| Computer # |
| 1869.
| 1 | Dynamic imaging of the heart from scattering parameters using deep learning – an MR based feasibility study E.F. Meliado1,2,3, C.A. Louka2, C.A.T. van den Berg2,4, and B.R. Steensma1,2 1Department of Radiology, University Medical Center Utrecht, Utrecht, Netherlands, 2Computational Imaging Group for MR diagnostics & therapy, Center for Image Sciences, University Medical Center Utrecht, Utrecht, Netherlands, 3Tesla Dynamic Coils BV, Zaltbommel, Netherlands, 4Department of Radiotherapy, University Medical Center Utrecht, Utrecht, Netherlands Keywords: Image Reconstruction, Image Reconstruction, Cardiac motion, RF Arrays & Systems,Machine Learning/Artificial Intelligence Motivation: To enable more accessible and less costly monitoring of cardiovascular mechanical function. Goal(s): Perform a feasibility study into the potential of imaging the heart based on scattering parameters of an RF antenna array Approach: An MRI inspired reconstruction network was trained based on 150 in silico simulations of MRI segmented heart models. The method predicts 2D-maps of dielectric property changes and was tested in silico and in vivo on a healthy control. Results: In silico validation shows that it is feasible to reconstruct the shape and size of the heart, as well as left and right ventricular volumes, based on RF scattering measurements. Impact: This work shows the
feasibility of imaging the heart from differential scattering parameter
measurements and by using MRI inspired reconstruction. Preliminary results
warrant further investigation into acquiring paired MRI and RF scattering
measurements in human subjects. |
| 1870.
| 2 | Improved Reconstruction Speed for 5D Free Running Motion Resolved Using a Variable Projection Augmented Lagrangian (VPAL) Method Yitong Yang1, Matthias Chung2, Aws Hamid3, Jerome Yerly4, Davide Piccini5, Matthias Stuber4, and John N. Oshinski3 1Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, United States, 2Mathematics, Emory University, Atlanta, GA, United States, 3Radiology and Imaging Science, Emory University School of Medicine, Atlanta, GA, United States, 4Radiology, Lausanne University Hospital (CHUV)), Lausanne, Switzerland, 5Advanced Clinical Imaging Technology, Siemens Healthineers International AG, Lausanne, Switzerland Keywords: Image Reconstruction, Image Reconstruction Motivation: 5D free-running whole heart CMR offers CT-quality images but requires hours-long reconstruction time, preventing clinical usage. Therefore, a more efficient reconstruction algorithm is needed. Goal(s): We propose to use the advanced numerical algorithm to reduce the reconstruction time while preserving image quality. Approach: A variable projection augmented Lagrangian (VPAL) method for 5D motion-resolved image reconstruction was developed and compared with the state-of-the-art alternating direction method of multipliers (ADMM) on 15 5D free-running raw data sets. Results: When compared to the ADMM method, VPAL reduced the reconstruction time by 60%, preserved image similarity, had equivalent ejection fraction measurements, and had superior radiologist ratings. Impact: This study shows that using an advanced numerical algorithm for highly under-sampled MR reconstruction both reduces computational time and results in better image quality for diagnostics, bringing 5D free-running imaging closer to clinical usage. |
| 1871.
| 3 | 5D reconstruction exploiting spatial-motion-echo sparsity for accelerated free-breathing liver R2*/QSM Mungsoo Kang1, Ricardo Otazo1,2, Gerald Behr2, and Youngwook Kee1 1Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, United States, 2Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, United States Keywords: Image Reconstruction, Image Reconstruction, QSM Motivation: Conventional compressed sensing-based 4D respiratory motion-resolved image reconstruction techniques are limited in their ability to utilize correlations between echoes for free-breathing 3D multi-echo liver MRI, which is inherently a 5D imaging problem. Goal(s): Develop a 5D reconstruction algorithm to jointly exploit correlations between echoes and motion states. Approach: 5D reconstruction with sparsity constraints along the motion and echo dimensions was developed to reconstruct retrospectively undersampled k-space data and compared with the 4D reconstruction on one volunteer and one patient. Results: Compared to the 4D reconstruction, the proposed reconstruction showed comparable image quality to the reference (undersampling factor=1) and more reliable liver R2*/QSM values. Impact: Using the proposed 5D image reconstruction, the scan time of free-breathing 3D multi-echo liver MRI for R2*/QSM can be reduced while preserving image quality and providing reliable R2*/QSM values. |
| 1872.
