bluuebunny/crossref_metadata_2025 huggingface dataset:
The impact of deep learning based image reconstruction on CEST MRI for distinguishing inactive from active thyroid-associated ophthalmopathy
Authors: Wang Yunmeng , Cui Yuanyuan , Dai Jiankun , Wen Qingqing , Xiao Yi
Published: November 2024

Motivation: Thyroid-associated ophthalmopathy (TAO) is characterized by accumulation of collagen in extraocular muscle. CEST-MRI can evaluate the collagen content by focusing on amide compound. However, CEST effect is small and sensitive to low image SNR. A vendor-provided deep learning reconstruction (DLR) algorithm can dramatically increase image SNR. Goal(s): Investigate if CEST-MRI can distinguish inactive from active TAO and the impact of DLR on its diagnostic performance. Approach: 11 active and 12 inactive TAO were enrolled. CEST imaging was reconstructed with DLR and conventional reconstruction. Results: DLR can significantly increase SNR of CEST imaging and improved the diagnostic performance for discriminating inactive from active TAO. Impact: The treatment of TAO depends on the disease phase. DLR image reconstruction improved the performance of CEST in differentiation between inactive and active TAO. It would help in the evaluation and management of TAO patients.
A Comparative Analysis of CEST Techniques for Mapping Muscle Creatine and Phosphocreatine at 3T
Authors: Ju Licheng , Wang Kexin , Schär Michael , Xu Su , Rogers Joshua , Zhu Dan , Qin Qin , Weiss Robert , Xu Jiadi
Published: November 2024

Motivation: Creatine and phosphocreatine metabolites imaging at 3T are essential for related disease in muscle. Goal(s): Estimate creatine proton exchange rate in muscle; Simultaneous mapping of PCr and Cr by PLOF CEST method at 3T. Approach: Antemortem and postmortem animal study was to validate PCr/Cr CEST peak position and creatine exchange rate. Three types of CEST acquisition methods were compared on human leg muscle. Results: Z-spectra in mouse hindlimb before and after euthanasia indicated CrCEST is a slow-exchanging process (<150 s-1). This allowed us to simultaneously extract PCr/CrCEST signals and mapping in muscle at 3T using the PLOF method on both human and animal. Impact: Amide, Cr, and PCr CEST in the skeletal muscle can be mapped simultaneously at 3T by PLOF CEST within a clinically feasible acquisition duration, which has potential to assist in the diagnosis of related diseases.
B1 inhomogeneity corrected APT MRI based on direct saturation removed omega plot model at 5 T
Authors: Wu Qiting , Li Ye , Liang Dong , Zheng Hairong , Wu Yin
Published: November 2024

Motivation: B1 inhomogeneity correction is critical in CEST MRI. Reliable correction methods are desired. Goal(s): This study developed a B1 inhomogeneity correction method based on a direct saturation removed omega plot model and tested its performance in human brain APTw imaging at 5 T. Approach: Four healthy volunteers were scanned under four B1 levels. Corrected signal at nominal B1 was calculated from the omega plot model determined from either two or four B1 levels. Results: B1 inhomogeneity-induced artifact was shown on uncorrected APTw images, which was effectively mitigated after correction. Comparably homogeneous APTw maps were obtained between two and four B1 levels. Impact: The proposed method enables reliable B1 inhomogeneity correction from at least two B1 levels, providing an efficient way to improve quantitative CEST MRI, especially on high-field scanners.
Comparison between a new quantitative APT parameter APT_T1 by solving Bloch equation of multiple pool model and MTR_Rex
Authors: Miyoshi Mitsuharu , Oguchi Kazuhiro , Wakayama Tetsuya
Published: November 2024

Motivation: Quantitative parameter is required for APT. Goal(s): Comparing a new quantitative APT parameter APT_T1 (the ratio of APT transfer rate to longitudinal relaxation rate) with MTR_RexApproach: APT_T1 was calculated by solving the Bloch equation of multiple pool model. APT_T1 was compared with MTR_Rex in numerical simulation and a clinical case. Results: MTR_asym was influenced by rNOE and differed from MTR_Rex. On the other hand, although calculation methods of APT_T1 and MTR_Rex were different, APT_T1 approximated MTR_Rex under the condition with strong CEST RF. APT_T1 and MTR_Rex can be used as complementary quantitative APT parameters. Impact: A new quantitative APT parameter APT_T1 was calculated by solving Bloch equation of multiple pool model. APT_T1 approximated MTR_Rex under the condition with strong and long CEST RF with 100% duty cycle.
Accelerating CEST-MRI for 3D renal imaging
Authors: Wang Xiaojing , Dimitrov Ivan , Keupp Jochen , Madhuranthakam Ananth , Eggers Holger , Asturias Alexander , Vinogradov Elena
Published: November 2024

Motivation: CEST-MRI is gaining recognition as a valuable tool for characterization of tumors and other ailments. However, its application in body imaging is still relatively limited. Specifically, applications to renal imaging are hampered by fat artifacts, motion, and limited coverage. Goal(s): The goal of our work is to achieve reliable renal CEST MRI. Approach: We are developing and compare acceleration of 3D GRE multi-point Dixon methods, to work in conjunction with timed breathing. The acquisition acceleration strategies include Compressed Sensing (CS) and CS with CASPR k-space sampling pattern. Results: The developments allow expansion of the previous single-slice CEST MRI to a 3D whole-kidney coverage. Impact: CEST-MRI for renal assessment will provide valuable information on kidney health and function, adding to existing multiparameteric MRI protocols, to serve as a molecular-level assessment of diseases, such as differentiation of renal cancers and acute renal failure.
Advancing Z-Spectrum Analysis Protons (ZAP) Using Machine Learning with Random Forest Regression.
Authors: Malis Vadim , Miyazaki Mitsue
Published: November 2024

