| 08:15 |  | Screen Number: 1 1184. A quest for deep learning MR image reconstruction loss functions in k-spaceS. Xu, W. Huang, K. Hammernik, M. Terpstra, D. Rueckert, S. Gatidis, T. Kuestner
Medical Image and Data Analysis (MIDAS.lab), Department of Diagnostic and Interventional Radiology, University of Tuebingen, Tübingen, Germany Impact: We propose a loss function that is more suitable for k-space, taking into account its characteristics and enhancing the accuracy of k-space regression. This loss function can be applied to any tasks that require calibration of k-space similarity. |
| 08:17 |  | Screen Number: 2 1185. Resolving ambiguous space: Leveraging side information with deep learning to extend the limits of MR image reconstructionA. Atalik, S. Chopra, D. Sodickson
New York University, New York, United States Impact: By leveraging readily available sources of information which may not generally be used for image reconstruction, our approach reduces ambiguities, enabling more accurate solutions even with highly-sparse measurements. |
| 08:19 |  | Screen Number: 3 1186. The role of nonlinear activations in Fourier-domain neural networks: Noise resilience, regularization, blurring and autocorrelation artifactP. Dawood, F. Breuer, I. Homolya, M. Gram, P. M. Jakob, M. Zaiss, M. Blaimer
University Hospital Erlangen, Erlangen, Germany Impact: This work
illuminates the role of nonlinearity in robust artificial neural networks for
Fourier-domain interpolation via activated convolution layers at limited
training data and shows possible implications of noise resilience, image
blurring and autocorrelation artifacts in the image center. |
| 08:21 |  | Screen Number: 4 1187. Efficient deep-learning-based reconstruction of Ferumoxytol-enhanced whole-heart 5D cardiac MRIK. Borsos, A. Ogier, C. Roy, L. Romanin, M. Stuber, M. Prsa, T. Küstner, J. Yerly
Lausanne University Hospital, Lausanne, Switzerland Impact: FreeNet
demonstrates potential for rapid inline reconstruction of motion-resolved
free-running CMR images for the first time. Our work is a preliminary step
towards addressing current roadblocks, bringing “single-click” free-breathing
CMR to wider patient populations. |
| 08:23 |  | Screen Number: 5 1188. SELFIE: SElf-supervised Learning for Fast dynamIc golden-anglE radial MRI reconstruction with auto-extracted representationsM. Schellenberg, A. Mekhanik, V. Murray, R. Otazo
Memorial Sloan Kettering Cancer Center, New York, United States Impact: SELFIE can achieve comparable performance to supervised deep learning without the limitation of using a compressed sensing reference, which is promising for challenging clinical applications where acquiring a reference is impractical. |
| 08:25 |  | Screen Number: 6 1189. 4D Cardiac Shape Reconstruction Using Image-Aided Neural Signed Distance Correction FieldsZ. Zhang, Z. Liu, Z. Zhang, Z. Cui
ShanghaiTech University, Shanghai, China Impact: The 4D cardiac shape
sequence of a new patient can be obtained using our method with less time
required and the best shape precision among the deep learning-based methods so
far. |
| 08:27 |  | Screen Number: 7 1190. FlowMRI-Net: A generalizable self-supervised physics-driven 4D Flow MRI reconstruction network for aortic and cerebrovascular applicationsL. Jacobs, M. Piccirelli, V. Vishnevskiy, S. Kozerke
ETH and University Zurich, Zurich, Switzerland Impact: FlowMRI-Net
facilitates higher undersampling factors than the current state-of-the-art for aortic
and cerebrovascular 4D flow MRI within clinically feasible reconstruction times,
improving clinical adaptation particularly for cerebrovascular applications
which are otherwise too time-consuming. |
| 08:29 |  | Screen Number: 8 1191. Two-minute whole-heart MRI at 0.9 mm3 using a combined super-resolution and motion-corrected undersampled deep-learning reconstructionA. Phair, S. Littlewood, A. Fotaki, T. Fletcher, L. Felsner, C. Prieto, R. Botnar
King's College London, London, United Kingdom Impact: Extending
Super-MoCo-MoDL for combined super-resolution and undersampled reconstruction
allows sharp whole-heart 3D 0.9-mm3 isotropic-resolution images to
be obtained from low-resolution 2-minute scans with 18-fold overall
acceleration. This represents a promising approach towards achieving fast
high-resolution 3D clinical CMR. |
| 08:31 |  | Screen Number: 9 1192. Spatiotemporal Deformable Unrolling Networks for Cardiac Cine MR ReconstructionY. Zhu, J. Cheng, Z-X Cui, D. Gan, Y. Wang, J. Zeng, C. Wang, D. Liang
