Arterial Spin Labeling (ASL) Magnetic Resonance Imaging (MRI) is a noninvasive imaging technique used to quantify cerebral blood flow (CBF) by using magnetically labeled arterial blood water as an endogenous tracer. Although ASL eliminates the need for exogenous contrast agents, its widespread clinical use is limited by several challenges, including low Signal-to-Noise Ratio (SNR), susceptibility to motion, and various imaging artifacts. To address these limitations, both traditional denoising techniques and Machine Learning (ML)/Deep Learning (DL)-based approaches have been developed to improve the reliability of ASL by reducing noise, correcting artifacts, and enhancing image quality. In addition, the generation of simulated ASL datasets has become an important strategy for training and validating novel methods when sufficient clinical data are unavailable. This review examines conventional image-processing techniques together with modern machine learning and deep learning approaches developed to improve ASL image quality through denoising and enhancement. It also discusses the major artifacts that affect ASL acquisition and summarizes the simulation methodologies used for the development and evaluation of new algorithms.
S. A, Jini Raju, Ansamma John et al.· Italian National Conference...· 0 citations
The growing adoption of AI-based healthcare research has increased the need for properly anonymized medical imaging datasets. PHI within DICOM files - particularly burned-in pixel-level text - poses significant privacy and regulatory risks. Existing methods either focus solely on metadata or remove all detected text indiscriminately, sacrificing clinically relevant annotations.
This paper proposes a semantic de-identification pipeline integrating YOLOv11n-based text detection, domain-optimized EasyOCR, and a hybrid natural language processing (NLP) classification module combining regular expressions, keyword matching, and named entity recognition. A dual-path architecture processes metadata and pixel-level PHI in parallel, enabling complete DICOM sanitization while preserving non-PHI clinical annotations. The system was evaluated on 1,042 multi-modality DICOM images (CT, MRI, X-ray, ultrasound). As a secondary evaluation, the pipeline was also applied to two tissue-mimicking material (TMM) phantom datasets from TCIA - the RIDER Phantom MRI and Phantom FDA CT (RIDER = Reference Image Database to Evaluate Therapy Response; FDA = Food and Drug Administration) - which served as surrogates for controlled evaluation of metadata and burned-in identifier removal.
The system achieves an F1-score of 95.4%, 96.1% recall, a structural similarity index measure (SSIM) of 0.969, a peak signal-to-noise ratio (PSNR) of 28.9 dB, and processes each image in 2.8 s. It achieves SSIM of 0.986 and PSNR of 49.0 dB on RIDER Phantom MRI, and SSIM of 0.974 and PSNR of 31.5 dB on Phantom FDA CT.
These results confirm that the pipeline preserves quantitative pixel fidelity when applied to institutional and device identifiers embedded in phantom acquisitions, supporting blinding for domain-generalization studies across institutions. The modular design supports institutional customisation, making it suitable for clinical research workflows and privacy-compliant phantom imaging pipelines.
Remya Sethulekshmi, Manu J. Pillai, Nihal Ahammed et al.· Frontiers of Computer Scienc...· 0 citations