Motivation: To overcome long scan times in MRI's Z-Spectrum Analysis Protons (ZAP), impairing its clinical utility and patient comfort. Goal(s): This study aimed to refine ZAP, targeting a reduction in scan duration while maintaining high accuracy in proton exchange measurements. Approach: We applied Random Forest Regression to identify key offset frequencies, focusing on the most informative data and potentially reducing the scanning time. Results: Our approach successfully reduced scan times without compromising accuracy. The RFR model’s predictions aligned closely with traditional ZAP methods, indicating that fewer offset frequencies are needed for reliable data interpretation. Impact: This study may allow for targeted anatomical and disease-specific imaging with reduced scan times, potentially improving diagnostic accuracy and patient experience, and facilitating quicker, more focused clinical decisions.
Decorrelation Algorithm for Correcting B1 Artifacts in APTw Imaging at 3 Tesla
Authors: Papageorgakis Christos , Zucchelli Mauro , Dipasquale Ottavia , Mancini Laura , Bisdas Sotirios , Liebig Patrick , Zaiss Moritz , Casagranda Stefano
Published: November 2024

Motivation: We present a data-driven approach for B1 correction in APTw MRI eliminating the requirement for additional volume sampling at various B1-values, thereby significantly reducing acquisition time. Goal(s): This enhancement ensures feasible and robust B1 correction in clinical settings. Our method establishes homogeneity in white matter (WM) and gray matter (GM) values within 3D-APTw volume. Approach: By modeling the correlation between rB1 and the APTw data, followed by a decorrelation algorithm, we achieve closer alignment of WM and GM values across 3D volume. Results: This rapid technique considerably reduces acquisition time and significantly improves the coherence of WM and GM values throughout the slices. Impact: The proposed B1 decorrelation technique strongly impacts qualitative and semi-quantitative APTw imaging applications due to considerable reduction in B1 artifacts. Homogeneous contrast among WM, GM, and tumor values is achieved within and across slices.
One-Shot Learning for CEST-Centered Multiparametric MRI: Training Neural Network with One Single Scan
Authors: Chen Zhekai , Gong Tao , Bao Jianfeng , Lin Liangjie , Chen Lin
Published: November 2024

Motivation: Multiparametric imaging offers comprehensive information. However, its practical application is hindered by extended scanning times. Goal(s): To develop a CEST-centered multiparametric approach capable of producing multiple quantitative maps. Approach: ResNet was utilized to simultaneously quantify amide, NOE, MT, DS, B0, T1 and T2. By incorporating a reweighting scheme in conjunction with transfer learning, we demonstrate one single scan is adequate to train a well-performing neural network. The robustness and generalizability of the proposed method were validated using multicenter data. Results: The proposed method outperformed state-of-the-art CEST deep learning method, providing more accurate quantification results, all while requiring a limited amount of training data. Impact: The proposed method has the potential to establish a CEST-centered multiparametric approach, eliminating the need for multiple scanning protocols and, consequently, reducing scan time.
Motion-induced B1+-changes in dynamic glucose enhanced (DGE) MRI and how to remedy them.
Authors: Lehmann Patrick , Ljungberg Emil , Bloch Karin Markenroth , Yadav Nirbhay , Wirestam Ronnie , Sundgren Pia , van Zijl Peter , Knutsson Linda
Published: November 2024

Motivation: Motion-induced B1+-changes at the voxel level result in erroneous dynamic glucose enhanced (DGE) MRI effects. Goal(s): To investigate effects of motion-induced B1+-changes on DGE MRI and to address removing them. Approach: A volunteer changed head positions, and voxel-based B1+ was measured pre- and post-motion. Z-spectra with and without D-glucose infusion were simulated, with and without the measured B1+-changes. Results: Slight motion-induced B1+-alterations lead to pseudo-CEST effects comparable to DGE effects. These can be removed by acquiring a full Z-spectrum and using the asymmetry of the glucoCEST signal relative to the water frequency to assess the DGE signal changes. Impact: Motion-induced B1+-changes affect DGE signals, thus causing pseudo-CEST effects that complicate clinical interpretation. These effects can be overcome by acquiring a full Z-spectrum and exploiting the asymmetry of the glucoCEST signal changes relative to the water frequency.
Temporally-Aware Neural Networks For Cine MRI Reconstruction From Severely Undersampled Data
Authors: Mahajan Niraj , Rodríguez-Soto Ana , Chung Nuri , Hegde Sanjeet , Gordon Brent , Potersnak Amanda , Blood Joni , Contijoch Francisco
Published: November 2024

Motivation: MRI guidance of an interventional procedure requires fast image reconstruction. A neural network(NN)-based approach can exploit the similarities between consecutive frames to improve iMRI image reconstruction. Goal(s): We investigate if an LSTM can reconstruct images from just ten spokes per frame in a timeframe compatible with iMRI. Approach: A convolutional (conv)LSTM was trained using the open-source ACDC dataset. Results were compared with Multi-domain convolutional neural network (MD-CNN) - a recently-published 3D NN-based method for undersampled MRI reconstruction. Results: ConvLSTMs can reconstruct frames at ~226 fps (17x faster than MD-CNN ~13 fps). SSIM for the convLSTM was slightly lower than the MD-CNN (0.85 vs 0.89). Impact: With our LSTM-based model, we have achieved a 17x speed-up in the iMRI acquisition process without significant loss in image quality. This suggests that an LSTM-based method could be used to improve iMRI image speed and quality.
Movieformer: Motion-resolved 4D MRI reconstruction using a network with spatiotemporal attention
Authors: Mekhanik Anthony , Murray Victor , Otazo Ricardo
Published: November 2024

