Analysis Of DCE-MRI Data Research Articles

Probabilistic Nested Model Selection in Pharmacokinetic Analysis of DCE-MRI Data in Animal Model of Cerebral Tumor.

Best current practice in the analysis of dynamic contrast enhanced (DCE)-MRI is to employ a voxel-by-voxel model selection from a hierarchy of nested models. This nested model selection (NMS) assumes that the observed time-trace of contrast-agent (CA) concentration within a voxel, corresponds to a singular physiologically nested model. However, admixtures of different models may exist within a voxel's CA time-trace. This study introduces an unsupervised feature engineering technique (Kohonen-Self-Organizing-Map (K-SOM)) to estimate the voxel-wise probability of each nested model. Sixty-six immune-compromised-RNU rats were implanted with human U-251N cancer cells, and DCE-MRI data were acquired from all the rat brains. The time-trace of change in the longitudinalrelaxivity for all animals' brain voxels was calculated. DCE-MRI pharmacokinetic (PK) analysis was performed using NMS to estimate three model regions: Model-1: normal vasculature without leakage, Model-2: tumor tissues with leakage without back-flux to the vasculature, Model-3: tumor vessels with leakage and back-flux. Approximately two hundred thirty thousand (229,314) normalized profiles of animals' brain voxels along with their NMS results were used to build a K-SOM (topology-size: 8×8, with competitive-learning algorithm) and probability map of each model. K-fold nested-cross-validation (NCV, k=10) was used to evaluate the performance of the K-SOM probabilistic-NMS (PNMS) technique against the NMS technique. The K-SOM PNMS's estimation for the leaky tumor regions were strongly similar (Dice-Similarity-Coefficient, DSC=0.774 [CI: 0.731-0.823], and 0.866 [CI: 0.828-0.912] for Models 2 and 3, respectively) to their respective NMS regions. The mean-percent-differences (MPDs, NCV, k=10) for the estimated permeability parameters by the two techniques were: -28%, +18%, and +24%, for , and , respectively. The KSOM-PNMS technique produced microvasculature parameters and NMS regions less impacted by the arterial-input-function dispersion effect. This study introduces an unsupervised model-averaging technique (K-SOM) to estimate the contribution of different nested-models in PK analysis and provides a faster estimate of permeability parameters.

A Novel Multi-Model High Spatial Resolution Method for Analysis of DCE MRI Data: Insights from Vestibular Schwannoma Responses to Antiangiogenic Therapy in Type II Neurofibromatosis.

This study aimed to develop and evaluate a new DCE-MRI processing technique that combines LEGATOS, a dual-temporal resolution DCE-MRI technique, with multi-kinetic models. This technique enables high spatial resolution interrogation of flow and permeability effects, which is currently challenging to achieve. Twelve patients with neurofibromatosis type II-related vestibular schwannoma (20 tumours) undergoing bevacizumab therapy were imaged at 1.5 T both before and at 90 days following treatment. Using the new technique, whole-brain, high spatial resolution images of the contrast transfer coefficient (Ktrans), vascular fraction (vp), extravascular extracellular fraction (ve), capillary plasma flow (Fp), and the capillary permeability-surface area product (PS) could be obtained, and their predictive value was examined. Of the five microvascular parameters derived using the new method, baseline PS exhibited the strongest correlation with the baseline tumour volume (p = 0.03). Baseline ve showed the strongest correlation with the change in tumour volume, particularly the percentage tumour volume change at 90 days after treatment (p < 0.001), and PS demonstrated a larger reduction at 90 days after treatment (p = 0.0001) when compared to Ktrans or Fp alone. Both the capillary permeability-surface area product (PS) and the extravascular extracellular fraction (ve) significantly differentiated the 'responder' and 'non-responder' tumour groups at 90 days (p < 0.05 and p < 0.001, respectively). These results highlight that this novel DCE-MRI analysis approach can be used to evaluate tumour microvascular changes during treatment and the need for future larger clinical studies investigating its role in predicting antiangiogenic therapy response.

