This study introduces a novel zero-shot scan-specific self-supervised reconstruction method for magnetic resonance imaging (MRI) to reduce scan times. Conventional supervised reconstruction methods require large amounts of fully-sampled reference data, which is often impractical to obtain and can lead to artifacts by overly emphasizing learned patterns. Existing zero-shot scan-specific methods have attempted to overcome this data dependency but show limited performance due to insufficient utilization of k-space information and constraints derived from MRI forward model. To address these limitations, we introduce a framework utilizing all acquired k-space measurements for both network inputs and training targets. While this framework suffers from training instability, we resolve these challenges through three key components: an Attention-guided K-space Selective Mechanism (AKSM) that provides indirect constraints for non-sampled k-space points, Iteration-wise K-space Masking (IKM) that enhances training stability, and a robust sensitivity map estimation model utilizing cross-channel constraint that performs effectively even at high reduction factors. Experimental results on the FastMRI knee and brain datasets with reduction factors of 4 and 8 demonstrate that the proposed method achieves superior reconstruction quality and faster convergence compared to existing zero-shot scan-specific methods, making it suitable for practical clinical applications. The implementation of our proposed method is publicly available at https://github.com/joojinho97/AeSPa.git.

The modality gap between vision and text embeddings in CLIP presents a significant challenge for zero-shot image captioning, limiting effective cross-modal representation. Traditional approaches, such as noise injection and memory-based similarity matching, attempt to address this gap, yet these methods either rely on indirect alignment or relatively naive solutions with heavy computation. Diffusion Bridge introduces a novel approach to directly reduce this modality gap by leveraging Denoising Diffusion Probabilistic Models (DDPM), trained exclusively on text embeddings to model their distribution. Our approach is motivated by the observation that, while paired vision and text embeddings are relatively close, a modality gap still exists due to stable regions created by the contrastive loss. This gap can be interpreted as noise in cross-modal mappings, which we approximate as Gaussian noise. To bridge this gap, we employ a reverse diffusion process, where image embeddings are strategically introduced at an intermediate step in the reverse process, allowing them to be refined progressively toward the text embedding distribution. This process transforms vision embeddings into text-like representations closely aligned with paired text embeddings, effectively minimizing discrepancies between modalities. Experimental results demonstrate that these text-like vision embeddings significantly enhance alignment with their paired text embeddings, leading to improved zero-shot captioning performance on MSCOCO and Flickr30K. Diffusion Bridge achieves competitive results without reliance on memory banks or entity-driven methods, offering a novel pathway for cross-modal alignment and opening new possibilities for the application of diffusion models in multi-modal tasks. The source code is available at: https://github.com/mongeoroo/diffusion-bridge

Multi-contrast magnetic resonance (MR) images offer critical diagnostic information but are limited by long scan times and high cost. While diffusion models (DMs) excel in medical image synthesis, they often struggle to maintain anatomical consistency and utilize the diverse characteristics of multi-contrast MR images effectively. We propose APT, a unified diffusion model designed to generate accurate and anatomically consistent multi-contrast MR images. APT introduces a mutual information fusion module and an anatomical consistency loss to preserve critical anatomical structures across multiple contrast inputs. To enhance synthesis, APT incorporates a two-stage inference process: in the first stage, a prior codebook provides coarse anatomical structures by selecting appropriate guidance based on precomputed similarity mappings and Bézier curve transformations. The second stage applies iterative unrolling with weighted averaging to refine the initial output, enhancing fine anatomical details and ensuring structural consistency. This approach enables the preservation of both global structures and local details, resulting in realistic and diagnostically valuable synthesized images. Extensive experiments on public multi-contrast MR brain images demonstrate that our approach significantly outperforms state-of-the-art methods. The source codes are available at https://github.com/yejees/APT.