Motivation: Investigate utility of self-attention deep learning to exploit global temporal information in motion-resolved 4D MR imaging. Goal(s): Design a novel hybrid convolutional-attention network to reconstruct motion-resolved 4D images without explicit k-space data consistency. Approach: A hybrid Unet-style 4D reconstruction network was developed to incorporate windowed multiscale spatiotemporal multihead self-attention. Training and testing were performed on free-breathing data acquired on patients with abdominal tumors. Results: Spatiotemporal attention successfully captured motion in multiple dimensions with improved image quality relative to state-of-the-art XD-GRASP reconstruction. Impact: Self-attention deep learning mechanism can combine long-range spatial learning and global temporal learning to augment capabilities of convolutional networks for improved motion-resolved 4D MRI of mobile tumors.
Volumetric Reconstruction Resolves Off-Resonance Artifacts in Static and Dynamic PROPELLER MRI
Authors: Ghosh Annesha , Wetzstein Gordon , Pilanci Mert , Fridovich-Keil Sara
Published: November 2024

Motivation: Off-resonance correction typically requires field map measurements or pretraining data, which are slow/difficult to collect. Goal(s): We propose a physics-based strategy to address off-resonance artifacts using only PROPELLER measurements, by modeling an additional spectral dimension. Approach: This strategy exploits an equivalence between measuring a PROPELLER blade at a certain angle, and viewing a relief sculpture at the same angle. In this equivalence, three-dimensional structures (fat) appear shifted along the blade/view direction relative to flatter structures (water). Results: Our method resolves continuous chemical shift artifacts while allowing for video reconstruction of dynamic tissues. We provide preliminary results on synthetic static and dynamic data. Impact: We use volumetric reconstruction to correct off-resonance artifacts and perform fat/water separation in PROPELLER MRI, without additional field map measurements or pretraining data. We hope our method opens the door to shorter scan times and higher temporal resolution imaging.
ML models for 4D cine imaging
Authors: Wrobel Mark , Muthurangu Vivek , Montalt Javier , Steeden Jennifer
Published: November 2024

Motivation: The motivation behind this study is to severely shorten scan times for children undergoing cardiac examination. Goal(s): The goal is to be able to create a 4D dataset from a short, free breathing real-time 2D stack of images. Approach: We apply three machine learning models to the 2D stack. The first reconstructs the undersampled image data, the second corrects respiratory artefacts caused by free-breathing and the third model super resolves the images. Results: The image quality is vastly improved after applying the machine learning models. The ventricular volumes are also in good agreement with the reference volumes. Impact: Severely reduced scan times for comprehensive cardiac examination in CHD without the use of breath-holds. Machine Learning methods may be able to also be used for other imaging sequences, also resulting in faster image acquisition.
Cine Cardiac MRI Motion Correction using Denoising Diffusion Probabilistic Models
Authors: Liu Yang , Diao Jiameng , Zhou Zijian , Qi Haikun , Hu Peng
Published: November 2024

Motivation: Cine cardiac MRI is used to evaluate cardiac functions and vascular abnormalities. However, MRI requires a long scan time, which inevitably induces motion artifacts. Goal(s): Develop a cine cardiac MR image motion correction technique to reduce both the scan time and motion artifacts. Approach: We trained a diffusion-based model with simulated data from a public ACDC dataset to reduce the cine cardiac MRI motion artifacts. Results: The proposed method was compared with GAN and U-Net methods in removing motion artifacts. It produced results that closely approach the ground-truth, achieving the highest SSIM and PSNR scores among all the evaluated methods. Impact: Our method demonstrates improvements in motion compensation compared with GAN and U-Net.
Rapid LAVA imaging with deep learning reconstruction: evaluation of image quality and diagnostic performance in patients with liver cancer
Authors: Sa Guo , Wang Qidong , Shang Desheng , Wen Qingqing , Dou Weiqiang , Feng Zhan , Chen Feng
Published: November 2024

Motivation: 3D gradient-echo based liver acceleration volume acquisition (LAVA) sequence is widely used for dynamic contrast imaging in liver. LAVA usually requires breath-holding for over 16 seconds, posing a challenge for individuals with difficulty in prolonged breath-holding. Goal(s): To investigate whether deep learning reconstruction (DLR) allows for LAVA imaging with reduced scan time but without sacrificing image diagnostic quality. Approach: SNR, CNR, and subjective analysis using 5-point Likert scales were compared to evaluate the image quality and diagnostic performance between DLR-LAVA and conventional LAVA. Results: Compared to conventional LAVA, DLR-LAVA showed similar SNR, CNR, and qualitative image quality scores. Impact: Deep learning reconstruction based rapid LAVA imaging is promising for reducing breath-hold time while maintaining similar image quality compared with conventional LAVA imaging.
Scale Time-Equivariant Convolutional Neural Networks For Dynamic Magnetic Resonance Imaging
Authors: Zhu Yuliang , Cheng Jing , Cui Zhuoxu , Wang Yulin , Zeng Jie , Wang Chengbo , Liang Dong
Published: November 2024