University of Nottingham Ningbo China, Ningbo, China Impact: The proposed method SDUN provides
an effective scheme for accurately capturing and learning complex cardiac
motions and deformations in cardiac cine MR reconstruction through employing a
deformable and adaptive CNN in proximal network. |
| 08:33 |  | Screen Number: 10 1193. A Multi-task Implicit Neural Representation Model for Reconstruction, Motion Field Estimation, and Segmentation of Cardiac Cine MRIP. D. Negho, N. Vogt, P-A Vuissoz, J. Oster
Université de Lorraine, Nancy, France Impact: The proposed model has the potential to speed up cardiac function assessment, by diffusing a single manual segmentation across successive temporal frames and slices, while also providing motion information and offering the possibility to reconstruct a super-resolved volume. |
| 08:35 |  | Screen Number: 11 1194. High spatial resolution cardiac T1 mapping on 1.5T and 3T utilizing deep learning-based image reconstructionD. Amsel, J. Wetzl, D. Giese, R. Gebker, C. Tillmanns, A. Lingg, P. Krumm, K. Chow, T. Küstner
University of Tuebingen, Tuebingen, Germany Impact: The acquisition of higher spatial
resolution T1 maps is achieved for both 1.5T and 3T systems. The proposed
method may improve the detection of small focal lesions without increasing the
required scan time or breath hold duration. |
| 08:37 |  | Screen Number: 12 1195. Machine Learning-Optimized Sampling Enables High-Resolution 3D MR Fingerprinting for Dynamic Quantification of Solute Transport in CSFY. Zhu, G. Wang, J. Zhu, R. Boyacioglu, C. Flask, X. Yu
Case Western Reserve University, Cleveland, United States Impact: We present a novel method to leverage machine learning to improve sampling for MR fingerprinting. This method achieves simultaneous mapping of T1 and T2 in the whole mouse brain at 200-µm isotropic resolution in 4.3 min. |
| 08:39 |  | Screen Number: 13 1196. Enhance-then-Synthesize: A Deep Learning Acceleration Pipeline for Spinal MRI, Reducing Scan Time by 60%Z. Zhang, J. Li, S. Jiang, Y. Xia, B. Jiang, L. Wang, L. Xiang, L. Fan, S. Liu
Subtle Medical Inc., Shanghai, China Impact: The DL-based enhance-then-synthesize pipeline reduced the scanning time for spine MRI protocols up to 60%, while delivering image quality comparable to or higher than that of the standard scanning sequence. The generated sequences proved to be viable alternatives for diagnosis. |
| 08:41 |  | Screen Number: 14 1197. Synthetic FDG PET from functional MRI for epilepsy: development and external validationC. Yao, J. Lu
Xuanwu Hospital Capital Medical University, Beijing, China Impact:
- Deep learning can generate high-fidelity
synthetic PET based on functional MRI
-
Radiologists confirm high imaging fidelity
between synthetic and actual PET
-
The imaging features of synthetic PET are
similar to actual PET
-
Synthetic PET improves epilepsy diagnosis
and prognosis
|
| 08:43 |  | Screen Number: 15 1198. Training Diffusion Probabilistic Models with Limited Data for Accelerated MRI Reconstruction with Application to Stroke MRIS. Kumar, Y. Arefeen, H. Saber, J. Tamir
The University of Texas at Austin, Austin, United States Impact: Training diffusion probabilistic models with limited data across various MRI contrasts holds substantial potential to accelerate diverse MRI protocols, addressing a critical unmet need in time-sensitive care scenarios, such as stroke diagnosis. |
| 08:45 |  | Screen Number: 16 1199. Universal MR Image Restoration with Diffusion Models as Plug-and-Play PriorsM. Mostapha, R. Miron, N. Janardhanan, M. Nadar, O. Darwish, T. Huelnhagen, T. Würfl, H. Chandarana, D. Grodzki, R. Schneider
Siemens Healthineers, Princeton, United States Impact: The Proposed Diffusion PnP
method enables fast and accurate MRI reconstructions using a pre-trained
diffusion prior, without the need for fine-tuning or retraining. This approach
demonstrates strong potential for diverse clinical applications in MRI. |
| 08:47 |  | Screen Number: 17 1200. Harmonizer: A Deep Learning Framework for Transforming Low-resolution Clinical dMRI to Research-grade QualityS. Cetin-Karayumak, R. Zurrin, W. Consagra, L. O'Donnell, Y. Rathi
Harvard Medical School and Brigham and Women's Hospital, Somerville, United States Impact: his work enhances clinical dMRI data for multi-site neuroimaging studies, enabling high-quality, research-compatible analyses. The Harmonizer algorithm opens opportunities to study various disorders and pathologies using clinical data, supporting detailed white matter analyses and cross-site comparisons. |
