquired in water phantom and pineapple at 7T. Data fitting algorithm was developed to accurately fit observed measurements and extract quantitative relaxometry data. Results: With specific gradient spoiling and phase cycling, and a fitting algorithm that accounts for system imperfections, transverse relaxation constants can be estimated at a 150 µm resolution along a 1D line-scan. Impact: Our novel line-scan GESTSE sequence and fitting algorithm enables accurate quantitative relaxometry at spatial scales sufficient to resolve human cortical laminae. This method offers a new avenue to efficiently detect layer-specific cortical structure and pathology in vivo.
Cardiac-incoherent sampling of transverse signal decay mitigates cardiac-induced noise in brain maps of R2*
Authors: Raynaud Quentin , Oliveira Rita , Yerly Jérôme , van Heeswijk Ruud B. , Lutti Antoine
Published: November 2024

Motivation: Robust measures of the relaxation rate R2* are essential for use in neuroscience studies. With standard acquisition techniques, data are sampled consecutively at multiple echo times at the same phase of the cardiac cycle. This coherent sampling leads to a high level of cardiac-induced noise in brain maps of R2*. Goal(s): We design a new data acquisition strategy that mitigates the impact of cardiac-induced noise in brain quantitative R2* maps. Approach: We incoherently sample cardiac-induced noise by shifting k-space location during the multi-echo acquisition. Results: Compared to standard techniques, this strategy reduces the variability of R2* estimates across repetitions by 30-40%. Impact: The proposed k-space shifting acquisition strategy reduces the level of cardiac-induced noise in brain maps of R2*. This will increase the sensitivity of brain change analyses in future neuroscience studies that include R2* mapping.
The Application of High Temporal Resolution Semi-quantitative Dynamic Contrast Enhanced MRI in Predicting Ki-67 Expression in Breast Cancer
Authors: Feng Wen , Lei Junqiang , Xiong Yuhui , Niu Yicong , Li Zhifan , Ma Qinqin , Wang Zihan
Published: November 2024

Motivation: It was hoped that a method can come under observation to capture the semi-quantitative hemodynamic characteristics of tumor enhancement, so as to provide richer and more accurate information for the early clinical diagnosis of breast cancer. Goal(s): To investigate the application value of semi-quantitative parameters of three enhanced sequences of MRI techniques in predicting Ki-67 expression in breast cancer. Approach: The semi-quantitative parameters of the enhanced images of the three groups were calculated respectively. The predictive parameters of Ki-67 expression in breast cancer were obtained by statistical analysis. Results: ROI1+54-per-brevity of enhancement was valuable for predicting the expression of Ki-67 in breast cancer(P=0.032). Impact: The DISCO-MRI with fast scanning speed and high time resolution needs to be further studied whether it can replace the traditional DCE-MRI scanning in the future, and it also needs to find a suitable post-processing mode.
Test-Retest Reliability, Agreement, and Bias of Deep Learning Based Reconstructions for PDFF and R2* Quantification
Authors: Do Hung , Starekova Jitka , Malis Vadim , Bae Won , Berkeley Dawn , Tymkiw Brian , AlGhuraibawi Wissam , Reeder Scott , Brittain Jean , Kadbi Mo , Hernando Diego
Published: November 2024

Motivation: Deep Learning Reconstruction (DLR) has been used routinely in clinical setting for qualitative weighted images. It is imperative to evaluate DLR for quantitative imaging prior to widespread clinical adoption. Goal(s): To assess the test-retest reliability of PDFF and R2* values calculated from DL-reconstructed images compared to those from the conventional reconstruction (CONV). Approach: A commercial PDFF/R2* phantom was imaged twice, with repositioning between acquisitions. Each scan was reconstructed with CONV and DLRs, which were used to calculate PDFF and R2* maps. Results: Excellent test-retest reliability for all three reconstructions with R2>0.99 and minimal bias (<0.58% for PDFF and <3.67 s-1 for R2*). Impact: SNR, resolution, and scan-time of quantitative MRI may benefit from DLR similarly as for qualitative MRI. This study showed that DLR has excellent test-retest reliability for PDFF/R2* quantification with minimal bias, providing foundational evidence for wider clinical adoption.
What does liver R2* mean when iron levels are not elevated? Understanding the variation in R1 and R2* in the healthy liver
Authors: Hales Patrick , Salvati Roberto , Robson Matthew , Fernandes Carolina
Published: November 2024