Motivation: The scale symmetry of anatomical structures commonly exists in dynamic magnetic resonance imaging (MRI) data but have rarely been explored. Goal(s): Our goal is to effectively leverage the scale symmetry of local structures in both spatial and temporal dimensions to improve the reconstrcution quality in dynamic MRI. Approach: We present a novel method that incorporates the scale equivariant convolution modules into an unrolled deep neural network. Results: The proposed method was test on the cardiac cine MRI data reconstruction tasks and achieved the improved performance with a PSNR of 43.6967 and a SSIM of 0.9834. Impact: Our method improved the data-efficiency for deep dynamic MRI reconstructions and robustness to drifts in scale of the images that might stem from the variability of patient anatomies or change in field-of-view across different MRI scanners.
Cyclic-Consistency for Improved Self-Supervised Learning of Highly Accelerated MRI Reconstruction
Authors: Zhang Chi , Demirel Omer , Akçakaya Mehmet
Published: November 2024

Motivation: To improve self-supervised deep learning (DL) reconstruction for highly-accelerated acquisition regimes. Goal(s): To introduce the concept of cyclic-consistency to improve self-supervised DL reconstruction for highly-accelerated MRI. Approach: Cyclic-consistency data is formed by simulating new undersampled acquisitions from the neural network output, with a similar undersampling pattern distribution as the true one. Then reconstruction on these simulated data is trained to match acquired data at the true sampling locations, building cyclic consistency for network training. This is supplemented with a conventional self-supervised masking strategy. Results: The proposed method significantly reduces artifacts at rate 6 and 8 fastMRI reconstruction, and 20-fold fMRI. Impact: Substantial reduction in aliasing artifacts is achieved at high acceleration rates using the proposed cyclic-consistent self-supervised learning method compared to existing self-supervised learning methods.
NLCG-Net: A Model-Based Zero-Shot Learning Framework for Undersampled Quantitative MRI Reconstruction
Authors: Jiang Xinrui , Bilgic Berkin
Published: November 2024

Motivation: Typical quantitative MRI (qMRI) methods estimate parameter maps after image reconstructing, which is prone to biases and error propagation. Goal(s): To propose an end-to-end method to directly estimate qMRI maps from undersampled k-space data using model-based reconstruction and zero-shot network regularization. Approach: We propose a Nonlinear Conjugate Gradient (NLCG) optimizer for model-based T2/T1 estimation, which incorporates U-Net regularization trained in a scan-specific manner. Results: T2 and T1 mapping results demonstrate the ability of the proposed NLCG-Net to improve estimation quality compared to subspace reconstruction at high accelerations. Impact: We propose a model-based qMRI technique, NLCG-Net, that incorporates mono-exponential signal modeling with zero-shot scan-specific neural network regularization to enable high fidelity T1 and T2 mapping.
Aliasing Artefact Suppression in Machine Learning MRI Reconstruction for Random Phase-Encode Undersampling
Authors: Yuan TengFei , Kang Zhaoxin , Chi Jieru , Yang Jie
Published: November 2024

Motivation: Random phase-encode undersampling of Cartesian k-space trajectories is widely implemented in magnetic resonance imaging. However, its one-dimensional randomness inherently introduces large coherent aliasing artefacts along the undersampled direction in the reconstruction, which need to be suppressed. Goal(s): Our goal is to introduce a novel reconstruction scheme to reduce the one-dimensional undersampling-induced aliasing artefacts. Approach: We propose an intermediate-domain network tailored for operation in image-Fourier space, which utilizes the superior non-coherent properties of decoupled one-dimensional signals to reduce aliasing artifacts. Results: Experiments illustrate that the proposed method is particularly well-suited for regular Cartesian undersampling scenarios. Impact: The intermediate-domain network tailored to operate in the Image-Fourier space, can efficiently reduce aliasing artefacts by utilizing the superior incoherence property of the decoupled one-dimensional signals. This could further inspire the development of MRI reconstruction technology based on machine learning.
Integrating Quantitative Mapping into Physics-Based Deep Learning for Improved Accelerated Image Reconstruction
Authors: Carvalho Catarina , Gaspar Andreia , Nunes Rita , Correia Teresa
Published: November 2024

Motivation: Physics-based deep learning has been increasingly applied to MRI image reconstruction to accelerate acquisitions. Goal(s): Here, we investigate whether including a relaxometry model into these networks enables higher quality accelerated reconstructions, and consequently more accurate quantitative maps. Approach: Two recurrent inference machines with different physics models were implemented: (1) reconstruction of contrast-weighted image series and (2) direct T2 map estimation, from undersampled k-space dataResults: Including relaxometry into physics-informed networks improves reconstruction and T2 map quality for acceleration factors as high as 8-fold. Impact: Integrating relaxometry models into physics-informed deep learning-based image reconstruction methods enables high quality quantitative mapping directly from undersampled k-space data, from which contrast-weighted images can also be accurately synthesised.
Joint group sparsity-based motion-compensated deep learning reconstruction for 3D whole-heart joint T1/T2 mapping
Authors: Felsner Lina , Phair Andrew , Kunze Karl , Botnar René , Prieto Claudia
Published: November 2024

Motivation: Myocardial T1 and T2 mapping is crucial in the assessment of cardiovascular disease. 3D whole-heart joint T1/T2 mapping approaches have been proposed, however they require long reconstruction times. Goal(s): By leveraging deep learning (DL)-based techniques, we aim to significantly reduce the reconstruction times for 3D whole-heart joint T1/T2 mapping, while maintaining high-quality results. Approach: Recently a joint group sparsity-based DL approach was proposed for image reconstruction of undersampled multi-contrast MRI data. Here, we propose to extend this approach for non-rigid motion-corrected reconstructions for multi-contrast 3D data for joint T1/T2 mapping. Results: Our approach achieves good agreement with reference techniques, while outperforming single-contrast reconstructions. Impact: Joint group sparsity-based deep learning non-rigid motion-corrected reconstruction for multi-dimensional joint 3D T1/T2 whole-heart mapping achieves good agreement with reference techniques and outperforms single-contrast reconstructions. The approach significantly reduces reconstruction times, making it feasible for clinical applications.
Deep-learning-based optimization of k-space undersampling in self-supervised MRI reconstruction
Authors: Liu Chun , Hu Peng , Qi Haikun
Published: November 2024

