ArticleInsights into imaging2021
Deep learning-based fully automated Z-axis coverage range definition from scout scans to eliminate overscanning in chest CT imaging.
Article in Insights into imaging, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 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
19 citing papers in PubMed, 52 citations in OpenAlex.
- Deep Learning-based Non-rigid Image Registration for High-dose Rate Brachytherapy in Inter-fraction Cervical Cancer.Journal of digital imaging · 2023Trial
- TOPOS: target organ prediction on scout views for automated CT scan planning.European radiology · 2026Article
- Mapping the Landscape of Over-Scanning in CT Imaging: A Scoping Review.Journal of medical radiation sciences · 2026Review
- AI-driven assessment of over-scanning in chest CT: A systematic review and meta-analysis.European journal of radiology open · 2025Review
- Artificial intelligence-based cardiac transthyretin amyloidosis detection and scoring in scintigraphy imaging: multi-tracer, multi-scanner, and multi-center development and evaluation study.European journal of nuclear medicine and molecular imaging · 2025Article
- Detecting the left atrial appendage in CT localizers using deep learning.Scientific reports · 2025Article
- Deep learning-based segmentation of ultra-low-dose CT images using an optimized nnU-Net model.La Radiologia medica · 2025Article
- AI for image quality and patient safety in CT and MRI.European radiology experimental · 2025Review
- Reduced dose helical CT scout imaging on next generation wide volume CT system decreases scan length and overall radiation exposure.European journal of radiology open · 2024Article
- Implementing verifiable oncological imaging by quality assurance and optimization (i‑Violin) : Protocol for a European multicenter study.Radiologie (Heidelberg, Germany) · 2024Article
- [Artificial intelligence in diagnostic radiology for dose management : Advances and perspectives using the example of computed tomography].Radiologie (Heidelberg, Germany) · 2024Review
- Artificial intelligence-based analysis of whole-body bone scintigraphy: The quest for the optimal deep learning algorithm and comparison with human observer performance.Zeitschrift fur medizinische Physik · 2024Article
- The SINFONIA project repository for AI-based algorithms and health data.Frontiers in public health · 2024Article
- Decentralized collaborative multi-institutional PET attenuation and scatter correction using federated deep learning.European journal of nuclear medicine and molecular imaging · 2023Article
- MAIC-10 brief quality checklist for publications using artificial intelligence and medical images.Insights into imaging · 2023Review
- High-dimensional multinomial multiclass severity scoring of COVID-19 pneumonia using CT radiomics features and machine learning algorithms.Scientific reports · 2022Article
- Quantum Iterative Reconstruction for Low-Dose Ultra-High-Resolution Photon-Counting Detector CT of the Lung.Diagnostics (Basel, Switzerland) · 2022Article
- Article
- Automated Method for Evaluation of Z-axis Laser Alignment Accuracy using the Siemens Computed Tomography Phantom.Journal of medical physicsArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors at 5 institutions in 4 countries.
Funding
Abstract
backgroundDespite the prevalence of chest CT in the clinic, concerns about unoptimized protocols delivering high radiation doses to patients still remain. This study aimed to assess the additional radiation dose associated with overscanning in chest CT and to develop an automated deep learning-assisted scan range selection technique to reduce radiation dose to patients.
resultsA significant overscanning range (31 ± 24) mm was observed in clinical setting for over 95% of the cases. The average Dice coefficient for lung segmentation was 0.96 and 0.97 for anterior-posterior (AP) and lateral projections, respectively. By considering the exact lung coverage as the ground truth, and AP and lateral projections as input, The DL-based approach resulted in errors of 0.08 ± 1.46 and - 1.5 ± 4.1 mm in superior and inferior directions, respectively. In contrast, the error on external scout views was - 0.7 ± 4.08 and 0.01 ± 14.97 mm for superior and inferior directions, respectively.The ED reduction achieved by automated scan range selection was 21% in the test group. The evaluation of a large multi-centric chest CT dataset revealed unnecessary ED of more than 2 mSv per scan and 67% increase in the thyroid absorbed dose.
conclusionThe proposed DL-based solution outperformed previous automatic methods with acceptable accuracy, even in complicated and challenging cases. The generizability of the model was demonstrated by fine-tuning the model on AP scout views and achieving acceptable results. The method can reduce the unoptimized dose to patients by exclunding unnecessary organs from field of view.
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.