Characterizing Errors in Pharmacokinetic Parameters from Analyzing Quantitative Abbreviated DCE-MRI Data in Breast Cancer.
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ABSTRACT:
SUBMITTER: Slavkova KP
PROVIDER: S-EPMC8293327 | biostudies-literature |
REPOSITORIES: biostudies-literature
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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.
| S-EPMC9381479 | biostudies-literature
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.
| S-EPMC5556235 | biostudies-literature
Project description:PurposeT1 -weighted dynamic contrast-enhanced Magnetic Resonance Imaging (DCE-MRI) is typically quantified by least squares (LS) fitting to a pharmacokinetic (PK) model to yield parameters of microvasculature and perfusion in normal and disease tissues. Such fitting is both time-consuming as well as subject to inaccuracy and instability in parameter estimates. Here, we propose a novel neural network approach to estimate the PK parameters by extracting long and short time-dependent features in DCE-MRI.MethodsA Long Short-Term Memory (LSTM) network, widely used for processing sequence data, was employed to map DCE-MRI time-series accompanied with an arterial input function to parameters of the extended Tofts model. Head and neck DCE-MRI from 103 patients were used for training and testing the LSTM model. Arterial input functions (AIFs) from 78 patients were used to generate synthetic DCE-MRI time-series for training, during which data augmentation was used to overcome the limited size of in vivo data. The model was tested on independent synthesized DCE data using AIFs from 25 patients. The LSTM performance was optimized for the numbers of layers and hidden state features. The performance of the LSTM was tested for different temporal resolution, total acquisition time, and contrast-to-noise ratio (CNR), and compared to the conventional LS fitting and a CNN-based method.ResultsCompared to LS fitting, the LSTM model had comparable accuracy in PK parameter estimations from fully temporal-sampled DCE-MRI data (~3 s per frame), but much better accuracy for the data with temporally subsampling (4s or greater per frame), total acquisition time truncation by 48%-16%, or low CNR (5 and 10). The LSTM reduced normalized root mean squared error by 40.4%, 46.9%, and 53.0% for sampling intervals of 4s, 5s, and 6s, respectively, compared to LS fitting. Compared to the CNN model, the LSTM model reduced the error in the parameter estimates up to 55.2%. Also, the LSTM improved the inference time by ~ 14 times on CPU compared to LS fitting.ConclusionOur study suggests that the LSTM model could achieve improved robustness and computation speed for PK parameter estimation compared to LS fitting and the CNN based network, particularly for suboptimal data.
| S-EPMC7429301 | biostudies-literature
Project description:PurposeTo investigate the feasibility of using undersampled k-space data and an iterative image reconstruction method with total generalized variation penalty in the quantitative pharmacokinetic analysis for clinical brain dynamic contrast-enhanced magnetic resonance imaging.MethodsEight brain dynamic contrast-enhanced magnetic resonance imaging scans were retrospectively studied. Two k-space sparse sampling strategies were designed to achieve a simulated image acquisition acceleration factor of 4. They are (1) a golden ratio-optimized 32-ray radial sampling profile and (2) a Cartesian-based random sampling profile with spatiotemporal-regularized sampling density constraints. The undersampled data were reconstructed to yield images using the investigated reconstruction technique. In quantitative pharmacokinetic analysis on a voxel-by-voxel basis, the rate constant Ktrans in the extended Tofts model and blood flow FB and blood volume VB from the 2-compartment exchange model were analyzed. Finally, the quantitative pharmacokinetic parameters calculated from the undersampled data were compared with the corresponding calculated values from the fully sampled data. To quantify each parameter's accuracy calculated using the undersampled data, error in volume mean, total relative error, and cross-correlation were calculated.ResultsThe pharmacokinetic parameter maps generated from the undersampled data appeared comparable to the ones generated from the original full sampling data. Within the region of interest, most derived error in volume mean values in the region of interest was about 5% or lower, and the average error in volume mean of all parameter maps generated through either sampling strategy was about 3.54%. The average total relative error value of all parameter maps in region of interest was about 0.115, and the average cross-correlation of all parameter maps in region of interest was about 0.962. All investigated pharmacokinetic parameters had no significant differences between the result from original data and the reduced sampling data.ConclusionWith sparsely sampled k-space data in simulation of accelerated acquisition by a factor of 4, the investigated dynamic contrast-enhanced magnetic resonance imaging pharmacokinetic parameters can accurately estimate the total generalized variation-based iterative image reconstruction method for reliable clinical application.
