Advanced MRI Techniques and Applications

Neueste Paper

• A Dual-Stream Regulated Reconstruction and Segmentation Network with Hierarchical Artifact-Prior Modeling for Ultra-Low-Field Pediatric Neuroimaging

  Bahram Jafrasteh, Leo Milecki, Qingyu Zhao · 18. September 2026

  Automated quality assessment, enhancement, and segmentation of multiple structures in $0.064\,\mathrm{T}$ ultra-low-field pediatric MRI are limited by a low signal-to-noise ratio, weak anatomical boundaries, and frequent artifacts. We present a unified framework for the LISA 2026 Challenge that perf…

• Fast Cross-Strength Multi-Contrast Brain MRI Translation using Latent Bridge Matching

  Siddharth Srivastava, Till Bretschneider · 18. September 2026

  Magnetic Resonance Imaging (MRI) acquired at different field strengths exhibits pronounced variation in noise, resolution, homogeneity, and contrast, which limits comparability across acquisition settings and complicates downstream analysis. We address this with a unified conditional model for contr…

• Attention-guided super-resolution of 4D flow MRI in carotid arteries

  Ali Mokhtari, Dominik Obrist · 7. September 2026

  Four-dimensional (4D) flow magnetic resonance imaging (MRI) is a powerful non-invasive technique for visualizing and quantifying complex blood flow patterns in vivo. Despite its clinical promise, broader adoption is limited by low spatial resolution and sensitivity to noise, which restrict accurate …

• Sharpening the Ensemble: An SSIM-Aligned Residual Refiner for Brain-MRI Inpainting Post-Processing

  Kubilay Ka\u{g}an K\"om\"urc\"u, \.Ilkay \"Oks\"uz · 4. September 2026

  Brain-MRI inpainting replaces a masked region of a scan with synthesized, anatomically plausible healthy tissue, so that analysis tools built for healthy brains can be applied to images they would otherwise reject. On the BraTS local-synthesis benchmark, which ranks submissions on the structural sim…

• RARF: Region-Aware Rectified Flows for 3D Brain MRI Inpainting

  Tomas Guija-Valiente, Blanca Rodriguez-Gonzalez, Norberto Malpica, Angel Torrado-Carvajal · 4. September 2026

  Medical image inpainting has the potential to improve automated brain MRI analysis by reconstructing healthy tissue within pathological regions. We introduce RARF, a task-agnostic region-aware rectified flow framework for masked data generation. We instantiate the framework for 3D brain MRI inpainti…

• Conditional Flow Matching for Cross-Field MRI Harmonisation

  Baris Imre, Aram Salehi, Levente Baljer, Andrew Webb, Marius Staring, Efe Ilicak · 2. September 2026

  Magnetic resonance images of the same subject look markedly different across field strengths, which complicates the comparison and pooling of data across sites. We address cross-field brain-MRI translation for the MRIxFields2026 challenge, and in particular its Task~3: a single model that translates…

• FlowMoDL: Model-Based Deep Learning with Conjugate-Gradient Data Consistency for Highly Accelerated 4D Flow MRI Reconstruction

  Tristan Gottwald, Michelle Bruch, Mubashir-Ul Hassan, Fatma Alickovic, Milan Kloiber, Daniel Tenbrinck, Torsten Panholzer, Melanie Schaller, Jana Hutter · 27. August 2026

  We present FlowMoDL, an unrolled neural network for highly accelerated 4D flow MRI reconstruction that directly optimizes for both anatomical magnitude and phase-derived velocity accuracy. Building on the MoDL framework, FlowMoDL alternates a learned (3+1)D spatiotemporal denoiser with conjugate-gra…

• A Configurable Privacy-Preserving MRI Processing Workflow Using Deep Learning-Based Brain Extraction and Adaptive Anatomical Preservation

  Rayeef Ali Khan, Komal Raj Mahantesh · 20. August 2026

  Structural Magnetic Resonance Imaging (MRI) is widely used in neuroimaging research and clinical practice, but structural MRI volumes may retain facial and cranial anatomical information that raises privacy concerns. Existing deep learning-based brain extraction methods generally produce a single fi…

• Primitive Representation Learning for Unsupervised Dynamic Contrast Enhanced MRI Reconstruction

