ReviewCureus2024
Enhancing Radiological Diagnosis: A Comprehensive Review of Image Quality Assessment and Optimization Strategies.
Review in Cureus, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
11 citing papers in PubMed.
- The ethical and regulatory framework of medical artificial intelligence (AI) system's behavioral performance for image-based diagnostics: An ethical evaluation of the International and European Union Scope.PLOS digital health · 2026Article
- An integrated digital-to-surgical framework for patient-specific chest wall resection and reconstruction using 3D-printed titanium implants: proof-of-concept study.npj biomedical innovations · 2026Article
- Automated contrast-to-noise ratio analysis in chest CT: validation of an open-source segmentation approach.Insights into imaging · 2026Article
- Work experiences and practices of radiographers during the coronavirus disease 2019 pandemic: Future pandemic preparation.Health SA = SA Gesondheid · 2026Article
- Pediatric 3D MRCP imaging: strategies for enhancing exam quality.Abdominal radiology (New York) · 2026Article
- Chest radiography in interstitial lung disease: Accuracy and radiological features from a systematic review and meta-analysis.Sarcoidosis, vasculitis, and diffuse lung diseases : official journal of WASOG · 2025Article
- Quantitative Evaluation of Image Quality and Radiation Dose Using Novel Intelligent Noise Reduction (INR) Software in Chest Radiography: A Phantom Study.Diagnostics (Basel, Switzerland) · 2025Article
- Towards the Performance Characterization of a Robotic Multimodal Diagnostic Imaging System.Journal of imaging · 2025Article
- Pathological Mechanisms of Radiation-Induced Lung Injury and Novel Nano-Drug Delivery Therapeutic Strategies.International journal of nanomedicine · 2025Review
- Article
- Ability of Deep Learning Image Reconstruction to Preserve Detail and Remove Noise in Coronary Computed Tomography Angiography: A Clinical Analysis.Journal of computer assisted tomographyArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Image quality plays a pivotal role in the accurate diagnosis and effective management of diseases in radiology. This review explores the principles, methodologies, and strategies for assessing and optimizing image quality across various imaging modalities, including X-ray, computed tomography (CT), magnetic resonance imaging (MRI), ultrasound, and nuclear medicine. We discuss key factors influencing image quality, such as spatial resolution, noise, contrast, and artifacts, and highlight techniques for quality assurance, image optimization, and dose reduction in clinical practice.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.