ArticleBioengineering (Basel, Switzerland)2023
Automated MRI Field of View Prescription from Region of Interest Prediction by Intra-Stack Attention Neural Network.
Article in Bioengineering (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 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
10 citing papers in PubMed.
- Deep learning-based automatic field of view planning for prostate MRI in oblique coronal and oblique axial planes.Scientific reports · 2026Article
- Deep learning in fetal, infant, and toddler neuroimaging research.Developmental cognitive neuroscience · 2026Review
- Automated Field of View Prescription for Whole-body Magnetic Resonance Imaging Using Deep Learning Based Body Region Segmentations.Investigative radiology · 2025Article
- Transforming Medical Imaging: The Role of Artificial Intelligence Integration in PACS for Enhanced Diagnostic Accuracy and Workflow EfficiencyCurrent medical imaging · 2025Review
- Recent trends in AI applications for pelvic MRI: a comprehensive review.La Radiologia medica · 2024Review
- Artificial intelligence for neuro MRI acquisition: a review.Magma (New York, N.Y.) · 2024Review
- Deep learning for accelerated and robust MRI reconstruction.Magma (New York, N.Y.) · 2024Review
- Automated Placement of Scan and Pre-Scan Volumes for Breast MRI Using a Convolutional Neural Network.Tomography (Ann Arbor, Mich.) · 2023Article
- AI in MRI: Computational Frameworks for a Faster, Optimized, and Automated Imaging Workflow.Bioengineering (Basel, Switzerland) · 2023Article
- End to End Multitask Joint Learning Model for Osteoporosis Classification in CT Images.Computational intelligence and neuroscience · 2023Article
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
Abstract
Manual prescription of the field of view (FOV) by MRI technologists is variable and prolongs the scanning process. Often, the FOV is too large or crops critical anatomy. We propose a deep learning framework, trained by radiologists' supervision, for automating FOV prescription. An intra-stack shared feature extraction network and an attention network are used to process a stack of 2D image inputs to generate scalars defining the location of a rectangular region of interest (ROI). The attention mechanism is used to make the model focus on a small number of informative slices in a stack. Then, the smallest FOV that makes the neural network predicted ROI free of aliasing is calculated by an algebraic operation derived from MR sampling theory. The framework's performance is examined quantitatively with intersection over union (IoU) and pixel error on position and qualitatively with a reader study. The proposed model achieves an average IoU of 0.867 and an average ROI position error of 9.06 out of 512 pixels on 80 test cases, significantly better than two baseline models and not significantly different from a radiologist. Finally, the FOV given by the proposed framework achieves an acceptance rate of 92% from an experienced radiologist.
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.