| 4 | Cartesian spirals: An alternative to radial imaging for 4D-MRI in MR-guided radiotherapy Bastien Lecoeur1,2, Prashant Nair1, Rosalyne Westley3, Li Feng4, Uwe Oelfke1, Wayne Luk2, and Andreas Wetscherek1 1Joint Department of Physics, The Institute of Cancer Research and the Royal Marsden NHS Foundation Trust, Sutton, United Kingdom, 2Computing, Imperial College London, London, United Kingdom, 3The Royal Marsden NHS Foundation Trust, Sutton, United Kingdom, 4Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University Grossman School of Medicine, New York, NY, United States Keywords: Image Reconstruction, Pancreas Motivation: 4D-MRI could improve online MR-guided radiotherapy treatments on the MR-Linac, but long reconstruction times hinder clinical implementation. Goal(s): To reconstruct and evaluate respiratory-correlated and time-resolved 4D-MRIs from different acquisitions. Approach: We reconstructed 4D-MRIs using the XD-GRASP and GRASP-Pro algorithms from a clinical protocol (stack-of-stars) and a new Cartesian spiral sequence. We compared the reconstructions regarding diaphragm motion. Results: Respiratory-resolved 4D-MRIs were reconstructed under 4 minutes from current acquisitions. Time-resolved 4D-MRIs from clinical acquisitions showed significant artefacts limiting the achievable temporal resolution which can be overcome by using a different acquisition. Impact: Respiratory-correlated 4D-MRIs reconstruction times were
compatible with online radiotherapy constraints for pancreatic cancer patients
on the MR-Linac. Reaching high temporal resolutions in reconstructing
time-resolved 4D-MRIs is not currently possible from the current clinical
protocol. |
| 1873.
| 5 | Improving reconstruction of multiband real-time MRI Isaac Watson1, Elisa Zamboni2, James McStravick3, David Mitchell4, Martin Trefzer1, Angelika Sebald4, and Aneurin Kennerley3 1School of Physics, Engineering and Technology, University of York, York, United Kingdom, 2School of Psychology, University of Nottingham, Nottingham, United Kingdom, 3Department of Sport and Exercise Sciences, Manchester Metropolitan University, Manchester, United Kingdom, 4York Cross-disciplinary Centre for Systems Analysis, University of York, York, United Kingdom Keywords: Image Reconstruction, Image Reconstruction Motivation: Static sequences cannot capture movement and current real-time MRI sequences cannot provide volumetric coverage. Our motivation is too bridge this gap. Goal(s): This work aims to extend a standard real-time MRI sequence to use multiband excitation to improve real-time volumetric coverage for diagnostic purposes. Approach: Development of a radial sequence incorporating multiband excitation. A compressed sensing reconstruction algorithm is also implemented. Phantom and in-vivo images are acquired and analysed to assess image quality and reconstruction times. Results: We present both phantom and in-vivo images acquired using this approach. The proposed reconstruction approach results in a higher SNR compared to conventional reconstruction. Impact: We present improvements in real-time MRI by adding multiband
excitation, allowing for the dynamic imaging of complex movements across a
volume. This will improve the monitoring and diagnostic capabilities of
real-time MRI, for example in maxillofacial surgery or orthopaedics. |
| 1874.
| 6 | Dynamic Latent Variable Modeling for Improved Cardiac MRI Reconstruction Shuo Zhou1,2, Sen Jia1,2, Jing Cheng1,2, Zhuoxv Cui1,2, Yanjie Zhu1,2, Dong Liang1,2, Haifeng Wang1,2, and Yihang Zhou1,2 1Shenzhen Institute of Advanced Technology,Chinese Academy of Sciences, Shenzhen, China, 2Chinese Academy of Sciences, Beijing, China Keywords: Image Reconstruction, Heart Motivation: The beating of the heart is predictable, and existing methods mainly focus on sparsity and low rank, ignore the predictability. Goal(s): Our goal is to improve the quality of dMRI reconstruction through predictability. Approach: Introduced a method to extract predictability latent vectors and reconstruct images based on it. Results: We used highly undersampled data for reconstruction and compared it with L+S,the experimental results indicate that we have achieved better reconstruction results than L+S. Impact: This work use predictability for dMRI reconstruction without the use of sparsity and low rank.It is possible to introduce a new perspective for the reconstruction of dynamic images. |
| 1875.