| S-EPMC5616060 | biostudies-literature
Project description:The purpose was to analyze the value of quantitative parameters of DCE-MRI in evaluating micro-infiltration of malignant bone tumors.MethodsThirty-nine New Zealand white rabbits were used to establish malignant bone tumor models by implanting VX2 tumor fragments into the right tibiae. After three weeks, models were examined by conventional MRI and DCE-MRI; then the right tibiae were cut into sagittal sections and partitioned into histology slices for comparison with microscopic findings. Micro-infiltration groups were selected and the range of infiltration was determined under the microscope, and corresponding DCE images analyzed to obtain the quantitative parameters include Ktrans, Kep, ve and vp in parenchyma areas, micro-infiltration areas and simple edema areas. One-way ANOVA was used to compare the differences of the parameters between the three areas. Receiver operating characteristic curves (ROCs) were plotted to determine the accuracy of different parameters by area under curves (AUCs).Results22 cases (22/39, 56.4%) were included in the micro-infiltration group and the infiltration depth ranged from 1.3 mm to 4.6 mm, with an average depth of 3.2 mm ± 0.8 mm. The statistical results of quantitative parameters in the three areas were as follows: Ktrans values were (0.494 ± 0.052), (0.403 ± 0.049), (0.173 ± 0.047) min-1 (p = =0.000), Kep values were (1.959 ± 0.65), (1.528 ± 0.372), (1.174 ± 0.486) min-1 (p = =0.000), ve values were (0.247 ± 0.068), (0.283 ± 0.057), (0.168 ± 0.062) min-1 (p = =0.000), vp values were (0.125 ± 0.036), (0.108 ± 0.033), (0.098 ± 0.025) min-1 (p = =0.022), respectively. Ktrans and Kep values had significant difference in the three areas after comparing between-groups, respectively. However, there were no significant difference in vp values between parenchyma and micro-infiltration areas (p = =0.078), micro-infiltration and simple edema areas (p = =0.315), and ve values between parenchyma and micro-infiltration areas (p = =0.056). The ve values were higher in parenchyma and micro-infiltration areas then simple edema areas. Ktrans had highest accuracy in differentiating different areas (AUC > 0.9), respectively.ConclusionQuantitative parameters Ktrans, Kep and ve can assess the extent of intramedullary invasion of malignant bone tumors. Ktrans have highest accuracy in differentiating different regions.
| S-EPMC6881657 | biostudies-literature
Project description:PurposeIn dynamic contrast enhanced (DCE) MRI, separation of signal contributions from perfusion and leakage requires robust estimation of parameters in a pharmacokinetic model. We present and quantify the performance of a method to compute tissue hemodynamic parameters from DCE data using established pharmacokinetic models.MethodsWe propose a Bayesian scheme to obtain perfusion metrics from DCE MRI data. Initial performance is assessed through digital phantoms of the extended Tofts model (ETM) and the two-compartment exchange model (2CXM), comparing the Bayesian scheme to the standard Levenberg-Marquardt (LM) algorithm. Digital phantoms are also invoked to identify limitations in the pharmacokinetic models related to measurement conditions. Using computed maps of the extra vascular volume (ve) from 19 glioma patients, we analyze differences in the number of un-physiological high-intensity ve values for both ETM and 2CXM, using a one-tailed paired t-test assuming un-equal variance.ResultsThe Bayesian parameter estimation scheme demonstrated superior performance over the LM technique in the digital phantom simulations. In addition, we identified limitations in parameter reliability in relation to scan duration for the 2CXM. DCE data for glioma and cervical cancer patients was analyzed with both algorithms and demonstrated improvement in image readability for the Bayesian method. The Bayesian method demonstrated significantly fewer non-physiological high-intensity ve values for the ETM (p<0.0001) and the 2CXM (p<0.0001).ConclusionWe have demonstrated substantial improvement of the perceptive quality of pharmacokinetic parameters from advanced compartment models using the Bayesian parameter estimation scheme as compared to the LM technique.