  Veronika Spieker, Wenqi Huang, Cemre Ariyurek, Liam Timms, Daniel Rueckert, Onur Afacan, Julia A. Schnabel, Sila Kurugol · 19. August 2026

  Reliable quantitative analysis of dynamic contrast-enhanced MRI requires high-quality spatiotemporal reconstructions at high undersampling rates. Scan-specific reconstructions using Gaussian and Gabor primitives have shown promising results without the need for large training datasets, but have not …

• Harnessing Magnitude-Only and Complex Measurements for Improved Dynamic MRI Reconstruction with Learned Priors

  Mahdi Saberi, Yaşar Utku Alçalar, Merve Gülle, Chetan Shenoy, Mehmet Akçakaya · 19. August 2026

  MRI reconstruction methods for undersampled k-space data naturally utilize complex-valued measurements. Parallel developments in sparse phase retrieval have shown that magnitude-only measurements may provide complementary information for signal recovery. However, their use in MRI reconstruction rema…

• UMPIRE-Net: Unrolled Magnitude-Phase Regularization Network for Accelerated MRI

  Mahdi Saberi, Toygan Kiliç, Mehmet Akçakaya · 17. August 2026

  MRI reconstruction from undersampled k-space measurements is an ill-posed inverse problem. Physics-driven deep learning (PD-DL) methods have shown strong performance for this task by combining the MRI forward model with learned image regularization within algorithm-unrolling frameworks. However, mos…

• Physics-Informed Implicit Neural Representations for Improved Myocardial Perfusion MRI Quantification

  Christos Tsepas, Chang Yan, Maximilian Fuetterer, Sebastian Kozerke, Cian M Scannell · 13. August 2026

  Quantifying myocardial perfusion from cardiac magnetic resonance (CMR) can be achieved by fitting tracer-kinetic models to the dynamic contrast-enhanced MR data. However, fitting the observed data with multi-compartment exchange models, which describe the evolution of the contrast agent in the tissu…

• Motion Artifact-Aware Self-Supervised Representation Learning for 3D Brain MRI Motion Artifact Reduction

  Mojtaba Safari, Shansong Wang, Zach Eidex, Matthew Goette, Tonghe Wang, Zhen Tian, Xiaofeng Yang · 12. August 2026

  Patient motion remains a source of image degradation in brain MRI, leading to signal loss, blurring, and geometric distortion that compromise quantitative analysis. Existing deep learning methods for motion correction typically rely on paired clean-corrupted data or k-space acquisitions, which are r…

• MRIComp4Flow: Compression of 3D Brain MRI for Training Multi-Modal Generative Models

  Lisa K. Fischer, Mykhailo Riabets, Daniel Rueckert, Benedikt Wiestler, Anke Meyer-Baese, Sandeep Nagar · 12. August 2026

  Large-scale multi-modal MRI datasets impose substantial storage and I/O costs, limiting the training of 3D generative models on commodity infrastructure. While lossy compression is known to preserve accuracy for discriminative segmentation networks, its effect on generative models, which must learn …

• CUPA-T2*: Covariance-Aware Uncertainty Propagation and Alignment for T2* Mapping in Accelerated MRI

  Gideon N. L. Rouwendaal, Natascha Niessen, Hannah Eichhorn, Dirk H. J. Poot, Christine Preibisch, Julia A. Schnabel · 11. August 2026

  Quantitative T2* maps have strong potential for biomarker discovery but are limited by long scan times, rendering them impractical in clinical settings. Significant acceleration can be achieved through undersampling in k-space combined with learning-based reconstruction. However, reconstruction arti…

• Does FLAIR super-resolution erase or hallucinate small white-matter lesions?

  Zahra Khodakarami, Yue Li, Pulkit Khandelwal, John Detre, Sandhitsu Das, Christopher Brown, David Wolk, Paul Yushkevich · 7. August 2026

  White matter hyperintensities (WMH), bright regions on Fluid-attenuated Inversion Recovery (FLAIR) scans are associated with cerebrovascular pathology and neurodegeneration. FLAIR is usually acquired with thick slices in clinical settings, giving it poor through-plane resolution. Super-resolution (S…