| 7 | Measuring Spatiotemporal Resolution in Real-Time MRI Chin-Cheng Chan1 and Justin P. Haldar1 1Signal and Image Processing Institute, Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, United States Keywords: Image Reconstruction, Image Reconstruction, local perturbation responses, point-spread functions, resolution analysis, dynamic imaging, real-time MRI Motivation: Real-time MRI can provide powerful insights into dynamic processes, but practical experimental limitations have led to the widespread use of undersampled data. While advanced reconstruction methods can mitigate undersampling artifacts, these methods are unlikely to be perfect, and their rigorous validation has been a longstanding open problem. Goal(s): To introduce a new reference-free approach for evaluating real-time MRI results. Approach: We introduce a framework for measuring spatiotemporal resolution in real-time MRI, based on the propagation of spatiotemporal perturbations through image reconstruction. Results: The proposed approach is sensitive to spatiotemporal resolution features, and provides valuable new information for the interpretation of real-time MRI results. Impact: The proposed framework enables measurement of spatiotemporal resolution, providing new information that is important for the interpretation of real-time MRI results, and can also be useful for the development/tuning of acquisition and reconstruction methods. |
| 1876.
| 8 | Improving the spatial-temporal fidelity and resolution of dynamic MRI using complex-valued spatial-temporal super-resolution method Duohua Sun1, Silu Han1, Laurel Dieckhaus1, Elizabeth Hutchinson1, and Nan-kuei Chen1 1The University of Arizona, Tucson, AZ, United States Keywords: Image Reconstruction, Image Reconstruction Motivation: To improve image quality and spatial-temporal resolution of T2*-weighted dynamic MRI in applications such as MRI-guided focused ultrasound surgery, resulting in more effective diagnostic and treatment outcomes. Goal(s): Our goal is to present an innovative complex-valued spatial-temporal multi-band super-resolution technique, enhancing spatial-temporal resolution, signal-to-noise ratio (SNR) and minimizing susceptibility artifacts in T2*-weighted dynamic MRI. Approach: We highlight the susceptibility artifact through a hybrid simulation and design a multi-band-super-resolution sequence. We perform an experiment on phantom and the acquired data is reconstructed using our proposed method. Results: Hybrid simulation and reconstruction on phantom data demonstrate improvements in SNR, spatial-temporal-resolution, and reduction of susceptibility artifacts. Impact: Conventional magnitude-based super-resolution
reconstruction exhibits signal loss due to susceptibility effects. Our proposed
method integrating phase in reconstruction shows an improvement in SNR, spatial-temporal
resolution, and a reduction of susceptibility artifacts. |
| 1877.
| 9 | Characterizing laryngeal dynamics during voicing and breathing with real-time multi-slice variational manifold learning Rushdi Z. Rusho1, Matthew R. Hoffman2, Christopher S. Apfelbach3, Wahidul Alam1, Hiroyuki Oya4, Matthew A. Howard4, David Meyer 5, Mathews Jacob6, and Sajan Goud Lingala1,7 1Roy J. Carver Department of Biomedical Engineering, The University of Iowa, Iowa City, IA, United States, 2Department of Otolaryngology, The University of Iowa, Iowa City, IA, United States, 3Department of Communication Sciences and Disorders, The University of Iowa, Iowa City, IA, United States, 4Department of Neurosurgery, The University of Iowa, Iowa City, IA, United States, 5Janette Ogg Voice Research Center, Shenandoah University, Winchester, VA, United States, 6Department of Electrical and Computer Engineering, The University of Iowa, Iowa City, IA, United States, 7Department of Radiology, The University of Iowa, Iowa City, IA, United States Keywords: Image Reconstruction, Image Reconstruction Motivation: Visualizing the movement of laryngeal muscles during voicing and breathing is important for understanding the mechanics of speech production. Goal(s): The goal is to improve our understanding of speech production by visualizing gross-vocal fold movements due to laryngeal muscle contractions. Approach: We proposed a multi-slice non-gated spiral sparse sampling strategy, and variational manifold based reconstruction scheme for dynamic laryngeal MR imaging at high spatio-temporal resolution. Results: Our proposed method can capture gross vocal fold motions (e.g., adduction, abduction, elongation, shortening) during various voicing and breathing at both high spatial (1.5 mm2) and high temporal resolutions (36 ms/frame). Impact: Otolaryngologists, voice and neuroscientists interested in larynx physiology and disorders pertaining to breathing and phonation. |
| 1878.