Motivation: The influence of liver iron concentration (LIC) on liver R1 and R2* has been well described. However, in healthy subjects, R1 and R2* may be predominantly influenced by liver water content (LWC). The expected variability in R1 and R2* due to changes in LWC is not well understood. Goal(s): Our goal was to model the expected variability in liver R1 and R2* due to changes in both LWC and LIC. Approach: We developed a model, and tested the predictions against measured R1/R2* values in healthy subjects. Results: The model predictions showed good agreement with the measured data in the majority of healthy subjects. Impact: This work explains the variability in liver R1 and R2* due to changes in liver water content (LWC) and iron. This could aid our interpretation of changes in these parameters in subjects where LWC is clinically relevant.
Improving Self-Gated Radial MR Elastography for Free-Breathing Quantification of Liver Stiffness in Children
Authors: Kafali Sevgi , Bolster Bradley , Shih Shu-Fu , Delgado Timoteo , Deshpande Vibhas , Zhong Xiaodong , Adamos Timothy , Ghahremani Shahnaz , Calkins Kara , Wu Holden
Published: November 2024

Motivation: MR elastography (MRE) detects liver fibrosis by measuring hepatic stiffness (HS). Breath-holding (BH) is required to avoid motion artifacts, but this is challenging in children. Previous radial free-breathing (FB) MRE eliminated the BH, but required 2 min/slice. Goal(s): To reduce scan time for radial FB-MRE and analyze the effects of self-gating on HS. Approach: Radial FB-MRE was modified to acquire lower resolution and perform spatial interpolation, which reduced the scan time. Self-gating was performed to compensate for breathing motion. Results: In 23 children, self-gated radial FB-MRE (1 min/slice) yielded more accurate and repeatable HS than non-self-gated radial FB-MRE, with respect to standard BH-MRE. Impact: Self-gated free-breathing MR elastography (MRE) of the liver produced accurate and repeatable hepatic stiffness in 1 min/slice, with respect to standard cartesian breath-held MRE, and can be beneficial for children who cannot perform breath-holding.
Assessing Renal Interstitial Fibrosis by Compartmental, Non-Compartmental, Model-Free Diffusion MRI and Their Corticomedullary Difference
Authors: Hu Wentao , Dai Yongming , Liu Fang , Wu Dongmei , Zhang Minfang , Zhou Yan
Published: November 2024

Motivation: Non-invasive evaluation of chronic kidney disease (CKD) is of increasing value. Goal(s): This study aims to assess renal interstitial fibrosis (IF) using diffusion-based MRI approached and their corticomedullary difference (CMD) in CKD patients. Approach: MRI parameters from apparent diffusion coefficient (ADC), intra-voxel incoherent motion (IVIM), diffusion kurtosis imaging (DKI) and diffusion-relaxation correlated spectrum imaging (DR-CSI) were obtained both for renal cortex and medullary. CMD of these parameters were calculated. Results: Several diffusion-based MRI parameters are promising for differentiating between the mild and moderate-severe renal IF. CMD shows potential for the assessment of renal IF on a supplementary role. Impact: This study shows attempts on non-invasive assessment of renal interstitial fibrosis using several typical advanced model or model-free methods regarding diffusion MRI. Also, it illustrates the possible role of renal corticomedullary difference of these approaches in the assessment.
Accelerated Abdominal 3D T1rho Mapping using Diamond Radial Sampling
Authors: Jogi Sandeep , Peng Qi , Jafari Ramin , Otazo Ricardo , Wu Can
Published: November 2024

Motivation: Current T1rho mapping of the abdomen is performed using breath-hold, respiratory triggering, or stack-of-stars acquisitions, which have limited spatial resolution and coverage or require a long scan time. Goal(s): To develop accelerated free-breathing 3D T1rho mapping technique for the abdomen using efficient diamond sampling. Approach: Free-breathing 3D T1rho imaging was performed on six volunteers using the MAPSS sequence. T1rho values were compared between stack-of-stars and diamond sampling. Results: The scan time was reduced from 5:24min per TSL for stack-of-stars to 2:26min per TSL for diamond sampling. The T1rho values obtained with both methods were comparable. Impact: The proposed free-breathing 3D T1rho mapping of the abdomen with accelerated diamond sampling has the potential to provide a quantitative assessment of abdominal lesions for improved diagnosis and treatment response evaluation.
Performance Study and Optimization of an MRI Motion-Compensated Reconstruction Program
Authors: Zeroual Mohamed Aziz , Isaieva Karyna , Vuissoz Pierre-André , Odille Freddy
Published: November 2024