Open source code for the generation of digital reference objects for dynamic contrast-enhanced MRI analysis software validation.

Dynamic contrast-enhanced MR images can be analyzed through the application of a wide range of kinetic models. This process is prone to variability and a lack of standardization that can affect the measured metrics. There is a need for customized digital reference objects (DROs) for the validation of DCE-MRI software packages that undertake kinetic model analysis. DROs are currently available only for a small subset of the kinetic models commonly applied to DCE-MRI data. This work aimed to address this gap. Code was written in the MATLAB programming environment to generate customizable DROs. This modular code allows the insertion of a plug-in to describe the kinetic model to be tested. We input our generated DROs into three commercial and open-source analysis packages and assessed the agreement of kinetic model parameter values output with the 'ground-truth' values used in the DRO generation. For the five kinetic models tested, the concordance correlation coefficient values were >98%, indicating excellent agreement of the results with 'ground-truth'. Testing our DROs on three independent software packages produced concordant results, strongly suggesting our DRO generation code is correct. This implies that our DROs can be used to validate other third party software for the kinetic model analysis of DCE-MRI data. This work extends published work of others to allow customized generation of test objects for any applied kinetic model and allows the incorporation of B1 mapping into the DRO for application at higher field strengths.

Abstract P3-03-03: Quantitative multiparametric MRI predicts response to neoadjuvant therapy in the community setting

Abstract Background: Early and accurate prediction of response to neoadjuvant therapy (NAT) would empower personalization of breast cancer treatment regimens based on expected response. Noninvasive, quantitative dynamic contrast-enhanced (DCE-MRI) and diffusion-weighted magnetic resonance imaging (DW-MRI), when performed during the course of NAT, can accurately predict the ultimate pathological response. While these techniques have been incorporated into clinical trials in the academic setting, they have not yet been translated to the community setting, where most cancer patients receive their care. Implementation of quantitative MRI in the community setting widely expands the potential impact it can provide by 1) allowing community settings to participate in clinical trials that require quantitative MRI, and 2) advancing quantitative MRI towards standard-of-care for prediction of response in breast cancer. Methods: Women with locally advanced breast cancer (N = 28) were imaged four times during the course of NAT: 1) prior to the start of NAT, 2) after 1 cycle of NAT, 3) after 2-4 cycles of NAT, and 4) 1 cycle after MRI #3. Imaging data was acquired on 3T Siemens Skyra scanners equipped with breast coils and sited in a community hospital and radiology clinic, respectively. DW-MRI and DCE-MRI were acquired over 10 slices of 5 mm thickness. DW-MRI was acquired with diagonal monopolar diffusion-encoding gradients with b-values of 0, 200, and 800 s/mm2 in a total scan time of 1 minute 39 seconds. Voxel wise tumor cellularity was quantified using the apparent diffusion coefficient (ADC). For DCE-MRI, a gadolinium-based contrast agent was administered intravenously at 2 mL/sec after the acquisition of baseline scans. DCE-MRI data was acquired dynamically with a temporal resolution of 7.27 seconds for a total acquisition time of 8 minutes. The volume transfer constant Ktrans was calculated using Patlak analysis of DCE-MRI data to characterize the tumor vasculature. The tumor was semi-automatically segmented using a manually drawn region of interest followed by fuzzy c-means clustering of DCE-MRI data to identify a functional tumor volume. Measurements of tumor volume were combined with both ADC and Ktrans to yield metrics of tumor cellularity and bulk tumor flow, respectively. Results: Women who achieved pathological complete response at the time of surgery (pCR; n=8) displayed significantly different treatment-induced changes in MRI-derived tumor parameters versus women who did not achieve pCR (non-pCR, n=20). After 1 cycle of NAT, women who achieved pCR had smaller functional tumor volume and lower cellularity (p &amp;lt; 0.05) than non-pCR study participants. At the third and fourth MRI, tumor volume, ADC, Ktrans, cellularity, and bulk tumor flow were all significantly different between the pCR and non-pCR cohorts (p &amp;lt; 0.05). Of note, longest tumor diameter was not predictive of pCR at any time point in this study. Conclusions: This study demonstrates that quantitative DCE- and DW-MRI can be implemented successfully in community care facilities within standard-of-care settings for imaging locally advanced breast cancer. Metrics extracted from the change in DW-MRI (ADC) and DCE-MRI (Ktrans) can accurately predict pathological complete response to neoadjuvant therapy and may be more sensitive to tumor response than the RECIST criteria. Furthermore, incorporating quantitative metrics with tumor volume further increases the ability to predict pathological response to NAT in locally advanced breast cancer. While there are still challenges to address to effectively implement these quantitative metrics into the clinical workflow, this study is first in its kind to transition a decade’s worth of quantitative MRI advancements from academic settings into standard-of-care. Citation Format: John Virostko, Anna G Sorace, Kalina P Slavkova, Anum S Kazerouni, Angela M Jarrett, Julie C DiCarlo, Stefanie Woodard, Sarah Avery, Boone W Goodgame, Debra Patt, Thomas E Yankeelov. Quantitative multiparametric MRI predicts response to neoadjuvant therapy in the community setting [abstract]. In: Proceedings of the 2021 San Antonio Breast Cancer Symposium; 2021 Dec 7-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2022;82(4 Suppl):Abstract nr P3-03-03.