| 10 | MULTI-PHASE SPATIAL RECONSTRUCTION METHOD FOR ACCELERATED DYNAMIC CONTRAST-ENHANCED MRI Alexander Mertens1 and Hai-Ling Margaret Cheng1,2,3 1The Edward S. Rogers Sr. Department of Electrical and Computer Engineering, University of Toronto, Toronto, ON, Canada, 2Institute of Biomedical Engineering, Toronto, ON, Canada, 3Ted Rogers Centre for Heart Research, Translational Biology & Engineering Program, University of Toronto, Toronto, ON, Canada Keywords: Image Reconstruction, Perfusion, Dynamic Contrast Enhancement, Real-Time MRI Motivation: Dynamic contrast-enhanced (DCE) MRI requires both high spatial and high temporal resolution for accurate quantification and delineation. In practice, temporal resolution is often traded for spatial resolution and volume coverage. Goal(s): To develop a reconstruction method that offers both high spatial and temporal resolution. Approach: We describe a two-stage reconstruction technique that consistently produces high-temporal, high-spatial resolution estimates of the ground truth data and is more accurate than current state-of-the-art methods. Results: The proposed method achieves high quality reconstruction with as few as one acquired spoke per frame when radial sampling is used. Impact: The theoretical and practical success of the spatial subspace method over
temporal subspace methods encourages further research on the topic. Furthermore,
reconstruction at 1 spoke per frame enables larger imaging volumes, thus more capable
imaging tools. |
| 1879.
| 11 | Parallel MRI accelerator: The MPSoC design for real time image reconstruction Abdul Basit1, Omair Inam1, and Hammad Omer1 1COMSATS UNIVERSITY ISLAMABAD, Islamabad, Pakistan Keywords: Image Reconstruction, Cardiovascular Motivation: Real-time MRI requires efficient data acquisition and low latency image reconstruction along high temporal resolution. The pMRI method known as GRAPPA, offers advantage in terms of fast data acquisition. Goal(s): However, large computational requirements of GRAPPA limit its performance in real-time clinical settings. Approach: This paper presents a novel MPSoC based hardware accelerator which combines 32-bit FPGA based accelerator module with multiple DSP engines and on-chip ARM processor, to provide sufficient computational resources for GRAPPA. Results: The results using in-vivo cardiac datasets i.e. 18-receiver coils, show that the proposed accelerator reconstructs cardiac images at ∼35 frames-per-second without degrading the image quality. Impact: The proposed accelerator is capable to reconstruct 35 frames in one
second as compared to the CPU-based counterparts which can only reconstruct 2
frames/second for a given GRAPPA reconstruction setting in our experiments. |
| 1880.
| 12 | Plug and play truncated low rank optimization combined iterative soft thresholding (PNPT) for dynamic MRI reconstruction Runyu Yang1, Haozhong Sun1, Haokun Li1, and Huijun Chen1 1Center for Biomedical Imaging Research, Department of Biomedical Engineering, Tsinghua University, Beijing, China Keywords: Image Reconstruction, Heart, Reconstruction Motivation: Try to obtaining high spatial and temporal resolution image in dynamic MRI. Goal(s): The plug and play truncated low rank optimization combined iterative soft thresholding(PNPT-) embedded method was proposed to reconstructe data and tested to accurately estimate the rank function for dMRI reconstruction. Approach: The PNPT- method uses truncated nuclear norm combined iterative soft thresholding to assign different shrink values to different singular vectors, enabling the preservation of essential image information while effectively eliminating noise. Results: The embedded method was proposed to reconstructe data and tested to accurately estimate the rank function for dMRI reconstruction. Impact: The proposed method demonstrated reduced
aliasing artifacts compared to other methods and yielded better reconstruction
images in cine and perfusion data. Consequently, it provided more
accurate and efficient image information, benefiting the clinical diagnosis of
heart-related diseases. |
| 1881.
| 13 | Zero-shot self-supervised deep learning reconstruction for abdominal DCE MR multitasking Zihao Chen1,2,3, Ruofan Sheng4, Kaipu Jin4, Shihong Han5, Jian Xu1, Mengsu Zeng4, Debiao Li2,3, and Qi Liu1 1UIH America, Inc., Houston, TX, United States, 2Cedars-Sinai Medical Center, Los Angeles, CA, United States, 3University of California, Los Angeles, Los Angeles, CA, United States, 4Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, China, 5United Imaging Healthcare, Shanghai, China Keywords: Image Reconstruction, Machine Learning/Artificial Intelligence, MR Multitasking, DCE MRI, self-supervised learning, zero-shot learning Motivation: Deep learning (DL) MR multitasking reconstruction can reduce the reconstruction time, but previous methods are supervised learning, which may learn artifacts from the reference images. Goal(s): Our goal was to develop a DL reconstruction method that can improve image quality beyond supervised DL and conventional iterative reconstruction. Approach: We developed a zero-shot self-supervised deep learning method for DCE MR multitasking reconstruction. Results: With shorter reconstruction time than conventional iterative reconstruction, the proposed method obtained better image quality than both supervised DL and conventional iterative reconstruction methods. Impact: With
the proposed method, DCE MR multitasking can have better image quality with
shorter reconstruction time than previous iterative reconstruction, which is
essential for the potential clinical application of the motion-resolved and
high spatial-temporal-resolution abdominal DCE MR multitasking. |
| 1882.