Motivation: MRI motion-compensated reconstruction programs rely on several computationally intensive algorithms. For clinical use, they need to use efficiently the computational resources of compute nodes to achieve a good performance. Goal(s): Evaluate the performance of a program across architectures and optimize its execution without any code modification. Approach: We investigate the different parallelization paradigms. We use the roofline model, and the performance profiling tools to derive the architectural efficiency on CPU. A Matlab GPU implementation of the reconstruction kernel is used to draw comparisons. Results: The optimal parallel mapping reduces considerably the reconstruction time. Moreover, GPUs don’t outperform CPUs in every reconstruction problem. Impact: We demonstrate the importance of micro-architectural study in the overall behavior of MRI motion-compensated reconstruction codes. This study provides insights about how to select the most suitable architecture to a given code, depending on its hardware limitations.
Real-time Respiratory Correction of the Pilot Tone Cardiac Signal to Stabilize Triggering during Breath-holds
Authors: Pan Yue , Bacher Mario , Ahmad Rizwan , Simonetti Orlando , Speier Peter
Published: November 2024

Motivation: This research aims to refine Pilot Tone (PT) cardiac triggering, which can be impacted by changes in signal amplitude caused by breath-holding. Goal(s): Its primary goal is to develop and validate a real-time correction algorithm that improves the PT cardiac trigger stability during breath-holds without introducing additional variability during free breathing. Approach: The algorithm analyzes PT data for respiratory patterns to adapt beat-by-beat amplitude correction of the cardiac signal. The methodology was tested with different breathing paradigms at 1.5T and 3T to assess robustness. Results: The correction successfully adjusted PT cardiac signal amplitude, reducing missed triggers across respiratory patterns without increasing triggering variation. Impact: The described real-time correction improves the reliability of cardiac PT triggers during inspiratory breath-hold maneuvers, increasing the opportunities to replace ECG triggering with PT triggering, and therefore to eliminate the need for ECG leads.
Comparison of Motion Correction Schemes for Cardiac Diffusion Tensor Imaging Using Image Registration
Authors: Liu Yuchi , Kara Danielle , Garrett Thomas , Jin Ning , Speier Peter , Kwon Deborah , Bi Xiaoming , Nguyen Christopher
Published: November 2024

Motivation: Motion correction (MOCO) is critical for free-breathing cardiac DTI, but it is challenging due to low SNR and variations in b-value contrast in acquired images. Goal(s): To compare MOCO schemes for cardiac DTI with the ultimate goal of implementing the entire post-processing workflow inline on the scanner. Approach: Three image registration-based MOCO schemes were compared with a previous low rank tensor (LRT) method in 7 volunteers. Results: Compared with the LRT method, proposed MOCO methods achieved smaller standard deviations of MD and FA values, more smoothly varying helical structure in HA maps, and visually less motion of the heart in diffusion weighted images. Impact: A robust and fast motion correction approach for free-breathing cardiac diffusion tensor imaging (DTI) improves the quality of diffusion maps, and facilitates clinical translation of cardiac DTI.
Motion-insensitive multi-contrast intracranial vascular imaging:Feasibility of self-navigated motion-detection from iSNAP sequence
Authors: Chao Xiaoqian , Liu Lixin , Wu Peng , Han Lu , Wang He , Chen Zhensen
Published: November 2024

Motivation: The multi-contrast intracranial vascular imaging sequence iSNAP is less sensitive to motion due to the use of 3D radial readout, but image blurring and thus decreased visibility of distal small vessels may still occur in the case of large head motion. Goal(s): To evaluate the feasibility of estimating the continuous head motion from iSNAP k-space data under different temporal resolutions. Approach: Subsets of iSNAP's 3D radial k-space data were used to reconstruct a series of low-resolution images.Then the SPM toolbox was used to perform rigid registration,between the image volumes. Results: The temporal profiles of the motion parameters are well estimated. Impact: The results indicate the head motion can be well estimated from iSNAP. The estimated motion parameters can be used to correct the k-space data in the future, thus allowing reconstruction of motion-free iSNAP images.
MR Sequence Design in the Low Stochastic Regime - Development of a Low-SAR “T2-Weighted” Scan
Authors: Symms Mark , Grist James , McGovern Jeff , Tyler Damian
Published: November 2024

