ISSN# 1545-4428 | Published date: 19 April, 2024
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At-A-Glance Session Detail
   
AI/ML: Reconstructing Undersampled MR Data
Digital Poster
AI & Machine Learning
Thursday, 09 May 2024
Exhibition Hall (Hall 403)
13:45 -  14:45
Session Number: D-157
No CME/CE Credit

Computer #
4497.
17Perturbation Robust: Deep Adversarial-Equilibrium Unfolding Network for Magnetic Resonance Image Reconstruction
Tian Zhou1, Zhuoxu Cui1, Kun Shang1, and Dong Liang1
1Shenzhen Institute of Advanced Technology,Chinese Academy of Sciences, Shenzhen, China

Keywords: AI/ML Image Reconstruction, Data Processing

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.

4498.
18Detail-preserving self-supervised federated learning for undersamped MR image reconstruction
Juan Zou1,2, Cheng Li1, Ruoyou Wu1,3, Jing Yang1, Wenxin Fan1, and Shanshan Wang1,3
1Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, shenzhen, China, 2School of Physics and Optoelectronics, Xiangtan University, xiangtan, China, 3Peng Cheng Laboratory, shenzhen, China

Keywords: AI/ML Image Reconstruction, Data Processing

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.

4499.
19Cyclic-Consistency for Improved Self-Supervised Learning of Highly Accelerated MRI Reconstruction
Chi Zhang1,2, Omer Burak Demirel1,2, and Mehmet Akçakaya1,2
1Department of Electrical and Computer Engineering, University of Minnesota, Minneapolis, MN, United States, 2Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, MN, United States

Keywords: AI/ML Image Reconstruction, Machine Learning/Artificial Intelligence, Self-supervised, Reconstruction

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.

4500.
20DL based reconstruction method for an undersampled PROPELLER MRI data
Florintina C1, Sudhanya Chatterjee1, Rohan Patil1, Sajith Rajamani1, and Suresh Emmanuel Joel1
1GE HealthCare, Bangalore, India

Keywords: AI/ML Image Reconstruction, Image Reconstruction, PROPELLER

Motivation: Periodically Rotated Overlapping Parallel Lines with Enhanced Reconstruction (PROPELLER) is a popular MRI acquisition scheme used for clinical and research MRI data acquisition due to its robustness to motion. However, it is known to have long scan times.

Goal(s): Reduce scan time for PROPELLER scans to make it feasible for usage in regular clinical settings.

Approach: An unrolled algorithm based deep learning reconstruction method for PROPELLER scans has been proposed, which performs reconstruction at the blade level.

Results: Proposed method has been demonstrated to perform good reconstruction on single coil data for multiple anatomies and contrasts.

Impact: This method has the potential to reduce PROPELLER scan times and make it a popular choice for acquisition in clinical settings.

4501.
21NLCG-Net: A Model-Based Zero-Shot Learning Framework for Undersampled Quantitative MRI Reconstruction
Xinrui Jiang1 and Berkin Bilgic2,3
1School of Information Science and Technology, Fudan University, Shanghai, China, 2Department of Radiology, Harvard Medical School, Boston, MA, United States, 3Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, United States

Keywords: AI/ML Image Reconstruction, Machine Learning/Artificial Intelligence

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. 

4502.
22Aliasing Artefact Suppression in Machine Learning MRI Reconstruction for Random Phase-Encode Undersampling
TengFei Yuan1, Zhaoxin Kang1, Jieru Chi1, and Jie Yang2
1College of Electronics and Information, Qingdao University, Qingdao, China, 2College of Mechanical and Electrical Engineering, Qingdao University, Qingdao, China

Keywords: AI/ML Image Reconstruction, Image Reconstruction

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.