Motivation: Self-supervised deep learning has shown good performance in reconstructing undersampled k-space. While recent developments focus on improving reconstruction performance for a given undersampling pattern, there is limited research aiming to learn and optimize k-space sampling strategies to offer a performance gain in self-supervised reconstruction. Goal(s): To design a deep learning framework to optimize the sampling pattern in self-supervised MRI reconstruction. Approach: An Auto Mask Module was optimized simultaneously with the self-supervised reconstruction module in an end-to-end framework. Results: The proposed method can achieve better reconstruction results than self-supervised methods based on fixed masks. Impact: The proposed method can produce better self-supervised reconstruction results by optimizing the k-space undersampling pattern.
Accelerating MRI with Spiral Trajectory Optimization and Reconstruction using Diffusion Models
Authors: Chan Trevor , Dong Jessie , Yu Nabo , Song Hee Kwon , Rajapakse Chamith
Published: November 2024

Motivation: Deep learning methods for accelerated MRI achieve state-of-the-art results but largely ignore additional speedups possible with noncartesian sampling trajectories. Goal(s): To create a generative diffusion model-based reconstruction algorithm for multi-coil undersampled spiral MRI. Approach: We train a conditional diffusion model and use frequency-based guidance to ensure consistency between images and measurements. Results: Evaluated on retrospective data, we show high quality (SSIM > 0.87) in reconstructed images with ultrafast scan times (0.02 seconds for a 2D image). We use this algorithm to identify a set of optimal variable-density spiral trajectories and show large improvements in image quality compared to conventional nufft reconstruction. Impact: We apply diffusion models to the task of non-cartesian reconstruction. Combining efficient spiral sampling trajectories, multicoil imaging, and deep learning reconstruction enables drastically accelerated imaging. Potential applications of this technology include real-time 3D imaging.
ResoNet: MRI Super-Resolution via Multi-Path GAN with Adaptive Convolutional Kernels
Authors: Zhang Haotian , Teng Long , Zhao Yi , Qu Hang , Dou Weiqiang , Ban Qi-qi , Wen Gangzhong
Published: November 2024

Motivation: The project aims to improve MRI image resolution, addressing the limitations of current super-resolution methods that hinder accurate medical diagnosis and treatment planning. Goal(s): Enhancing MRI image clarity through ResoNet, an innovative approach featuring a multi-path generator, self-adaptive kernel sizing, and a comprehensive loss function. Approach: Multi-path generator enhances resolution with different kernel sizes, preserving details. Self-adaptive component optimizes kernel size for efficiency. Comprehensive loss function combines GAN-based discriminator, VGG16, and WGAN-GP for sTab. training and quality samples. Results: ResoNet significantly enhances SSIM and PSNR scores, promising substantial impact in medical imaging for more precise diagnoses and treatment planning by healthcare professionals. Impact: This work improves MRI diagnostics by enhancing image resolution, enabling clearer visuals for accurate medical assessments. It optimizes performance and resources, accelerates scanning time, and potentially transforms emergency medical practices, making it an improvement in medical imaging.
Random forest classifiers show myelin alterations in Long-COVID
Authors: Ricciardi Antonio , Grosso Elena , Shatila Madiha , Zandi Michael , Yiannakas Marios , Prados Ferran , Kanber Baris , Wingrove Jed , Grussu Francesco , Battiston Marco , Samson Rebecca , Tur Carmen , Batterham Rachel , Makaronidis Janine , Rolandi Nicolò , Palesi Fulvia , D'Angelo Egidio , Ciccarelli Olga , Wheeler-Kingshott Claudia Gandini
Published: November 2024

Motivation: Some people exposed to SARS-CoV-2 experience persistent symptoms months after recovering from acute infection. These symptoms, grouped under the umbrella of “long-COVID”, include neurological and autonomic dysfunctions for which there is not yet a clear explanation. Goal(s): To identify biophysically meaningful features characterising long-COVID pathophysiology in the brain. Approach: A novel multimodal MRI protocol was developed to measure iron accumulation, myelin content, inflammation, microstructure alterations, atrophy, and cerebral blood flow changes. Machine learning was employed to discriminate long-COVID from controls and people who fully recovered. Results: Alterations in bound-pool fraction (myelin content) emerge in long-COVID. Impact: This study encourages investigating the neurological aspect of long-COVID and its interplay with conditions affecting myelin as it shows that long-COVID may cause myelin alterations or be more likely to occur in people with compromised myelin content in the brain.
Assessing and Enhancing the Robustness of Brain Tumor Segmentation using a Probabilistic Deep Learning Architecture
Authors: Alwadee Ebtihal , Sun Xianfang , Qin Yipeng , Langbein Frank
Published: November 2024

Motivation: Motivated by the challenge of enhancing the robustness of deep neural network decisions against variable noise in MRI-based brain tumor segmentation. Goal(s): This study aims to evaluate the efficacy of probabilistic bottlenecks in enhancing segmentation robustness. Approach: Our approach simulates structured perturbations at increasing strength to assess their impact on segmentation performance utilizing the Wasserstein distance between per-sample Dice score distributions and the sensitivity with respect to the perturbation strength. Results: Results show probabilistic bottlenecks significantly increase robustness to Gaussian noise, yet offer limited improvement towards Gaussian blur, with varying results for other perturbations, highlighting the perturbation-specific nature of network resilience. Impact: This study provides a tool to assess and guard against various perturbations in deep learning.It specifically demonstrates that probabilistic bottlenecks boost robustness of performance withrespect to certain noise types, but not all.
Evaluation of Biochemically Recurrent Prostate Cancer Using the Prostate Imaging for Recurrence Reporting Systems
Authors: liu xiang , Shen Jun , Deng Kan
Published: November 2024