Motivation: The introduction of Machine Learning-based Image Reconstruction ("Deep Learning") offers a fresh opportunity to explore MR parameter space without the restrictive requirement to maximise MR signal. Goal(s): Demonstrate an application of the Low Stochastic Regime approach using low flip angle refocusing with good SNR and strong tissue contrast. Approach: Fast Spin Echo (FSE) images were acquired using reduced flip angle refocusing and extended echo train lengths, in combination with Deep Learning-Reconstruction (“DL-Recon”). Results: Using DL-Recon to effectively weaken the conventional constraint of maximising MR signal, the redesigned sequence produced images with lower RF power deposition but similar contrast to the product CPMG sequence. Impact: Clinical applications where T2-weighted imaging is SAR-limited.
Deep learning image formation for fast and high-resolution brain MRI at 0.055 tesla
Authors: Lau Vick , Man Christopher , Zhao Yujiao , Xiao Linfang , Leong Alex , Wu Ed
Published: November 2024

Motivation: High, isotropic resolution (e.g., 1mm) is desirable for lesion detection and biomarkers extraction for cognitive disorders. However, ultra-low-field (ULF) MRI severely suffers from low spatial resolution and signal-to-noise ratio. Goal(s): To investigate the potential of 3D deep learning in generating <=1mm isotropic resolution results from 2D partial Fourier-sampled, low-resolution noisy brain images acquired from our custom-made 0.055T scanner. Approach: We advanced 3D deep learning partial Fourier reconstruction and super-resolution method (PF-SR) to achieve 3x/4x super-resolution factors. Results: Preliminary results indicate possibility of PF-SR with higher super-resolution factors on reconstructing experimental ULF T1w/T2w data to 1/0.75mm3 with reduced artefacts and noise. Impact: Enhancing image resolution and fidelity for fast ultra-low-field brain imaging at 0.055T using data-driven 3D deep learning approach to <=1mm3 resolution potentially enables image-guided therapies and valuable neuroimaging analysis for assessing aging and cognitive conditions.
Deep Learning Reconstruction algorithm for T2-weighted Turbo Spin Echo Renal MRI
Authors: Gao Mengmeng , Li Shichao , Chen Wei , Li Zhen
Published: November 2024

Motivation: MRI is a powerful diagnosis tool for renal tumors with a long acquisition time. How to improve image quality while shortening acquisition time is a major research focus. Goal(s): To use a deep learning (DL) algorithm to reconstruct low-resolution T2-weighted turbo spin-echo (TSE) renal MRI scans and compare with standard-resolution T2-weighted TSE sequence. Approach: A total of 14 patients with clinically suspected renal tumors who underwent renal low-resolution DL-reconstructed T2-weighted TSE sequence(T2DL) and standard-resolution T2-weighted TSE sequence (T2S) were included. Results: T2DL reduced acquisition time by 32% and improved overall image quality compared with T2S. Impact: A DL reconstruction method for low-resolution renal T2-weighted TSE sequence has the potential to reduce acquisition time and improve image quality compared with standard acquisition method, which may help detect renal lesions early and improve the survival rates of patients.
Principal Component Analysis Reconstruction Improves Visualization of Multivolume MR Spectroscopic Imaging at 1.5 T
Authors: Arias-Mendoza Fernando , Nath Kavindra , Li Lin , Srinivasan Ravi
Published: November 2024

Motivation: We address the critical problem of the low signal-to-noise ratio (SNR) of in vivo MR spectroscopic imaging (MRSI) at 1.5 tesla using principal component analysis (PCA). Goal(s): We seek to improve the translatability of MRSI acquired in 1.5T clinical scanners by improving data visualization using PCA reconstruction (PCAR). Approach: We initially corrected for spectral phase and frequency variations in multivolume 31P and 1H MRSI data noninvasively acquired at 1.5 T from human subjects and subsequently used PCAR. Results: We validate the improvement of the MRSI data after PCAR by demonstrating signals that were not distinguishable in the original datasets. Impact: Principal component analysis reconstruction (PCAR) improves the accuracy to assess spectral signals from multivolume 31P and 1H MR spectroscopic imaging data acquired on highly accessible 1.5T clinical scanners, increasing their potential to become noninvasive metabolic biomarkers of human diseases.
A novel neural network method predicts non-acquired brain diffusion MRI to promote HARDI in children
Authors: Oladosu Olayinka , Lo Fanny , Geeraert Bryce , Lebel Catherine , Zhang Yunyan
Published: November 2024