4503.
23Low-rank regularized implicit neural representation for k-space completion in fast MRI reconstruction
Guoyan Lao1, Ruimin Feng1, Yuyao Zhang2, and Hongjiang Wei1,3
1School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China, 2School of Infomation Science and Technology, ShanghaiTech University, Shanghai, China, 3The National Engineering Research Center of Advanced Magnetic Resonance Technologies for Diagnosis and Therapy (NERC-AMRT), Shanghai Jiao Tong University, Shanghai, China

Keywords: AI/ML Image Reconstruction, Machine Learning/Artificial Intelligence

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.

4504.
24Feature-Image Variational Network for Accelerated MRI Reconstructions
Ilias Giannakopoulos1, Patricia Johnson1,2,3, Jesi Kim1, Matthew Breen1, Yvonne Lui1,2,3, and Riccardo Lattanzi1,2,3
1Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, NYU Grossman School of Medicine, New York, NY, United States, 2Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, NYU Grossman School of Medicine, New York, NY, United States, 3Vilcek Institute of Graduate Biomedical Sciences, NYU Grossman School of Medicine, New York, NY, United States

Keywords: AI/ML Image Reconstruction, Machine Learning/Artificial Intelligence, Compressed Sensing, Parallel Imaging

Motivation: To improve learning-based MRI reconstructions to achieve higher clinical accuracy and detail retention.

Goal(s): To introduce modifications in the end-to-end (E2E) variational network (VarNet) to enhance its performance for undersampled MRI reconstructions.

Approach: We performed training in feature-space instead of image-space and included an attention layer that leverages the spatial locations of the Cartesian undersampling artifacts. We combined the new network with the E2E VarNet into Feature-Image VarNet to facilitate cross-domain learning.

Results: Reconstructions were evaluated using standard metrics and clinical scoring. Feature-Image VarNet outperformed all open-source models in the fastMRI leaderboard and preserved small anatomical details that were blurred with E2E VarNet.

Impact: We propose the Feature-Image (FI) variational network (VarNet), which performs cross-domain learning between feature and image spaces. FI VarNet significantly enhances the reconstruction quality of undersampled MRI and could enable clinically acceptable reconstructions at higher acceleration factors than currently possible.

4505.
25Integrating Quantitative Mapping into Physics-Based Deep Learning for Improved Accelerated Image Reconstruction
Catarina Carvalho1,2, Andreia S. Gaspar1, Rita G. Nunes1,3, and Teresa M. Correia2,3
1Institute for Systems and Robotics – Lisboa, Department of Bioengineering, Instituto Superior Técnico,Universidade de Lisboa, Lisbon, Portugal, Lisbon, Portugal, 2Center of Marine Sciences (CCMAR), Faro, Portugal, Faro, Portugal, 3School of Biomedical Engineering and Imaging Sciences, King’s College London, United Kingdom, London, United Kingdom

Keywords: AI/ML Image Reconstruction, Quantitative Imaging

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 data

Results: 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.

4506.
26Joint group sparsity-based motion-compensated deep learning reconstruction for 3D whole-heart joint T1/T2 mapping
Lina Felsner1,2, Andrew Phair1, Karl P. Kunze3, René M. Botnar1,4,5, and Claudia Prieto1,4,5
1School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom, 2King’s Institute for Artificial Intelligence, London, United Kingdom, 3MR Research Collaborations, Siemens Healthcare Limited, Camberley, United Kingdom, 4School of Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile, 5Millennium Institute for Intelligent Healthcare Engineering, Santiago, Chile

Keywords: AI/ML Image Reconstruction, Cardiovascular

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.

4507.
27Deep-learning-based optimization of k-space undersampling in self-supervised MRI reconstruction
Chun Liu1,2, Peng Hu1,2, and Haikun Qi1,2
1School of Biomedical Engineering, ShanghaiTech University, Shanghai, China, 2Shanghai Clinical Research and Trial Center, Shanghai, China

Keywords: AI/ML Image Reconstruction, Image Reconstruction

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.