Motivation: PI-RR assessment is not universally validated and has not yet been extensively studied for diagnostic accuracy and interreader variability. Goal(s): To evaluate the performance of PI-RR for detecting local recurrence in PCa patients with BCR after radiation therapy (RT) or radical prostatectomy (RP). Approach: 110 patients who underwent MRI and FDG PET/CT were included. A group of four radiologists with varying experience was recruited for recurrence evaluation. Results: With a PI-RR of 3 as a cutoff, the AUCs ranged from 0.56 to 0.83 after RT and 0.73 to 0.90 after RP across the four readers. The ICC values were 0.54 and 0.68, respectively. Impact: PI-RR may serve as a clinical guide to improve the management of recurrent PCa by achieving good diagnostic performance and interreader agreement.
Detection Efficiency of 18F-PSMA-1007 PET/CT and Multi-parameter MRI in Prostate Cancer and Its Correlation with Pathological Grade
Authors: Yunshu Zhou , Zhiqiang Chen , ruodi Zhang , Shili Liu , Xiaohua Chen , Aijun Wang , Yuhui Xiong
Published: November 2024

Motivation: The combination of MRI imaging and PSMA PET/CT imaging has attracted more and more attention. Goal(s): Therefore, this study will compare the diagnostic value of 18F-PSMA-1007 PET/CT imaging and multi-parameter MRI alone and in combination with prostate cancer. Approach: This study retrospectively collected the clinical, imaging and pathological data of 50 patients with prostate disease who underwent mp-MRI and 18F-PSMA-1007PET/CT examination in our hospital. Results: We found that the combination of 18F-PSMA-1007 PET/CT and multi-parameter MRI can improve the diagnostic efficiency of prostate cancer. ADC value, SUVmax and SUVmax/ADC can distinguish between low-risk and medium-high-risk prostate cancer. Impact: The diagnostic efficacy of combined multi-parameter MRI (ADC) and 18F-PSMAPET/CT (SUVmax) is better than that of ADC, SUVmax and SUVmax/ADC with higher sensitivity and specificity. The combination of these two imaging modalities can complement each other and improve the accuracy.
Inverse Z-spectrum analysis of chemical exchange saturation transfer MRI in orthotopic models of paediatric-type diffuse high grade glioma.
Authors: Bolster Declan , Roy Upasana , Pereira Rita , Kessler Ketty , Jones Chris , Robinson Simon , Boult Jessica
Published: November 2024

Motivation: The diffuse growth of paediatric-type diffuse high grade glioma (PDHGG) precludes complete delineation with conventional MRI. Goal(s): Molecular imaging with CEST may improve tumour detection. Approach: Three orthotopic models of PDHGG were assessed using inverse Z-spectrum analysis of CEST data. Results: Clear distinction between tumour and contralateral normal-appearing brain in ssMT and rNOE maps, which relate to macromolecular content, was observed in two well-defined tumour models when analysed using MTRRex and AREX. Similar CEST contrast was also apparent in a third more diffuse model, inconspicuous on T2w-MRI. This CEST approach can successfully stratify PDHGG tumours in vivo. Impact: Relaxation compensated CEST metrics provide novel biochemical contrasts in three orthotopic models of paediatric-type diffuse high grade glioma, enabling detection of diffuse disease, highlighting the clinical potential of CEST contrasts to stratifying tumours.
EPR imaging of oxygen consumption driven by NQO1-activated compounds in FH-deficient renal tumors
Authors: Shibata Yuki , Crooks Daniel , Kishimoto Shun , Munasinghe Jeeva , Ervin Kristofer , Brender Jeffrey , Krishna Murali , Linehan William
Published: November 2024

Motivation: Renal tumors in patients affected by Hereditary Leiomyomatosis and Renal Cell Cancer (HLRCC) are aggressive and metastasize early. The high expression of NAD(P)H quinone oxidoreductase 1 (NQO1) in HLRCC could be a therapeutic target. Goal(s): Our goal was to determine if NQO1-activated substrates can be used as a therapeutic approach for HLRCC renal tumors. Approach: In vitro experiments and EPR, DCE, and photoacoustic imaging were performed to study the effect of NQO1-activated substrates on oxygen levels and tumor growth in HLRCC. Results: Our study demonstrated by EPR imaging that oxygen consumption is induced by NQO1-activated substrates, resulting in ROS production and tumor cytotoxicity. Impact: We found that NQO1-activated substrates induced non-mitochondrial oxygen consumption in FH-deficient renal tumor cells, triggering oxidative stress-induced cancer cell death. Our results suggest a promising path for NQO1-targeted therapy in HLRCC, necessitating further research and treatment development.
Targeting Extradomain-B Fibronectin to Monitor Immune Checkpoint Therapy with MRI in Head and Neck Squamous Cell Carcinoma
Authors: Hall Ryan , Wang Hong , Laney Victoria , Lu Zheng-Rong
Published: November 2024