| 14 | Extended Capabilities for multi-dimensional MR for Abdominal Radiation Therapy Planning Junzhou Chen1, Qingle Kong1, Yang Chen1, Jie Deng2, Wensha Yang3, Debiao Li4, Anthony Christodoulou5, and Zhaoyang Fan1 1University of Southern California, Los Angeles, CA, United States, 2UT Southwestern Medical Cnter, Dallas, TX, United States, 3University of California, San Francisco, San Francisco, CA, United States, 4Cedars-Sinai Medical Center, Los Angeles, CA, United States, 5University of California, Los Angeles, Los Angeles, CA, United States Keywords: Image Reconstruction, Radiotherapy Motivation: Motion resolution of the original Multitasking 4D RTP MR sequence is not robust enough. Goal(s): To enhance motion robustness and introduce T1/T2 mappings of 4D abdominal RTP MR protocol. Approach: Estimation of explicit motion deformable vector fields to correct. Results: The proposed approach dramatically enhance motion robustness of MR multitasking in 4D MRI. Impact: The straight-forward explicit motion vector field estimation and application provides effective and robust motion correction . |
| 1883.
| 15 | A hybrid PCA acceleration method for rapid real-time 2D target tracking Mark Wright1, Gawon Han1, Jihyun Yun1,2, Eugene Yip1,2, Gino Fallone1,2, and Keith Wachowicz1,2 1Oncology, University of Alberta, Edmonton, AB, Canada, 2Medical Physics, Cross Cancer Institute, Edmonton, AB, Canada Keywords: Image Reconstruction, Radiotherapy, real-time, MR-Linac Motivation: Most standard MR sequences are too slow for real-time applications. Accelerated acquisitions are one method to achieve the required frame rates. Neural-network reconstruction and parallel imaging are two methods that can achieve the necessary frame rates but are computationally expensive or require extensive coil arrays. Goal(s): To develop an accelerated reconstruction method suitable for real-time applications which is computationally inexpensive and simple to implement. Approach: Our method was tested retrospectively on lung, liver, and prostate patients for image quality and auto-contourability using a range of metrics. Results: Image quality and contourability improved over similar methods while maintaining good reconstruction times for real-time applications. Impact: An accelerated PCA-based reconstruction method
was developed suitable for real-time applications, and in particular, target
tracking. It has improved image quality and auto-contourability compared to similar
methods while still maintaining simplicity in its implementation (low-cost
computing, single coil arrays). |
| 1884.
| 16 | MOTIF-CORD: Motion Integrated Forward Model with Co-Estimated Coil Sensitivity and Regularization by Denoiser for Free Breathing Liver DCE-MRI Chunxu Guo1, Sihao Chen1, Weijie Gan1, Yuyang Hu1, Jiaming Liu1, Cihat Eldeniz1, Yasheng Chen1, Ulugbek S. Kamilov1, Tyler J. Fraum1, and Hongyu An1 1Washington University in St. Louis, St. Louis, MO, United States Keywords: Image Reconstruction, Motion Correction Motivation: Dynamic contrast-enhanced (DCE) MRI faces challenges from respiratory motion and sub-optimal DCE contrast timing. Free-breathing DCE with high temporal resolution is desirable. Goal(s): We aim to reconstruct respiratory motion-free and high temporal resolution DCE-MRI. Approach: We proposed a Motion Integrated Forward model using motion vector fields and jointly estimated coil sensitivity to reconstruct severely under-sampled DCE data. Furthermore, we utilized a model-based deep learning framework to amalgamate the knowledge of the measurement model and the denoising prior. Results: The proposed method provided deformable motion vector fields, coil-sensitivity maps, and sharp motion-free DCE images without artifacts using highly under-sampled data. Impact: This method provides good quality free-breathing liver DCE MR images with high temporal resolution. It will eliminate the need for breath-holding. Moreover, continuous acquisition and high temporal resolution reconstruction mitigate the problem of sub-optimal DCE contrast in clinical diagnosis. |