| S-EPMC6317807 | biostudies-literature
Project description:PURPOSE:The aim of this study was to develop a portable perfusion phantom and validate its utility in quantitative dynamic contrast-enhanced magnetic resonance imaging of the abdomen. METHODS:A portable perfusion phantom yielding a reproducible contrast enhancement curve (CEC) was developed. A phantom package including perfusion and static phantoms were imaged simultaneously with each of three healthy human volunteers in two different 3T MR scanners. Look-up tables correlating reference (known) contrast concentrations with measured ones were created using either the static or perfusion phantom. Contrast maps of image slices showing four organs (liver, spleen, pancreas, and paravertebral muscle) were generated before and after data correction using the look-up tables. The contrast concentrations at 4.5 min after dosing in each of the four organs were averaged for each volunteer. The mean contrast concentrations (4 organs × 3 volunteers = 12) were compared for the two scanners, and the intra-class correlation coefficient (ICC) was calculated. Also, the ICC of the mean Ktrans values between the two scanners was calculated before and after data correction. RESULTS:The repeatability coefficient of CECs of perfusion phantom was higher than 0.997 in all measurements. The ICC of the tissue contrast concentrations between the two scanners was 0.693 before correction, but increased to 0.974 after correction using the look-up tables (LUTs) of perfusion phantom. However, the ICC was not increased after correction using static phantom (ICC: 0.617). Similarly, the ICC of the Ktrans values was 0.899 before correction, but increased to 0.996 after correction using perfusion phantom LUTs. The ICC of the Ktrans values, however, was not increased when static phantom LUTs were used (ICC: 0.866). CONCLUSIONS:The perfusion phantom reduced variability in quantitating contrast concentration and Ktrans values of human abdominal tissues across different MR units, but static phantom did not. The perfusion phantom has the potential to facilitate multi-institutional clinical trials employing quantitative DCE-MRI to evaluate various abdominal malignancies.
| S-EPMC5646228 | biostudies-literature
Project description:BackgroundDCE@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.ResultsPixel-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.ConclusionsA 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/.
| S-EPMC4228420 | biostudies-literature
Project description:PURPOSE:We propose a novel methodology to integrate morphological and functional information of tumor-associated vessels to assist in the diagnosis of suspicious breast lesions. THEORY AND METHODS:Ultrafast, fast, and high spatial resolution DCE-MRI data were acquired on 15 patients with suspicious breast lesions. Segmentation of the vasculature from the surrounding tissue was performed by applying a Hessian filter to the enhanced image to generate a map of the probability for each voxel to belong to a vessel. Summary measures were generated for vascular morphology, as well as the inputs and outputs of vessels physically connected to the tumor. The ultrafast DCE-MRI data was analyzed by a modified Tofts model to estimate the bolus arrival time, Ktrans (volume transfer coefficient), and vp (plasma volume fraction). The measures were compared between malignant and benign lesions via the Wilcoxon test, and then incorporated into a logistic ridge regression model to assess their combined diagnostic ability. RESULTS:A total of 24 lesions were included in the study (13 malignant and 11 benign). The vessel count, Ktrans , and vp showed significant difference between malignant and benign lesions (P = 0.009, 0.034, and 0.010, area under curve [AUC] = 0.76, 0.63, and 0.70, respectively). The best multivariate logistic regression model for differentiation included the vessel count and bolus arrival time (AUC = 0.91). CONCLUSION:This study provides preliminary evidence that combining quantitative characterization of morphological and functional features of breast vasculature may provide an accurate means to diagnose breast cancer.
| S-EPMC6347496 | biostudies-literature
Project description:PurposeTo develop a disposable point-of-care portable perfusion phantom (DP4) and validate its clinical utility in a multi-institutional setting for quantitative dynamic contrast-enhanced magnetic resonance imaging (qDCE-MRI).MethodsThe DP4 phantom was designed for single-use and imaged concurrently with a human subject so that the phantom data can be utilized as the reference to detect errors in qDCE-MRI measurement of human tissues. The change of contrast-agent concentration in the phantom was measured using liquid chromatography-mass spectrometry. The repeatability of the contrast enhancement curve (CEC) was assessed with five phantoms in a single MRI scanner. Five healthy human subjects were recruited to evaluate the reproducibility of qDCE-MRI measurements. Each subject was imaged concurrently with the DP4 phantom at two institutes using three 3T MRI scanners from three different vendors. Pharmacokinetic (PK) parameters in the regions of liver, spleen, pancreas, and paravertebral muscle were calculated based on the Tofts model (TM), extended Tofts model (ETM), and shutter speed model (SSM). The reproducibility of each PK parameter over three measurements was evaluated with the intraclass correlation coefficient (ICC) and compared before and after DP4-based error correction.ResultsThe contrast-agent concentration in the DP4 phantom was linearly increased over 10 min (0.17 mM/min, measurement accuracy: 96%) after injecting gadoteridol (100 mM) at a constant rate (0.24 ml/s, 4 ml). The repeatability of the CEC within the phantom was 0.997 when assessed by the ICC. The reproducibility of the volume transfer constant, Ktrans , was the highest of the PK parameters regardless of the PK models. The ICCs of Ktrans in the TM, ETM, and SSM before DP4-based error correction were 0.34, 0.39, and 0.72, respectively, while those increased to 0.93, 0.98, and 0.86, respectively, after correction.ConclusionsThe DP4 phantom is reliable, portable, and capable of significantly improving the reproducibility of qDCE-MRI measurements.
| S-EPMC9188826 | biostudies-literature