ArticlePeerJ2015
DCEMRI.jl: a fast, validated, open source toolkit for dynamic contrast enhanced MRI analysis.
Article in PeerJ, 2015. 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.
- qMRI-BIDS: An extension to the brain imaging data structure for quantitative magnetic resonance imaging data.Scientific data · 2022Article
- Article
- Medical physics challenges in clinical MR-guided radiotherapy.Radiation oncology (London, England) · 2020Review
- Article
- MITK-ModelFit: A generic open-source framework for model fits and their exploration in medical imaging - design, implementation and application on the example of DCE-MRI.BMC bioinformatics · 2019Article
- Executive summary of AAPM Report Task Group 113: Guidance for the physics aspects of clinical trials.Journal of applied clinical medical physics · 2018Article
- TumourMetrics: a comprehensive clinical solution for the standardization of DCE-MRI analysis in research and routine use.Quantitative imaging in medicine and surgery · 2017Article
- Quantitative effects of acquisition duration and temporal resolution on the measurement accuracy of prostate dynamic contrast-enhanced MRI data: a phantom study.Magma (New York, N.Y.) · 2017Article
- Quantitative Evaluation of Temporal Regularizers in Compressed Sensing Dynamic Contrast Enhanced MRI of the Breast.International journal of biomedical imaging · 2017Article
- A novel anthropomorphic flow phantom for the quantitative evaluation of prostate DCE-MRI acquisition techniques.Physics in medicine and biology · 2016Article
- ROCKETSHIP: a flexible and modular software tool for the planning, processing and analysis of dynamic MRI studies.BMC medical imaging · 2015Article
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
We present a fast, validated, open-source toolkit for processing dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) data. We validate it against the Quantitative Imaging Biomarkers Alliance (QIBA) Standard and Extended Tofts-Kety phantoms and find near perfect recovery in the absence of noise, with an estimated 10-20× speedup in run time compared to existing tools. To explain the observed trends in the fitting errors, we present an argument about the conditioning of the Jacobian in the limit of small and large parameter values. We also demonstrate its use on an in vivo data set to measure performance on a realistic application. For a 192 × 192 breast image, we achieved run times of <1 s. Finally, we analyze run times scaling with problem size and find that the run time per voxel scales as O(N (1.9)), where N is the number of time points in the tissue concentration curve. DCEMRI.jl was much faster than any other analysis package tested and produced comparable accuracy, even in the presence of noise.
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.