• K-space Gaussian Representation for Parallel MRI

  Yu Guan, Mingyu Hu, Jiale Hu, Zhuoxu Cui, Dong Liang, Qiegen Liu · 30. Juli 2026

  Accelerated magnetic resonance imaging (MRI) aims to recover the k-space signal from acquired measurements, where accurate estimation of missing samples is essential for high-fidelity reconstruction. Existing k-space reconstruction methods estimate missing samples through interpolation operators or …

• SHFormer: Dynamic Spectral Filtering Convolutional Neural Network and High-pass Kernel Generation Transformer for Adaptive MRI Reconstruction

  Sriprabha Ramanarayanan, Rahul G. S., Mohammad Al Fahim, Keerthi Ram, Ramesh Venkatesan, Mohanasankar Sivaprakasam · 23. Juli 2026

  Attention Mechanism (AM) selectively focuses on essential information for imaging tasks and captures relationships between distant pixel neighborhoods to compute feature representations. Accelerated MRI reconstruction benefits from AM, as the imaging process involves Fourier domain measurements that…

• Frequency-Hierarchical Active k-Space Sampling for Diagnostic MRI

  Ruru Xu, Kian Anvari Hamedani, Zhikai Yang, Ilkay Oksuz · 23. Juli 2026

  Active sampling for accelerated MRI must distribute a tight sampling budget across spatial frequencies that carry very different kinds of information. Low frequencies hold most of the anatomical context; high frequencies carry the fine details that drive pathology assessment. Existing active sampler…

• DRIFT: Difficulty-aware Rectified Flows for Through-plane MRI Super-Resolution

  Yoonseok Choi, Eun-Gyu Ha, Daniel Kim, Mohammed A. Al-masni, Ming-Hsuan Yang, Dong-Hyun Kim · 21. Juli 2026

  Magnetic Resonance Imaging (MRI) is often acquired with anisotropic resolution to reduce scan time, producing stair-step artifacts along the through-plane direction. In through-plane MRI super-resolution, an efficiency-fidelity trade-off arises: feed-forward regressors are fast but oversmooth at lar…

• Rendering Novel Views of MRI Using 3D Gaussian Splatting

  Robin Y. Park, Mark C. Eid, Rhydian Windsor, Amir Jamaludin, Ana I. L. Namburete, João F. Henriques, Andrew Zisserman · 26. Juni 2026

  The objective of this paper is to improve radiological gradings measured on MRIs of spines, by resampling scans so that the new view planes are better aligned with the target anatomy than the original sparse images. To this end, we adapt 3D Gaussian Splatting to form a volumetric reconstruction star…

• Deep Unrolled Networks in Representation Space Applied to MRI Reconstruction

  Efe Ilıcak, Baris Imre, Chloé Najac, Ruben van den Broek, Beatrice Lena, Andrew Webb, Marius Staring · 22. Juni 2026

  Deep unrolled networks (DUNs) integrate physical forward models with learned regularization in cascaded network architectures, achieving exceptional performance in inverse problems while maintaining interpretability. While most DUNs operate in the object domain (e.g., image space), recent variants e…

• Unsupervised Susceptibility Distortion Correction of EPI without Calibration Scans via Image Translation-Based Registration

  Wooseung Kim, Sung-Hong Park · 22. Juni 2026

  Functional magnetic resonance imaging (fMRI) utilizes echo-planar imaging (EPI) to capture blood-oxygen-level-dependent (BOLD) signals with high temporal resolution. However, EPI is inherently sensitive to magnetic field inhomogeneities, resulting in susceptibility-induced geometric distortions alon…

• Trustworthy MRI Reconstruction via Bayesian Uncertainty Quantification with Sparsity Prior Models

  Ahmed Karam Eldaly, Matteo Figini, Daniel C. Alexander · 17. Juni 2026

  We propose a novel Bayesian framework for joint image reconstruction and uncertainty quantification from compressed sensing magnetic resonance imaging data. The problem is formulated as a linear inverse problem, where prior distributions are assigned to the unknown image parameters. Specifically, th…

• Variational Network with Wavelet-based UNET in Accelerated MRI Reconstruction from Under Sampled K-space Data

  Yasir Arafat Prodhan, Shaikh Anowarul Fattah · 16. Juni 2026

  Fully sampled MRI requires dense k-space acquisition, leading to long scan times, reduced clinical throughput, and increased sensitivity to patient motion. Accelerated MRI addresses this by acquiring undersampled k-space data and reconstructing the missing information computationally. However, recon…