Motivation: Only 15-20% of patients with advanced head and neck squamous cell carcinoma (HNSCC) benefit from immunotherapy, with no reliable strategies to quickly predict or determine a patient’s response to treatment. Goal(s): We aim to evaluate a therapeutic monitoring strategy using MRI by targeting extradomain B fibronectin (EDB-FN) in the tumor stroma. Approach: Using mouse models bearing HNSCC allografts, we treated and monitored therapeutic efficacy of anti-PDL1 immunotherapy with MRI using MT218, our EDB-FN-targeted contrast agent. Results: Treatment-responsive tumors exhibited unique expression patterns of EDB-FN compared to controls that were visualized with MT218 MRI, demonstrating the potential for predicting and monitoring HNSCC immunotherapy response. Impact: We demonstrate that EDB-FN is a potential biomarker for immunotherapy response in HNSCC, and our MRI strategy targeting EDB-FN offers clinicians a potential method for predicting or monitoring immunotherapeutic outcomes to improve the clinical management of HNSCC.
Improving Cardiac Phase-Resolved T2*-Mapping of the Murine Heart: Artifact Reduction and Enhanced Accuracy
Authors: Shalikar Shahriar , Laghzali Oumaima , Liu Siqin , Pohlmann Andreas , Periquito Joao , Waiczies Sonia , Niendorf Thoralf , Ku Min-Chi
Published: November 2024

Motivation: Rapid blood flow in mice heart imposes severe phase shift error in k-space data, resulting ghosting artifact in the image. These artifacts notably affect the accuracy of myocardial T2* relaxometry, as they introduce variable ghosting artifacts into echo images. Goal(s): To investigate the effect of Flow Compensation (FC) on myocardial T2*-map acquired by retrospective-gated Multi-Gradient-Echo (MGE) sequence. Approach: First-order GMN were added to MGE sequence. Retrospective ECG-gating was performed on murine heart. Results: FC technique can potentially reduce T2* estimation error by mitigating the flow artifact on echo images in mice heart with very fast heart rate. Impact: First-order Gradient Moment Nulling minimizes image artifacts and enhances the accuracy of myocardial T2* measurements.
Ultrashort Echo Time Quantitative Susceptibility Mapping (UTE-QSM) of The Human Knee with Motion Registration
Authors: Athertya Jiyo , Moazamian Dina , Malhi Bhavsimran Singh , Jerban Saeed , Drygalski Annette , Chang Eric , Du Jiang , Jang Hyungseok
Published: November 2024

Motivation: There is a need for improving UTE-QSM in the human knee joint, which is prone to motion artifacts due to multiple repeated scans required for a short echo spacing. Goal(s): To investigate the efficacy of motion registration-based UTE-QSM for knee joint imaging. Approach: We employed rigid affine-based registration and non-rigid deformable registration based on B-spline as a pre-processing step for generating the QSM data. Results: It is seen that the registration process helps in reducing streaking artifacts and improving UTE-QSM of the knee joint. Impact: The UTE-QSM technique of the human knee joint is a potentially sensitive biomarker for the diagnosis of musculoskeletal diseases. Motion registration can improve the accuracy of UTE-QSM and hence likely enhance the diagnostic power.
Deep Learning Reconstruction (DLR) in Liver MRI: Comparison of Image Quality and diagnostic efficiency of LAVA sequence
Authors: Cheng Yong , Zhang Yu , Liu Zhixuan , Wang Ziwei , Zhang Miaoqi , Zhang Bo , Zhang Ruzhi , Shuai Tao
Published: November 2024

Motivation: The potential of DLR to improve the quality of liver acquisition with volume acceleration (LAVA) sequence images and its impact on lesion diagnosis has not been extensively reported. Goal(s): This study aims to investigate the utility of DLR in liver MR by comparing the image quality and diagnostic efficacy of the original LAVA sequence in the venous phase with the DL-LAVA sequence. Approach: The image quality and diagnostic performance of the DL-LAVA sequence were compared with the original LAVA sequence. Results: The results revealed that the DL-LAVA sequence significantly improved the image quality of the LAVA sequence, and its diagnostic performance was superior. Impact: The results show that DLR can significantly improve the image quality of LAVA sequence, which may improve the detection and diagnosis of liver-related lesions, providing a powerful imaging basis for prevention and treatment.
Single-shot and multi-shot cardiac diffusion MRI in vivo using high-amplitude gradient of 200mT/m
Authors: Moulin Kévin , Troalen Thomas , Speier Peter , Croisille Pierre , Viallon Magalie
Published: November 2024

Motivation: High gradient hardware has the potential to reduce TE and improve SNR for Cardiac diffusion tensor imaging (cDTI) for both single-shot (SS-EPI) and multi-shot EPI (MS-EPI) approaches. However, gradient amplitude and slew rate can be limited due to Peripheral and Cardiac Nerve stimulation (PNS/CNS). Goal(s): To compare Gmax=200mT/m and Gmax=80mT/m systems for cDTI. Approach: Healthy volunteers (n=3) were imaged on both systems using SS-EPI and MS-EPI. PNS/CNS and diffusion parametric mapping were compared. Results: Equivalent diffusion parameters were found for all acquisitions and systems. At Gmax=200mT/m, MS-EPI used the hardware system efficiently due to reduced PNS but remained limited by CNS. Impact: In this work, we studied hardware limitations due to PNS/CNS for two high-gradient systems for SS-EPI and MS-EPI. MS-EPI reduces image distortions due to B0 inhomogeneities, improves SNR compared to SS-EPI, and used the most efficiently the hardware system.
Measurement of 3D abdominal magnetic transfer ratio under a single breath-hold using 3D segmented EPI: a preliminary study
Authors: Li Qing , Jin Jin , Huang Yuxin , Wu Shiman , Fu Caixia , Yao Zhenwei
Published: November 2024

