Image denoising is considered a salient pre-processing step in sophisticated imaging applications. Over the decades, numerous studies have been conducted in denoising. Recently proposed Block matching and 3D (BM3D) filtering added a new dimension to the study of denoising. BM3D is the current state-of-the-art of denoising and is capable of achieving better denoising as compared to any other existing method. However, there is room to improve BM3D to achieve high-quality denoising. In this study, to improve BM3D, we first attempted to improve the Wiener filter (the core of BM3D) by maximizing the structural similarity (SSIM) between the true and the estimated image, instead of minimizing the mean square error (MSE) between them. Moreover, for the DC-only BM3D profile, we introduced a 3D zigzag thresholding. Experimental results demonstrate that regardless of the type of the image, our proposed method achieves better denoising performance than that of BM3D.

Unlike high-dose scans, low-dose cardiac CT perfusion imaging reduces patient radiation exposure and thereby the risk of potential health effects. However, it introduces significant image noise, degrading diagnostic quality and limiting clinical assessment. Denoising is thus a critical preprocessing step to enhance image quality without compromising anatomical or perfusion details. Traditionally used reconstruction-domain methods, such as Iterative Reconstruction and Compressed Sensing, are often limited by algorithmic complexity, dependence on raw sinogram data, and restricted adaptability. Conversely, image-domain methods offer more adaptable denoising options. Recently, learning-based approaches have further expanded this flexibility and demonstrated state-of-the-art performance across various denoising tasks. In this work, we present a deep learning-based denoising method specifically tuned for low-dose cardiac CT perfusion imaging. Our model is trained to reduce noise while preserving structural integrity and temporal contrast dynamics, which are critical for downstream analysis. Unlike many existing methods, our approach is optimized for perfusion data, where temporal consistency is essential. Residual cardiac motion remains a separate challenge, which we aim to address in our future work. Experimental results show significant improvements in quantitative image quality, using both reference-based and no-reference metrics, such as MSE/PSNR/SSIM and NIQE/FID/KID, as well as improved accuracy of perfusion measurements.

Dynamic myocardial CT perfusion imaging enables functional assessment of coronary artery stenosis and myocardial microvascular disease. However, it is susceptible to residual motion artifacts arising from cardiac and respiratory activity. These artifacts introduce temporal misalignments, distorting Time-Enhancement Curves (TECs) and leading to inaccurate myocardial perfusion measurements. Traditional nonrigid registration methods can address such motion but are often computationally expensive and less effective when applied to low-dose images, which are prone to increased noise and structural degradation. In this work, we present a CNN-based motion-correction framework specifically trained for low-dose cardiac CT perfusion imaging. The model leverages spatiotemporal patterns to estimate and correct residual motion between time frames, aligning anatomical structures while preserving dynamic contrast behaviour. Unlike conventional methods, our approach avoids iterative optimization and manually defined similarity metrics, enabling faster, more robust corrections. Quantitative evaluation demonstrates significant improvements in temporal alignment, with reduced Target Registration Error (TRE) and increased correlation between voxel-wise TECs and reference curves. These enhancements enable more accurate myocardial perfusion measurements. Noise from low-dose scans affects registration performance, but this remains an open challenge. This work emphasizes the potential of learning-based methods to perform effective residual motion correction under challenging acquisition conditions, thereby improving the reliability of myocardial perfusion assessment.