Comparison of DCE-MRI of murine model cancers with a low dose and high dose of contrast agent.

There are increasing concerns regarding intracellular accumulation of gadolinium (Gd) after multiple dynamic contrast enhanced (DCE) MRI scans. We investigated whether a low dose (LD) of Gd-based contrast agent is as effective as a high dose (HD) for quantitative analysis of DCE-MRI data, and evaluated the use of a split dose protocol to obtain new diagnostic parameters. Female C3H mice (n=6) were injected with mammary carcinoma cells in the hind leg. MRI experiments were performed on 9.4T scanner. DCE-MRI data were acquired with 1.5s temporal resolution before and after a LD (0.04mmol/kg), then again after 30min followed by a HD (0.2mmol/kg) bolus injection of Omniscan. The standard Tofts model was used to extract physiological parameters (Ktrans and ve) with the arterial input function derived from muscle reference tissue. In addition, an empirical mathematical model was used to characterize maximum contrast agent uptake (A), contrast agent uptake rate (α) and washout rate (β and γ). There were moderate to strong correlations (r=0.69-0.97, p<0001) for parameters Ktrans, ve, A, α and β from LD versus HD data. On average, tumor parameters obtained from LD data were significantly larger (p<0.05) than those from HD data. The parameter ratios, Ktrans, ve, A and α calculated from the LD data divided by the HD data, were all significantly larger than 1.0 (p<0.003) for tumor. T2* changes following contrast agent injection affected parameters calculated from HD data, but this was not the case for LD data. The results suggest that quantitative analysis of LD data may be at least as effective for cancer characterization as quantitative analysis of HD data. In addition, the combination of parameters from two different doses may provide useful diagnostic information.

Pharmacokinetic analysis of DCE-MRI data of locally advanced cervical carcinoma with the Brix model

Background: There is significant evidence that DCE-MRI may have the potential to provide clinically useful biomarkers of the outcome of locally advanced cervical carcinoma. However, there is no consensus on how to analyze DCE-MRI data to arrive at the most powerful biomarkers. The purpose of this study was to analyze DCE-MRI data of cervical cancer patients by using the Brix pharmacokinetic model and to compare the biomarkers derived from the Brix analysis with biomarkers determined by non-model-based analysis [i.e., low-enhancing tumor volume (LETV) and tumor volume with increasing signal (TVIS)] of the same patient cohort.Material and methods: DCE-MRI recordings of 80 patients (FIGO stage IB–IVA) treated with concurrent cisplatin-based chemoradiotherapy were analyzed voxel-by-voxel, and frequency distributions of the three parameters of the Brix model (ABrix, kep, and kel) were determined. Moreover, risk volumes were calculated from the Brix parameters and termed RV-ABrix, RV-kep, and RV-kel, where the RVs represent the tumor volume with voxel values below a threshold value determined by ROC analysis. Disease-free survival (DFS) and overall survival (OS) were used as measures of treatment outcome.Results: Significant associations between the median value or any other percentile value of ABrix, kep, or kel and treatment outcome were not found. However, RV-ABrix, RV-kep, and RV-kel correlated with DFS and OS. Multivariate analysis revealed that the prognostic power of RV-ABrix, RV-kep, and RV-kel was independent of well-established clinical prognostic factors. RV-ABrix, RV-kep, and RV-kel correlated with each other as well as with LETV and TVIS.Conclusion: Strong biomarkers of the outcome of locally advanced cervical carcinoma can be provided by subjecting DCE-MRI series to pharmacokinetic analysis using the Brix model. The prognostic power of these biomarkers is not necessarily superior to that of biomarkers identified by non-model-based analyses.