Motivation: High angular resolution diffusion imaging has great potential but is time-consuming so is limited in pediatric clinical studies. Goal(s): To assess the utility of novel deep learning techniques for predicting non-acquired brain diffusion MRI for equivalent HARDI analyses. Approach: A multilayer perceptron (MLP) and convolutional neural network (CNN) were trained to predict b=2000s/mm2 data from b=750s/mm2 data. The neurite orientation dispersion and density index (NODDI) outcomes were computed with quality evaluated with PSNR and SSIM. Results: Both deep learning methods achieved the goal but the CNN outperformed the MLP. Impact: By applying a competitive neural network method, high angular resolution diffusion imaging can be made possible for the pediatric population in a typical clinical setting based only on half of the data typically required.
Accelerating low-distortion diffusion MRI of the head and neck with transfer learning from a different organ, acquisition and scanner
Authors: Alus Or , Yu Victoria , Otazo Ricardo , Aliotta Eric
Published: November 2024

Motivation: Propeller and multi shot EPI sequences offer DWI head and neck images with reduced distortions, but suffer from longer scanning time and reduced SNR. Goal(s): Shorten the scan time while improving SNR and without impacting ADC values. Approach: Apply transfer learning to retrain denoising deep learning algorithm pretrained on different anatomy and vendor scanner, and using limited amount of data. Results: Retraining has improved quantitative metrics, resulting in higher quality denoised images using a limited dataset of 5 cases. Impact: Offsett extra time needed by distortion-reducing protocols to enhance the efficiency of MRI for cancer applications.
An Automatic Striatum Segmentation Model to Estimate MR Maps for Dopamine Transporter SPECT using Deep Learning
Authors: Wang Haiyan , Jiang Han , Chen Gefei , Du Yu , Lu Zhonglin , Zheng Hairong , Liang Dong , Mok Greta S. P. , Hu Zhanli
Published: November 2024

Motivation: Striatum segmentation on SPECT is necessary to quantify uptake for Parkinson's disease (PD), but is challenging due to the inferior resolution. MRI is the preferred reference for segmentation due to its excellent soft tissue contrast. Goal(s): This work proposes cross-modality automatic striatum segmentation, estimating MR striatal maps from clinical SPECT using deep learning (DL). Approach: nnU-Net-based method are implemented and SPECT images are paired with MR-based striatal maps as supervised learning (training:validation:testing = 136:24:40)Results: The proposed method can segment 4 MR-like individual compartments on clinical SPECT, which is also superior to several traditional and DL methods, both in physical and clinical metrics. Impact: The proposed DL-based cross-modality striatum segmentation method is feasible for clinical DaT SPECT in PD, and 4 MR-like individual compartments can be obtained to quantify striatal uptake, which is beneficial to the accurate diagnosis and clinical management of PD.
Prediction patients with alcohol dependence via graph classification on Brain Network Derived from Functional Magnetic Resonance Imaging
Authors: Park Shin-Eui , Jeon Yeong-Jae , Baek Hyeon-Man
Published: November 2024

Motivation: The study is motivated by the need for innovative approaches to alcoholism classification, leveraging neuro-functional network analysis from fMRI data to improve diagnostic accuracy and gain insights into alcoholism's complex nature. Goal(s): The primary goal is to achieve accurate alcoholism classification using functional connectivity patterns and machine learning. Approach: The study employed fMRI data from 15 healthy controls and 15 patients with alcohol dependence, utilizing advanced graph analysis techniques and machine learning algorithms. Results: The approach demonstrated a 73% classification accuracy, highlighting the potential of functional connectivity patterns as diagnostic markers and the value of machine learning in quantifying network differences. Impact: This research contributes to more precise alcoholism diagnosis and offers opportunities for biomarker discovery. It may facilitate earlier intervention and more effective treatments, benefiting both clinicians and patients. The impact includes advancing addiction research and improving patient care.
Diagnosis of Early Mild Cognitive Impairment in Type 2 Diabetes Mellitus by Deep Learning of Multimodal Neuroimages and Metadata
Authors: Han Kangfu , Yue Xiaomei , Qiu Shijun , Yang Feng , Li Gang
Published: November 2024

