Dataset Information

DCE@urLAB: a dynamic contrast-enhanced MRI pharmacokinetic analysis tool for preclinical data.

ABSTRACT:

Background

DCE@urLAB is a software application for analysis of dynamic contrast-enhanced magnetic resonance imaging data (DCE-MRI). The tool incorporates a friendly graphical user interface (GUI) to interactively select and analyze a region of interest (ROI) within the image set, taking into account the tissue concentration of the contrast agent (CA) and its effect on pixel intensity.

Results

Pixel-wise model-based quantitative parameters are estimated by fitting DCE-MRI data to several pharmacokinetic models using the Levenberg-Marquardt algorithm (LMA). DCE@urLAB also includes the semi-quantitative parametric and heuristic analysis approaches commonly used in practice. This software application has been programmed in the Interactive Data Language (IDL) and tested both with publicly available simulated data and preclinical studies from tumor-bearing mouse brains.

Conclusions

A user-friendly solution for applying pharmacokinetic and non-quantitative analysis DCE-MRI in preclinical studies has been implemented and tested. The proposed tool has been specially designed for easy selection of multi-pixel ROIs. A public release of DCE@urLAB, together with the open source code and sample datasets, is available at http://www.die.upm.es/im/archives/DCEurLAB/.

SUBMITTER: Ortuno JE

PROVIDER: S-EPMC4228420 | biostudies-literature | 2013 Nov

REPOSITORIES: biostudies-literature

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Publications

DCE@urLAB: a dynamic contrast-enhanced MRI pharmacokinetic analysis tool for preclinical data.

Ortuño Juan E JE Ledesma-Carbayo María J MJ Simões Rui V RV Candiota Ana P AP Arús Carles C Santos Andrés A

BMC bioinformatics 20131104

<h4>Background</h4>DCE@urLAB is a software application for analysis of dynamic contrast-enhanced magnetic resonance imaging data (DCE-MRI). The tool incorporates a friendly graphical user interface (GUI) to interactively select and analyze a region of interest (ROI) within the image set, taking into account the tissue concentration of the contrast agent (CA) and its effect on pixel intensity.<h4>Results</h4>Pixel-wise model-based quantitative parameters are estimated by fitting DCE-MRI data to s ...[more]

PMID: 24180558

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Project description:The aim of the study was to investigate dynamic contrast enhanced MRI (DCE-MRI) as a potential marker to assess the therapeutic responses of fecal microbiota transplantation (FMT) in patients with Crohn's disease (CD) and to determine the parameter or combination of parameters most strongly associated with changes in clinical indicators after treatment.In 22 CD patients, DCE-MRI was performed with a 3.0T scanner. Parameters of DCE-MRI (vascular transfer constant [K] and blood volume [BV]) in the terminal ileum were compared between before and day 90 after FMT treatment. The differences of clinical indicators (C-reactive protein [CRP], Harvey-Bradshaw index [HBI]) and DCE-MRI parameters (K, BV) between pre- and post-treatment was calculated by Student's 2-tailed, paired t-test. The correlations between percent change of clinical indicators (ΔCRP, ΔHBI) with DCE-MRI parameters (ΔK, ΔBV) were analyzed by Pearson's correlation coefficients. A logistic regression model was used to identify the changes of DCE-MRI parameters related to the treatment outcomes. Receiver operating characteristic curves (ROCs) were generated to assess which DCE-MRI parameter showed the best accuracy for evaluation of therapeutic response.After treatment, mean values of clinical indicators decreased significantly (CRP: 62.68 ± 31.86 vs 43.55 ± 29.63 mg/L, P = .008; HBI: 7.18 ± 2.10 vs 5.73 ± 2.33, P = 0.012). Both DCE-MRI parameters showed prominent differences before and after treatment: K (1.86 ± 0.87 vs 1.39 ± 0.83 min, P = .017), BV (61.02 ± 28.49 vs 41.96 ± 22.75 mL/100 g, P = .005). There were significant correlations between ΔCRP or ΔHBI and percent change of CDE-MRI parameters (ΔK to ΔCRP: 0.659; ΔK to ΔHBI: 0.496; ΔBV to ΔCRP: 0.442; ΔBV to ΔHBI: 0.476). Compared to ΔK and ΔBV individually, the combination of both parameters performed best in assessment of therapeutic response with an area under the ROCs (AUC) of 0.948.K and BV parameters derived from DCE-MRI have the potential to assess for therapeutic response after FMT treatment for CD. The combination of K and BV measurements improved the predictive capability compared to the individual parameters.

Project description:ObjectivesTo automatically detect MRI artifacts on dynamic contrast-enhanced (DCE) maximum intensity projections (MIPs) of the breast using deep learning.MethodsWomen who underwent clinically indicated breast MRI between October 2015 and December 2019 were included in this IRB-approved retrospective study. We employed two convolutional neural network architectures (ResNet and DenseNet) to detect the presence of artifacts on DCE MIPs of the left and right breasts. Networks were trained on images acquired up to and including the year 2018 using a 5-fold cross-validation (CV). Ensemble classifiers were built with the resulting CV models and applied to an independent holdout test dataset, which was formed by images acquired in 2019.ResultsOur study sample contained 2265 examinations from 1794 patients (median age at first acquisition: 50 years [IQR: 17 years]), corresponding to 1827 examinations of 1378 individuals in the training dataset and 438 examinations of 416 individuals in the holdout test dataset with a prevalence of image-level artifacts of 53% (1951/3654 images) and 43% (381/876 images), respectively. On the holdout test dataset, the ResNet and DenseNet ensembles demonstrated an area under the ROC curve of 0.92 and 0.94, respectively.ConclusionNeural networks are able to reliably detect artifacts that may impede the diagnostic assessment of MIPs derived from DCE subtraction series in breast MRI. Future studies need to further explore the potential of such neural networks to complement quality assurance and improve the application of DCE MIPs in a clinical setting, such as abbreviated protocols.Key points• Deep learning classifiers are able to reliably detect MRI artifacts in dynamic contrast-enhanced protocol-derived maximum intensity projections of the breast. • Automated quality assurance of maximum intensity projections of the breast may be of special relevance for abbreviated breast MRI, e.g., in high-throughput settings, such as cancer screening programs.

Dataset Information

DCE@urLAB: a dynamic contrast-enhanced MRI pharmacokinetic analysis tool for preclinical data.

Background

Results

Conclusions

Publications

DCE@urLAB: a dynamic contrast-enhanced MRI pharmacokinetic analysis tool for preclinical data.

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