| 08:49 |  | Screen Number: 18 1201. 7T MRI-Synthesized Iron and Myelin Histology by Deep LearningS. Pittayapong, S. Hametner, B. Bachrata, W. Bogner, R. Höftberger, G. Grabner
Carinthia University of Applied Sciences, Klagenfurt, Austria Impact: Our
research shows that deep learning can synthesize myelin and iron stainings from
multi-contrast MRI. This technique enhances understanding of brain development,
function, and diseases, promising advances in medical imaging and histology. |
| 08:51 |  | Screen Number: 19 1202. Super-Resolution Image Enhancement for 3D Morphological Lung MRI with bSTAR at 0.55TP. Panos, M. Pradella, K. Hostettler, O. Bieri, G. Bauman
University of Basel, Allschwil, Switzerland Impact: Due to physical and physiological challenges lung MRI is currently rarely used in clinical routine1. Our study demonstrates that a deep learning-based super-resolution image enhancement results in improved visualization of MRI lung morphology without introducing new anatomical structures. |
| 08:53 |  | Screen Number: 20 1203. Volumetric Measurement Comparisons for Deep Learning Improved Ultra-Low Field MRIK. T. Islam, S. Zhong, P. Zakavi, H. Kavnoudias, S. Farquharson, G. Durbridge, M. Barth, K. McMahon, P. Parizel, A. Dwyer, G. Egan, M. Law, Z. Chen
Monash University, Clayton, Australia Impact: SynthSR and LoHiResGAN were evaluated for their unique approaches to enhancing ultra-low-field MRI images. Their ability to improve volumetric accuracy highlights their potential to support and expand access to high-quality neuroimaging in settings with limited access to high-field MRI. |
| 08:55 |  | Screen Number: 21 1204. Deep learning reconstruction based rapid T2-weighted imaging improved the image quality for primary rectal cancer stagingL. Zhu, B. Shi, K. Wang, W. Feng, J. Dai, Y. Xia, H. Zhang
Ruijin Hospital, Shanghai Jiao Tong University of Medicine, Shanghai, China Impact: The application of DLR would be beneficial for rectum T2WI in terms of shorten scan time, improved tumor boundary delineation, and better node characteristic presentation which are important for primary rectal cancer TN staging. |
| 08:57 |  | Screen Number: 22 1205. An End-to-End Deep Learning Method for Reconstructing SMS-PI Accelerated Musculoskeletal MRIM. Mostapha, G. Koerzdoerfer, E. Raithel, N. Janardhanan, M. Nadar, J. Fritz
Siemens Healthineers, Princeton , United States Impact: Proposed methods facilitate
the reconstruction of 8-fold accelerated 2D-TSE-MR images across various planes,
contrasts, and MSK regions. Preliminary results indicate the feasibility of DL
reconstruction at 12-fold acceleration, potentially allowing for significant
speed-ups compared to the slower standard of care. |
| 08:59 |  | Screen Number: 23 1206. 2-Minute 3D FSE Knee MRI with 10-fold accelerated Sonic DL – Rapid morphometric and qualitative assessment of Cartilage and MeniscusL. Carretero, B. Nunes, X. Zhu, E. Sanchez-Lacalle, D. Sundaran, J. Dholakia, M. Fung, M. Padron
GE HealthCare, Madrid, Spain Impact: Sonic DL technique enables
10-fold acceleration of 3D FSE, delivering accurate cartilage and meniscus morphometry. Its superior
sharpness and lesion conspicuity offer potential for routine clinical use,
improving both diagnostic confidence in 3D knee imaging and patient throughput. |
| 09:01 |  | Screen Number: 24 1207. Improved Image Quality, Diagnostic Performance, and Reading Efficiency Based on Deep Learning-Reconstructed Accelerated Rectal MRIW. Peng, S. wang, H. zhang
National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Beijing, China Impact: This study offered
a comprehensive and viable perspective on the application of DLR in rectal MRI,
which facilitated improved image quality and reading efficiency, while reducing
acquisition time. Moreover, it enhanced the accuracy of T-staging for junior
radiologists. |
| 09:03 |  | Screen Number: 25 1208. Micro Transformer Cascades for Accelerated Multi-Coil MRI Reconstruction with Overlapped SwinV2 AttentionT. Rahman, A. Bilgin, S. Cabrera
The University of Texas at El Paso, El Paso, United States Impact: A smaller than conventional SwinV2-Micro Transformer
architecture is proposed to facilitate the incorporation of powerful overlapped
attention in Transformer cascades for MRI reconstruction. This allows the
formation of longer cascades enabling higher reconstruction quality at
different acceleration factors. |