Motivation: Identification of early cognitive impairment in type 2 diabetes mellitus (T2DM) patients is of paramount importance for mitigating cognitive decline of patients and enhancing their quality of life. Goal(s): Our objective was to develop a robust deep learning model for diagnosing early cognitive impairment in T2DM using multi-modal neuroimages. Approach: We developed a multi-modal neural network, which incorporated informative clinical metadata (i.e., MoCA, BMI and HbA1c) to design metadata-induced contrastive Laplacian regularization. Results: The proposed approach demonstrated significant improvement in accuracy in the identification of T2DM with/without mild cognitive impairment in a dataset with 311 subjects. Impact: Superior diagnostic performance of the proposed method for early cognitive impairment in T2DM demonstrates its ability in understanding of T2DM cognitive impairment associated brain alterations and its potential applications on other brain disorders.
EPR oximetry using Ox071 detects acute kidney injury induced by cyclophosphamide
Authors: Kishimoto Shun , Gadisetti Chandramouli , Devasahayam Nallathamby , Horie Kazumasa , Yamashita Kota , Yamamoto Kazutoshi , Merkle Hellmut , Brender Jeffrey , Crooks Daniel , Krishna Murali , Linehan W.
Published: November 2024

Motivation: EPR oximetry using Ox071 holds promise in detecting chemotherapy-induced acute kidney injury by monitoring pO2 distribution. Goal(s): Using EPR, our study assessed the ability to discern pO2 variations between healthy kidneys and kidney injury models induced by intraperitoneal cyclophosphamide treatment. Approach: To validate these changes, we performed ex vivo histological assessments with pimonidazole staining, comparing these results with EPR based pO2 maps. Results: Analysis of the hypoxic fraction in tumor tissues via pimonidazole staining revealed a transient reduction at 2 days post-treatment, followed by recovery at 30 days. EPR oximetry results consistently mirrored these trends, affirming its reliability as a non-invasive method. Impact: Historically, EPR oximetry focused on tumor hypoxia due to Ox063's limitations in well-oxygenated tissues. Using Ox071, a deuterated analog, current research extends oximetry to normoxic tissues, notably the kidneys.
Pancreatic cancer habitats using CEST pH Imaging, Quantitative Susceptibility Mapping and R2* relaxivity at 7T
Authors: Dominguez-Viqueira William , Zhang Le , Ruiz Epifanio , Lundberg Alex , Budzevich Mikalai , Ibrahim-Hashim Arig , Irrera Pietro
Published: November 2024

Motivation: Many cancer studies concentrate in isolated markers like oxygenation, pH or relaxivity measurements. We believe studying all biomarkers and its interactions as microenvironments is the key. Goal(s): Our goal is to build a library of multiparametric data with pH-derived habitats to aid treatment response in the future. Approach: Mice with flank implanted pancreatic tumors were imaged to estimate tumor volume, pH, R2* and susceptibility maps. All maps were compared using one-way ANOVA between two pH-derived habitats. Results: Significant differences in susceptibility and R2* were found between pH-derived habitats for large tumors. The same trend, without significant differences, was found in smaller tumors. Impact: The study of different tumor microenvironments defined by CEST-derived pH habitats was demonstrated. A larger subset of animals with different tumor volumes and other multiparametric data will help understand tumor microenvironment better and aid treatment response in the future.
Immunotherapy Sensitiziation via Tumor Acidosis Mitigation by Esomeprazole Monitored with MRI
Authors: Murley Grace , Pudakalakatti Shivanand , Padron William , Wang Muxin , Armijo Ryan , de la Cerda Jorge , Dominic Abishai , Chin Renee , Schuler William , Rai Kunal , Pagel Mark , Bhattacharya Pratip
Published: November 2024

Motivation: Biomarkers to predict immunotherapy response are needed. AcidoCEST MRI and hyperpolarized magnetic resonance spectroscopy (HP-MRS) may predict response. Goal(s): Establish baseline pHe and lactate to pyruvate ratio in tumors with varying immunotherapy response. Observe changes after combination treatment with esomeprazole and immune checkpoint blockade (ICB). Approach: AcidoCEST MRI and HP-MRS were used to measure extracellular pH (pHe) and lactate to pyruvate ratio. Imaging was performed on treated and untreated groups. Results: Baseline lactate to pyruvate ratio and pHe were higher in the most resistant model compared to other models, but were unchanged in treatment vs control groups for the most resistant model. Impact: Robust clinical tools are sorely needed to predict immunotherapy response. Development of a non-invasive clinical imaging tool would allow for less wasted time trying ineffective medications and more personalized treatment plans tailored so treatment is as effective as possible.
Multiparametric magnetic resonance imaging findings of the pancreas: Comparison in patients with type 1 and type 2 diabetes
Authors: Higashi Mayumi , Tanabe Masahiro , Tanabe Katsuya , Okuya Shigeru , Takeda Koumei , Nagao Yuko , Ito Katsuyoshi
Published: November 2024

