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Found 20 papers, 12 papers with code
Highly efficient non-rigid registration in k-space with application to cardiac Magnetic Resonance Imaging
The accelerated scans in such applications result in imaging artifacts that compromise the motion estimation.
Benchmarking Dependence Measures to Prevent Shortcut Learning in Medical Imaging
Medical imaging cohorts are often confounded by factors such as acquisition devices, hospital sites, patient backgrounds, and many more.
TotalSegmentator MRI: Robust Sequence-independent Segmentation of Multiple Anatomic Structures in MRI
Since the introduction of TotalSegmentator CT, there is demand for a similar robust automated MRI segmentation tool that can be applied across all MRI sequences and anatomic structures.
Attention-aware non-rigid image registration for accelerated MR imaging
The proposed method was evaluated on in-house acquired fully sampled and accelerated data of 101 patients and 62 healthy subjects undergoing cardiac and thoracic MRI.
MedShapeNet -- A Large-Scale Dataset of 3D Medical Shapes for Computer Vision
For the medical domain, we present a large collection of anatomical shapes (e. g., bones, organs, vessels) and 3D models of surgical instrument, called MedShapeNet, created to facilitate the translation of data-driven vision algorithms to medical applications and to adapt SOTA vision algorithms to medical problems.
Uncertainty Estimation and Propagation in Accelerated MRI Reconstruction
We demonstrate that our proposed method produces high-quality reconstructions as well as uncertainty quantification that is substantially better calibrated than several strong baselines.
Global k-Space Interpolation for Dynamic MRI Reconstruction using Masked Image Modeling
We evaluate our approach on 92 in-house 2D+t cardiac MR subjects and compare it to MR reconstruction methods with image-domain regularizers.
Motion-compensated MR CINE reconstruction with reconstruction-driven motion estimation
In cardiac CINE, motion-compensated MR reconstruction (MCMR) is an effective approach to address highly undersampled acquisitions by incorporating motion information between frames.
Learning-based and unrolled motion-compensated reconstruction for cardiac MR CINE imaging
Motion-compensated MR reconstruction (MCMR) is a powerful concept with considerable potential, consisting of two coupled sub-problems: Motion estimation, assuming a known image, and image reconstruction, assuming known motion.
Physics-Driven Deep Learning for Computational Magnetic Resonance Imaging
We consider inverse problems with both linear and non-linear forward models for computational MRI, and review the classical approaches for solving these.
LAPNet: Non-rigid Registration derived in k-space for Magnetic Resonance Imaging
Physiological motion, such as cardiac and respiratory motion, during Magnetic Resonance (MR) image acquisition can cause image artifacts.
Complementary Time-Frequency Domain Networks for Dynamic Parallel MR Image Reconstruction
The iterative model is embedded into a deep recurrent neural network which learns to recover the image via exploiting spatio-temporal redundancies in complementary domains.
Age-Net: An MRI-Based Iterative Framework for Brain Biological Age Estimation
Thus, a whole-body assessment of the BA does not reflect the deviations of aging behavior between organs.
Fully Automated and Standardized Segmentation of Adipose Tissue Compartments by Deep Learning in Three-dimensional Whole-body MRI of Epidemiological Cohort Studies
Methods: Quantification and localization of different adipose tissue compartments from whole-body MR images is of high interest to examine metabolic conditions.
Organ-based Chronological Age Estimation based on 3D MRI Scans
Individuals age differently depending on a multitude of different factors such as lifestyle, medical history and genetics.
Unsupervised Adversarial Correction of Rigid MR Motion Artifacts
Motion is one of the main sources for artifacts in magnetic resonance (MR) images.
Unsupervised Medical Image Translation Using Cycle-MedGAN
The proposed framework utilizes new non-adversarial cycle losses which direct the framework to minimize the textural and perceptual discrepancies in the translated images.
Retrospective correction of Rigid and Non-Rigid MR motion artifacts using GANs
Motion artifacts are a primary source of magnetic resonance (MR) image quality deterioration with strong repercussions on diagnostic performance.
A Machine-learning framework for automatic reference-free quality assessment in MRI
Therefore, the assessment or the ensurance of sufficient image quality in an automated manner is of high interest.
MedGAN: Medical Image Translation using GANs
Image-to-image translation is considered a new frontier in the field of medical image analysis, with numerous potential applications.