4508.
28Accelerating MRI with Spiral Trajectory Optimization and Reconstruction using Diffusion Models
Trevor J Chan1, Jessie Dong1, Nabo Yu2, Hee Kwon Song3, and Chamith S Rajapakse3
1Bioengineering, University of Pennsylvania, Philadelphia, PA, United States, 2University of Pennsylvania, Philadelphia, PA, United States, 3Radiology, University of Pennsylvania, Philadelphia, PA, United States

Keywords: AI/ML Image Reconstruction, Machine Learning/Artificial Intelligence

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.

4509.
29Unsupervised reconstruction of undersampled 3D whole-heart Cartesian MRimaging using neural fields
Bruno Hernández1, Tabita Catalán2, Francisco Sahli1,2,3, Rene M Botnar1,2,4, and Claudia Prieto1,2,3,4
1Millennium Institute for Intelligent Healthcare Engineering, iHEALTH, Santiago, Chile, 2Millennium Nucleus For Applied Control And Inverse Problems, Santiago, Chile, 3School of Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile, 4School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom

Keywords: AI/ML Image Reconstruction, Machine Learning/Artificial Intelligence, Neural Fields, Undersampling reconstruction

Motivation: 3D MRI is fundamental for the assessment of cardiovascular   disease   but   suffers   from   long   scan   times. Undersampled reconstruction techniques have been proposed to accelerate the acquisition, but require long computational times for training.

Goal(s): To develop an unsupervised undersampled reconstruction approach based on implicit neural-field representations for 3D Cartesian MRI.

Approach: Dataset  was   acquired   using   image-based-navigator (iNAV). iNAV-based translational motion was corrected in k-space. Undersampled reconstruction was performed using a Neural-Fields. The method is evaluated on undersampled multi-coil data in comparison to a state-of-the-art.

Results: The feasible reconstruction results show similar image quality to the state-of-the-art reference, holding promise for future clinical evaluation.

Impact: The method proposed can be generalized to any context of reconstruction.  The use in digital devices is feasible, ensuring its possible medical use. Furthermore, this work methodology could allow the use of the net architecture given for other research contexts.

4510.
30Image-feature-understanding data consistency for under-sampled MRI reconstruction
Sha Wang1, Lijun Zhang1, Chunyao Wang1, and Zhenxi Zhang1
1Research and Development Center, Canon Medical Systems (China) Co., Ltd., Beijing, China

Keywords: AI/ML Image Reconstruction, Machine Learning/Artificial Intelligence

Motivation: The optimal selection of data consistency (DC) weight is task-dependent and a challenge in current deep learning unrolled reconstruction network which may result in compromised image quality, and thus deserves further investigations.

Goal(s): To propose a method to obtain adaptive data consistency weight which is superior to existing methods.

Approach: An image-feature-understanding data consistency (IFUDC) modulator is integrated into network to obtain adaptive DC weight based on input images.

Results: Image quality metrics (SSIM, PSNR) of proposed method are higher than those of existing method.

Impact: IFUDC is effective to modulate DC weight adaptively and helps to mitigate the difficulty in optimal DC weight selection.

4511.
31An End-to-End deep learning compressed sensing reconstruction model with adaptive shrinkage threshold
Yuan Lian1 and Hua Guo1
1Center for Biomedical Imaging Research, Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China

Keywords: AI/ML Image Reconstruction, Machine Learning/Artificial Intelligence

Motivation: Using deep learning methods to improve image quality and computation speed of compressed sensing reconstruction.

Goal(s): Develop a model-based deep learning model with adaptive threshold selection module to improve the reconstruction quality.

Approach: Introducing a new shrinkage function with adaptive threshold selection for Model-driven deep learning networks, and emploits End-to-End strategy for multicoil reconstruction.

Results: Experiments demonstrate the efficacy of End-to-End reconstruction strategy with sensitivity reconstruction module, and show that proposed adaptive threshold selection method can effectively reduce reconstruction errors.

Impact: We develop an End-to-End deep learning reconstruction network with adaptive threshold selection module. This network canenforce the performance of state-of-art model-based deep learning method for CS reconstruction, and achieve good reconstruction quality at R=8.