Motivation: Three-dimensional magnetic transfer ratio (MTR) measurement of the abdomen could be challenging due to the impact of motion. Goal(s): To validate if the 3D abdominal MTR could be measured under a single breath-hold using a 3D segmented EPI sequence. Approach: This study applied a 3D segmented EPI sequence with in- and through- plane accelerations to achieve 3D MTR measurement under a single breath-hold. Results: Three-dimensional MTR could be measured in a breath-hold of 17~18s, where 8s for a MT-on acquisition and 8s for a MT-off acquisition and a second or two for the system adjustment. Impact: Three-dimensional abdominal MTR measurement could be feasible in a single breath-hold using 3D EPI sequence. The gastrointestinal motility was noticed even at a time interval of 8s, which could impact the stability of the MTR measurement.
Improved Saturation Recovery-based Water-Specific T1 (T1W) Mapping
Authors: Fullerton Garrett , Muslu Yavuz , Tamada Daiki , Tang Jiayi , Thompson Richard , Hernando Diego , Reeder Scott
Published: November 2024

Motivation: T1 mapping is a promising biomarker of various chronic diseases, yet existing methods of T1 mapping suffer from high variability. Goal(s): Improve the overall performance of water-specific T1 (T1W) estimation with a rapid sequence for feasible implementation in body applications. Approach: Design and implement a multi-echo chemical shift-encoded MRI sequence with an optimized preparation pulse, optimized flip angle-modulation readout scheme, centric encoding, and joint parameter estimation to improve the performance of T1W estimation. Results: We successfully implemented the proposed sequence on an MRI system and demonstrated its feasibility for T1W mapping in the body. Impact: The proposed approach to chemical shift-encoded T1 mapping improves the overall performance of T1W estimation in the presence of confounding factors. By showing robustness to confounding factors, this helps improve T1 mapping as a clinically viable diagnostic tool.
Identification of cardiac phase in hyperpolarized 13C cardiac MRI using multiphase 1H MRI and unitized 3D patch-based reconstruction
Authors: Lin Sung-Han , Harrison Crystal , Derner Kelley , Erfani Zohreh , Park JaeMo
Published: November 2024

Motivation: The predetermined trigger delay may fail to acquire 13C images at an unstable heart rate and cause a misalignment between the cardiac phases depicted in structural and metabolic images. Goal(s): Aim to exploit the volumetric PBSR algorithm to establish an automated alignment workflow to identify the actual cardiac phase for HP 13C images. Approach: Image pairs of multiple 1H cardiac phases and single-phase 13C images will be reconstructed by 3D PBSR, and the pair with the best similarity/in-focus index will be elected. Results: The digital phantom and an in-vivo HP 13C cardiac image demonstrated the feasibility of cardiac phase detection. Impact: The currently proposed automated alignment workflow identifies the actual cardiac phase when the HP 13C images are acquired, and this technique will potentially benefit patients with arrhythmia or pediatric populations.
Diffusion Tensor Imaging using dual-echo steady-state method at Ultra-High-Field 7T MRI
Authors: Hsu Yi-Cheng , Liebig Patrick , Chu Ying-Hua
Published: November 2024

Motivation: High-resolution diffusion imaging is often perceived as less beneficial at ultra-high magnetic fields due to shorter T2 relaxation times. However, it is critical for detecting small lesions and assessing cartilage integrity. Goal(s): Our goal was to achieve high-resolution diffusion imaging that leverages the advantages of ultra-high magnetic fields. Approach: We employed diffusion-weighted DESS imaging to estimate diffusion tensor and fractional anisotropy in hand imaging. Results: DW DESS yielded superior image quality compared to conventional spin-echo EPI. It shows promise as a high-resolution diffusion imaging method, harnessing the potential benefits of ultra-high magnetic fields for musculoskeletal applications. Impact: Our study shows that DW DESS imaging offers high-resolution diffusion imaging benefits at ultra-high magnetic fields, particularly in musculoskeletal applications, with superior image quality and clinical relevance.
Motion robust MR-ARFI using timeseries single-shot spiral acquisition
Authors: Mohammadjavadi Morteza , Pauly Kim Butts , Glover Gary
Published: November 2024

Motivation: 2DFT MR-ARFI methods used to map ultrasound beam profiles can fail with small amounts of head motion because the signal is based on phase shifts from um-sized Acoustic Radiation Force displacement of tissue. Goal(s): To demonstrate that a single-shot timeseries method is more robust to bulk motion. Approach: Employing an efficient spiral redout trajectory, we compared the two methods’ ARFI images in a tissue-mimicking phantom undergoing controlled random translations. Results: The timeseries method demonstrated good quality tissue translation maps, while the 2DFT method failed with motions as small as ~50 microns RMS. Impact: Our single-shot method may allow routine use of MR-ARFI in humans for guiding US neuromodulation and US-based ablative therapy.
Characterizing cortical morphology alterations in ASD children aged 12 to 48 months
Authors: Feng Yuying , Zhu Linlin , Bai Pengxuan , Zhao Huifang , Du Xincheng , Liu Aoran , Shi Feng , Yang Jian , Jin Chao
Published: November 2024

Motivation: Investigate the age-specific anatomical abnormalities in the brains of children with ASD, and provide valuable insights into the development and progression of the disorder. Goal(s): Analyze the differences in cortical thickness, surface area, and volume between individuals with ASD and typically developing control (TDC) children aged 12-48 months. Approach: The study reconstructed the cerebral cortex from MRI images of children aged 12 to 48 months in ASD and TDC groups, analyzing the differences of cortical thickness, surface area and volume between groups. Results: The study identified different growth trajectories in cortical thickness, surface area and volume growth in ASD and TDC groups. Impact: The study highlights the significance of investigating the neural underpinnings of ASD, which can ultimately lead to more effective treatments and support for affected individuals and their families.