Whole-slide image (WSI) compression is a fundamental requirement in digital pathology. Yet, current validation metrics, such as PSNR and pathologist agreement, were developed before the emergence of pathology foundation models (PFMs). PFMs capture fine-grained tissue details in high-dimensional spaces that can be disrupted by compression artifacts invisible to the human eye, potentially affecting downstream tasks. To the best of our knowledge, we present a systematic study of model- and dataset-specific embedding-drift indicators using representation drift ($\Delta E$), a cosine-based similarity measure, across clinically motivated compression ratios. We evaluate three vision transformer models of identical ViT-Large architecture but different training data: DINOv2 (general vision, 142 million natural images), UNI (pathology, over 100,000 clinical WSIs), and Phikon-v2 (a second PFM). We use 4000 TCGA-BRCA H&E tumor tiles from two WSIs at six compression ratios, ranging from lossless to 80:1. The results demonstrate that UNI first exceeds the predefined $\Delta E>0.01$ sentinel criterion at the tested CR 10:1 operating point (PSNR $=37.76$ dB, usually considered excellent). DINOv2 first exceeds the same sentinel criterion at CR 20:1. At CR 20:1, UNI’s $\Delta E=0.109$ while DINOv2’s is $0.012$, a nearly tenfold difference at the same image quality. At CR 80:1, UNI’s cosine similarity drops to 0.316 while DINOv2 retains 0.895. Phikon-v2 first exceeds the same sentinel criterion at CR 10:1 and tracks UNI far more closely than DINOv2 ($\Delta E=0.311$ versus $0.037$ at CR 40:1). The two pathology-pretrained models exhibit substantially greater drift than DINOv2, a pattern consistent with an association between pathology-domain training and increased compression sensitivity. However, checkpoint-specific factors such as training objectives, preprocessing, learned invariances, embedding normalization, and training data may also contribute. Additional tests on a balanced 500-tile TCGA-LUAD pilot set, denser compression ratios, and an alternative JPEG2000 encoder show similar model-dependent drift patterns; however, the empirical drift values and operating criteria should be interpreted as model- and dataset-specific rather than as generalizable thresholds across tissues, institutions, scanners, or staining protocols. We introduce the Rate Distortion Representation (RDR) curve as a model-aware evaluation tool that reveals this representation blind spot: a compression range where PSNR remains conventionally acceptable while embedding geometry is substantially altered. These results are model- and dataset-specific and should not be interpreted as universal compression thresholds or clinical safety boundaries. Overall, the findings support the use of representation-level robustness assessment as a complement to image-level fidelity metrics in AI-oriented digital pathology workflows.

Computed Tomography (CT) is a widely used imaging modality that employs X-rays and computational reconstruction to visualize internal anatomy. Although higher radiation doses produce higher-quality images, they also increase long-term cancer risk, motivating the use of low-dose protocols. However, low-dose CT data inherently suffer from elevated Poisson–Gaussian noise, necessitating effective denoising strategies. In myocardial CT perfusion (CTP) imaging, this challenge is compounded by residual cardiac motion, which misaligns consecutive time points and impairs accurate estimation of perfusion maps for diagnosing coronary artery disease. Traditional approaches typically treat these two problems, noise and motion, separately, denoising the reconstructed images first or applying the registration first. Such serial pipelines often degrade clinically significant features; e.g., denoising may destroy structural details essential for registration, while motion correction can distort subtle intensity cues needed for noise modelling. To overcome these limitations, we propose a unified deep learning framework that performs noise suppression and motion correction jointly for low-dose myocardial CTP. The method integrates two complementary components through a parallel ensemble strategy: (i) a modified Fast and Flexible Denoising Network (FFDNet) that incorporates noise-level maps to mitigate blended noise effectively, and (ii) a CNN-based registration model, extended with Time Enhancement Curve (TEC) correction and 4D physiological consistency constraints to estimate temporally coherent and anatomically plausible motion fields. By combining their outputs without iterative dependencies, the proposed framework produces motion-corrected and denoised CTP sequences in a single unified processing step, thereby better preserving myocardial structure and perfusion dynamics than conventional serial pipelines. The model has been evaluated using both reference-based (MSE, PSNR, SSIM, PCC, Noise Variance, TRE) and no-reference (NIQE, FID, KID, AUC) image quality metrics, supplemented by expert human assessment. Results demonstrate that jointly learning noise characteristics and motion patterns enables restoration of low-dose CTP images while minimizing feature corruption, thereby advancing the clinical utility of low-dose myocardial CTP imaging.