Motivation: The diabetes-related pancreatic changes on MRI remain to be fully elucidated. Goal(s): Our goal was to determine characteristic imaging findings of the pancreas on MRI in patients with type 1 diabetes (T1D) and type 2 diabetes (T2D). Approach: Seven T1D patients and fifty-four T2D patients were imaged using multiparametric pancreatic MRI including T1 map, PDFF map, ADC map and cine-dynamic MRCP, and were compared to control patients. Results: T1D patients had reduced pancreatic size, increased pancreatic T1 and ADC values, and decreased pancreatic juice flow on cine-dynamic MRCP. T2D patients had increased pancreatic PDFF. Impact: Multiparametric MRI provided valuable information on pancreatic changes in both T1 and T2 diabetes and may be useful for assessing pancreatic endocrine and exocrine insufficiency and pancreatic pathological changes in patients with diabetes.
The “MR Bra”: Development of a support device for supine breast imaging with commercially available flexible body coils
Authors: Gordon Isobel , Herlihy Amy , Greenall Gemma , Collins Sally , Brady Michael
Published: November 2024

Motivation: Many women find conventional prone breast imaging uncomfortable. We developed a support device (the ‘MR Bra’) to enable supine breast imaging using existing flex coils, without breast compression. Goal(s): We aimed to evaluate the comfort of the MR bra and asses its SNR performance and structure visualisation capability. Approach: Two prototypes were developed. Healthy participants were scanned prone with the breast coil and supine using the flex coil and MR bra. The application of negative pressure was assessed with additional imaging. Results: The MR bra produced reduced, but acceptable, SNR values and improved patient comfort whilst enabling good visualisation of breast tissue structure. Impact: The MR bra enables comfortable supine breast imaging using readily-available flexible body coils whilst maintaining acceptable SNR values. Negative pressure to elongate the breast structure promises improved tissue visualisation. Further prototype development and evaluation of lesion identification is now underway.
Age and sex-matched intracardiac force analysis for repaired tetralogy of fallot patients using 4D flow MRI
Authors: Pirani Aaliyah , Flores Julio Garcia , Srabanti Monisha
Published: November 2024

Motivation: Cardiac force analysis shows promise for non-invasive treatment and diagnosis for patients with heart disease. Goal(s): Assessing intracardiac forces as a metric of diagnostics in repaired tetralogy of fallot patients which require lifelong monitoring. Approach: Intracardiac forces were analyzed using segment in twenty rTOF patients and 20 control patients. These were compared in age and sex-matched groups. Results: Age and sex-matched analysis showed significant differences in intracardiac forces for control and patient groups. Results also indicated that men and women may be affected differently by rTOF, as there were significant differences between the gender groups with rTOF. Impact: Evaluation of intracardiac forces shows promise as a biomarker for heart disease in rTOF females and males; also demonstrates potential for other diseases, although more investigations must be conducted. This can be used in clinical settings, for non-invasive diagnoses.
Accurate Peak and Mean Velocity with Deep Learning-Reconstructed highly Undersampled 4D Flow MRI using FlowVN
Authors: Qazi Sohaib Ayaz , Khalid Hussnain , Viola Federica , Ebbers Tino , Vanky Farkas , Dyverfeldt Petter
Published: November 2024

Motivation: 4D Flow MRI is limited by long scan times. Accelerated imaging by compressed sensing leads to long reconstruction times. Goal(s): The goal of this work was to evaluate a deep learning-based method (FlowVN) for reconstruction of pseudo-randomly heavily undersampled Cartesian 4D Flow. Approach: In this study, we explored FlowVN for the reconstruction of different acceleration factors and did velocity analysis of 4D Flow MRI. Results: The results show that FlowVN rapidly reconstructs undersampled 4D Flow images with good accuracy for average and peak velocity in the ascending aorta even at high acceleration factors. Impact: High-quality rapid reconstruction of highly undersampled 4D Flow MRI with deep learning has the potential to substantially facilitate the use of 4D Flow MRI in the clinical routine.