In recent advancements in Test Time Adaptation (TTA), most existing methodologies focus on updating normalization layers to adapt to the test domain. However, the reliance on normalization-based adaptation presents key challenges. First, normalization layers such as Batch Normalization (BN) are highly sensitive to small batch sizes, leading to unstable and inaccurate statistics. Moreover, normalization-based adaptation is inherently constrained by the structure of the pre-trained model, as it relies on training-time statistics that may not generalize well to unseen domains. These issues limit the effectiveness of normalization-based TTA approaches, especially under significant domain shift. In this paper, we introduce a novel paradigm based on the concept of a \textit{Buffer} layer, which addresses the fundamental limitations of normalization layer updates. Unlike existing methods that modify the core parameters of the model, our approach preserves the integrity of the pre-trained backbone, inherently mitigating the risk of catastrophic forgetting during online adaptation. Through comprehensive experimentation, we demonstrate that our approach not only outperforms traditional methods in mitigating domain shift and enhancing model robustness, but also exhibits strong resilience to forgetting. Furthermore, our \textit{Buffer} layer is modular and can be seamlessly integrated into nearly all existing TTA frameworks, resulting in consistent performance improvements across various architectures. These findings validate the effectiveness and versatility of the proposed solution in real-world domain adaptation scenarios. The code is available at https://github.com/hyeongyu-kim/Buffer_TTA.

Machine reasoning has made great progress in recent years owing to large language models (LLMs). In the clinical domain, however, most NLP-driven projects mainly focus on clinical classification or reading comprehension, and under-explore clinical reasoning for disease diagnosis due to the expensive rationale annotation with clinicians. In this work, we present a "reasoning-aware" diagnosis framework that rationalizes the diagnostic process via prompt-based learning in a time- and labor-efficient manner, and learns to reason over the prompt-generated rationales. Specifically, we address the clinical reasoning for disease diagnosis, where the LLM generates diagnostic rationales providing its insight on presented patient data and the reasoning path towards the diagnosis, namely Clinical Chain-of-Thought (Clinical CoT). We empirically demonstrate LLMs/LMs' ability of clinical reasoning via extensive experiments and analyses on both rationale generation and disease diagnosis in various settings. We further propose a novel set of criteria for evaluating machine-generated rationales' potential for real-world clinical settings, facilitating and benefiting future research in this area.

Recent advances in deep learning-based medical image segmentation studies achieve nearly human-level performance in fully supervised manner. However, acquiring pixel-level expert annotations is extremely expensive and laborious in medical imaging fields. Unsupervised domain adaptation (UDA) can alleviate this problem, which makes it possible to use annotated data in one imaging modality to train a network that can successfully perform segmentation on target imaging modality with no labels. In this work, we propose SDC-UDA, a simple yet effective volumetric UDA framework for Slice-Direction Continuous cross-modality medical image segmentation which combines intra- and inter-slice self-attentive image translation, uncertainty-constrained pseudo-label refinement, and volumetric self-training. Our method is distinguished from previous methods on UDA for medical image segmentation in that it can obtain continuous segmentation in the slice direction, thereby ensuring higher accuracy and potential in clinical practice. We validate SDC-UDA with multiple publicly available cross-modality medical image segmentation datasets and achieve state-of-the-art segmentation performance, not to mention the superior slice-direction continuity of prediction compared to previous studies.

In this study, we propose a three-dimensional Medical image classifier using Multi-plane and Multi-slice Trans-former (M3T) network to classify Alzheimer's disease (AD) in 3D MRI images. The proposed network synergically com-bines 3D CNN, 2D CNN, and Transformer for accurate AD classification. The 3D CNN is used to perform natively 3D representation learning, while 2D CNN is used to utilize the pre-trained weights on large 2D databases and 2D repre-sentation learning. It is possible to efficiently extract the lo-cality information for AD-related abnormalities in the local brain using CNN networks with inductive bias. The trans-former network is also used to obtain attention relationships among multi-plane (axial, coronal, and sagittal) and multi-slice images after CNN. It is also possible to learn the ab-normalities distributed over the wider region in the brain using the transformer without inductive bias. In this ex-periment, we used a training dataset from the Alzheimer's Disease Neuroimaging Initiative (ADNI) which contains a total of 4,786 3D T1-weighted MRI images. For the validation data, we used dataset from three different institutions: The Australian Imaging, Biomarker and Lifestyle Flagship Study of Ageing (AIBL), The Open Access Series of Imaging Studies (OASIS), and some set of ADNI data indepen-dent from the training dataset. Our proposed M3T is compared to conventional 3D classification networks based on an area under the curve (AUC) and classification accuracy for AD classification. This study represents that the pro-posed network M3T achieved the highest performance in multi-institutional validation database, and demonstrates the feasibility of the method to efficiently combine CNN and Transformer for 3D medical images.

