FORCE-Interior: A Poisson Flow Generative Prior for Interior Tomography Reconstruction

Interior tomography reconstructs a region of interest (ROI) from truncated projection measurements. However, projection truncation makes the inverse problem severely ill-posed, leading to non-unique solutions, oversmoothing, and truncation-induced artifacts when conventional reconstruction methods are directly applied to interior tomography. Existing learning-based interior CT methods have shown promising performance, but their generalization across different truncation patterns, ROI sizes, and noise levels remains an important challenge. Meanwhile, current generative model-based reconstruction methods are primarily designed for non-interior tomography settings and do not directly address ROI-based projection truncation. Moreover, without sufficient data-consistency constraints, generative sampling may yield anatomically plausible but measurement-inconsistent structures. To address these challenges, we propose FORCE-Interior, a Poisson-flow generative reconstruction framework for interior tomography. FORCE-Interior combines a full-FOV measurement-constrained initialization with per-step data consistency for ROI-truncated measurements, anchoring generative sampling to the acquired measurements throughout reconstruction. Experiments show that FORCE-Interior achieves improved structural and perceptual reconstruction quality at the two more severely truncated ROI sizes, with competitive results at the largest, while maintaining projection-domain consistency.

Self-Auditing Residual Drifting for Pathology-Preserving Accelerated Knee MRI

Accelerated magnetic resonance imaging reduces acquisition time, but reconstruction from undersampled k-space can blur diagnostically relevant structures or introduce failures that are not captured by global image metrics. We propose SA-RDM-DC, a Self-Auditing Residual generative Drifting Model with Data Consistency for accelerated knee MRI. The method adapts the newly proposed generative drifting paradigm to accelerated MRI by training a physics-conditioned drift field from the zero-filled reconstruction toward the fully sampled residual correction. It predicts image- and missing-k-space residual corrections, enforces data consistency with acquired k-space, uses frequency-aware and residual drifting supervision to recover fine detail, and produces dense error maps and slice-level risk scores in the same inference pass. We evaluate SA-RDM-DC on multi-coil fastMRI knee data at acceleration factors of 4, 8, and 12, with fastMRI+ pathology annotations for region-level and classifier-based task preservation, and on SKM-TEA for zero-shot and fine-tuned protocol-shift evaluation. Compared with zero-filled reconstruction, UNet-image-SENSE, DC-UNet, Score-Diffusion, ELF-Diff, SENSE-VarNet, and MoDL baselines, SA-RDM-DC achieves the highest SSIM across fastMRI acceleration factors while retaining subsecond per-slice inference and avoiding the long sampling time of iterative diffusion baselines. In pathology-aware analysis, SA-RDM-DC preserves lesion-region structural fidelity and reduces meniscus prediction instability. Its self-auditing scores strongly identify high-error reconstructions on fastMRI and partially transfer as a selective-review signal under SKM-TEA protocol shift. These results support reconstruction evaluation that jointly considers image fidelity, pathology preservation, runtime, and case-specific reliability.

MRI-to-CT synthesis using drifting models

Accurate MRI-to-CT synthesis could enable MR-only pelvic workflows by providing CT-like images with bone details while avoiding additional ionizing radiation. In this work, we investigate recently proposed drifting models for synthesizing pelvis CT images from MRI and benchmark them against convolutional neural networks (UNet, VAE), a generative adversarial network (WGAN-GP), a physics-inspired probabilistic model (PPFM), and diffusion-based methods (FastDDPM, DDIM, DDPM). Experiments are performed on two complementary datasets: Gold Atlas Male Pelvis and the SynthRAD2023 pelvis subset. Image fidelity and structural consistency are evaluated with SSIM, PSNR, and RMSE, complemented by qualitative assessment of anatomically critical regions such as cortical bone and pelvic soft-tissue interfaces. Across both datasets, the proposed drifting model achieves high SSIM and PSNR and low RMSE, surpassing strong diffusion baselines and conventional CNN-, VAE-, GAN-, and PPFM-based methods. Visual inspection shows sharper cortical bone edges, improved depiction of sacral and femoral head geometry, and reduced artifacts or over-smoothing, particularly at bone-air-soft tissue boundaries. Moreover, the drifting model attains these gains with one-step inference and inference times on the order of milliseconds, yielding a more favorable accuracy-efficiency trade-off than iterative diffusion sampling while remaining competitive in image quality. These findings suggest that drifting models are a promising direction for fast, high-quality pelvic synthetic CT generation from MRI and warrant further investigation for downstream applications such as MRI-only radiotherapy planning and PET/MR attenuation correction.

Median2Median: Zero-shot Suppression of Structured Noise in Images

Image denoising is a fundamental problem in computer vision and medical imaging. However, real-world images are often degraded by structured noise with strong anisotropic correlations that existing methods struggle to remove. Most data-driven approaches rely on large datasets with high-quality labels and still suffer from limited generalizability, whereas existing zero-shot methods avoid this limitation but remain effective only for independent and identically distributed (i.i.d.) noise. To address this gap, we propose Median2Median (M2M), a zero-shot denoising framework designed for structured noise. M2M introduces a novel sampling strategy that generates pseudo-independent sub-image pairs from a single noisy input. This strategy leverages directional interpolation and generalized median filtering to adaptively exclude values distorted by structured artifacts. To further enlarge the effective sampling space and eliminate systematic bias, a randomized assignment strategy is employed, ensuring that the sampled sub-image pairs are suitable for Noise2Noise training. In our realistic simulation studies, M2M performs on par with state-of-the-art zero-shot methods under i.i.d. noise, while consistently outperforming them under correlated noise. These findings establish M2M as an efficient, data-free solution for structured noise suppression and mark the first step toward effective zero-shot denoising beyond the strict i.i.d. assumption.

Machine Learning-Based Genomic Linguistic Analysis (Gene Sequence Feature Learning): A Case Study on Predicting Heavy Metal Response Genes in Rice

This study explores the application of machine learning-based genetic linguistics for identifying heavy metal response genes in rice (Oryza sativa). By integrating convolutional neural networks and random forest algorithms, we developed a hybrid model capable of extracting and learning meaningful features from gene sequences, such as k-mer frequencies and physicochemical properties. The model was trained and tested on datasets of genes, achieving high predictive performance (precision: 0.89, F1-score: 0.82). RNA-seq and qRT-PCR experiments conducted on rice leaves which exposed to Hg0, revealed differential expression of genes associated with heavy metal responses, which validated the model's predictions. Co-expression network analysis identified 103 related genes, and a literature review indicated that these genes are highly likely to be involved in heavy metal-related biological processes. By integrating and comparing the analysis results with those of differentially expressed genes (DEGs), the validity of the new machine learning method was further demonstrated. This study highlights the efficacy of combining machine learning with genetic linguistics for large-scale gene prediction. It demonstrates a cost-effective and efficient approach for uncovering molecular mechanisms underlying heavy metal responses, with potential applications in developing stress-tolerant crop varieties.