Magnetic resonance imaging (MRI) can provide diagnostic information with high-resolution and high-contrast images. However, MRI requires a relatively long scan time compared to other medical imaging techniques, where long scan time might occur patient’s discomfort and limit the increase in resolution of magnetic resonance (MR) image. In this study, we propose a Joint Deep Model-based MR Image and Coil Sensitivity Reconstruction Network, called Joint-ICNet, which jointly reconstructs an MR image and coil sensitivity maps from undersampled multi-coil k-space data using deep learning networks combined with MR physical models. Joint-ICNet has two main blocks, where one is an MR image reconstruction block that reconstructs an MR image from undersampled multi-coil k-space data and the other is a coil sensitivity maps reconstruction block that estimates coil sensitivity maps from undersampled multi-coil k-space data. The desired MR image and coil sensitivity maps can be obtained by sequentially estimating them with two blocks based on the unrolled network architecture. To demonstrate the performance of Joint-ICNet, we performed experiments with a fastMRI brain dataset for two reduction factors (R = 4 and 8). With qualitative and quantitative results, we demonstrate that our proposed Joint-ICNet outperforms conventional parallel imaging and deep-learning-based methods in reconstructing MR images from undersampled multi-coil k-space data.

With increasing fields of application for neural networks and the development of neural networks, the ability to explain deep learning models is also becoming increasingly important. Especially, prior to practical applications, it is crucial to analyze a model’s inference and the process of generating the results. A common explanation method is Class Activation Mapping(CAM) based method where it is often used to understand the last layer of the convolutional neural networks popular in the field of Computer Vision. In this paper, we propose a novel CAM method named Relevance-weighted Class Activation Mapping(Relevance-CAM) that utilizes Layer-wise Relevance Propagation to obtain the weighting components. This allows the explanation map to be faithful and robust to the shattered gradient problem, a shared problem of the gradient based CAM methods that causes noisy saliency maps for intermediate layers. Therefore, our proposed method can better explain a model by correctly analyzing the intermediate layers as well as the last convolutional layer. In this paper, we visualize how each layer of the popular image processing models extracts class specific features using Relevance-CAM, evaluate the localization ability, and show why the gradient based CAM cannot be used to explain the intermediate layers, proven by experimenting the weighting component. Relevance-CAM outperforms other CAM-based methods in recognition and localization evaluation in layers of any depth. The source code is available at: https://github.com/mongeoroo/Relevance-CAM

Accelerating MRI scans is one of the principal outstanding problems in the MRI research community. Towards this goal, we hosted the second fastMRI competition targeted towards reconstructing MR images with subsampled k-space data. We provided participants with data from 7,299 clinical brain scans (de-identified via a HIPAA-compliant procedure by NYU Langone Health), holding back the fully-sampled data from 894 of these scans for challenge evaluation purposes. In contrast to the 2019 challenge, we focused our radiologist evaluations on pathological assessment in brain images. We also debuted a new Transfer track that required participants to submit models evaluated on MRI scanners from outside the training set. We received 19 submissions from eight different groups. Results showed one team scoring best in both SSIM scores and qualitative radiologist evaluations. We also performed analysis on alternative metrics to mitigate the effects of background noise and collected feedback from the participants to inform future challenges. Lastly, we identify common failure modes across the submissions, highlighting areas of need for future research in the MRI reconstruction community.

This paper proposes a new method to correct beam hardening artifacts caused by the presence of metal in polychromatic X-ray computed tomography (CT) without degrading the intact anatomical images. Metal artifacts due to beam-hardening, which are a consequence of X-ray beam polychromaticity, are becoming an increasingly important issue affecting CT scanning as medical implants become more common in a generally aging population. The associated higher-order beam-hardening factors can be corrected via analysis of the mismatch between measured sinogram data and the ideal forward projectors in CT reconstruction by considering the known geometry of high-attenuation objects. Without prior knowledge of the spectrum parameters or energy-dependent attenuation coefficients, the proposed correction allows the background CT image (i.e., the image before its corruption by metal artifacts) to be extracted from the uncorrected CT image. Computer simulations and phantom experiments demonstrate the effectiveness of the proposed method to alleviate beam hardening artifacts.