On differentiation between vasogenic edema and non-enhancing tumor in high-grade glioma patients using a support vector machine classifier based upon pre and post-surgery MRI images

PurposeHigh grade gliomas (HGGs) are infiltrative in nature. Differentiation between vasogenic edema and non-contrast enhancing tumor is difficult as both appear hyperintense in T2-W/FLAIR images. Most studies involving differentiation between vasogenic edema and non-enhancing tumor consider radiologist-based tumor delineation as the ground truth. However, analysis by a radiologist can be subjective and there remain both inter- and intra-rater differences. The objective of the current study is to develop a methodology for differentiation between non-enhancing tumor and vasogenic edema in HGG patients based on T1 perfusion MRI parameters, using a ground truth which is independent of a radiologist’s manual delineation of the tumor. Material and MethodsThis study included 9 HGG patients with pre- and post-surgery MRI data and 9 metastasis patients with pre-surgery MRI data. MRI data included conventional T1-W, T2-W, and FLAIR images and DCE-MRI dynamic images. In this study, the authors hypothesize that surgeried non-enhancing FLAIR hyperintense tissue, which was obtained using pre- and post-surgery MRI images of glioma patients, should be largely comprised of non-enhancing tumor. Hence this could be used as an alternative ground truth for the non-enhancing tumor region. Histological examination of the resected tissue was done for validation. Vasogenic edema was obtained from the non-enhancing FLAIR hyperintense region of metastasis patients, as they have a clear boundary between enhancing tumor and edema. DCE-MRI data analysis was performed to obtain T1 perfusion MRI parameters. Support Vector Machine (SVM) classification was performed using T1 perfusion MRI parameters to differentiate between non-enhancing tumor and vasogenic edema. Receiver-operating-characteristic (ROC) analysis was done on the results of the SVM classifier. For improved classification accuracy, the SVM output was post-processed via neighborhood smoothing. ResultsHistology results showed that resected tissue consists largely of tumorous tissue with 7.21 ± 4.05% edema and a small amount of healthy tissue. SVM-based classification provided a misclassification error of 8.4% in differentiation between non-enhancing tumor and vasogenic edema, which was further reduced to 2.4% using neighborhood smoothing. ConclusionThe current study proposes a semiautomatic method for segmentation between non-enhancing tumor and vasogenic edema in HGG patients, based on an SVM classifier trained on an alternative ground truth to a radiologist’s manual delineation of a tumor. The proposed methodology may prove to be a useful tool for pre- and post-operative evaluation of glioma patients.

Evaluation of B1 inhomogeneity effect on DCE-MRI data analysis of brain tumor patients at 3T

BackgroundDynamic-contrast-enhanced (DCE) MRI data acquired using gradient echo based sequences is affected by errors in flip angle (FA) due to transmit B1 inhomogeneity (B1inh). The purpose of the study was to evaluate the effect of B1inh on quantitative analysis of DCE-MRI data of human brain tumor patients and to evaluate the clinical significance of B1inh correction of perfusion parameters (PPs) on tumor grading.MethodsAn MRI study was conducted on 35 glioma patients at 3T. The patients had histologically confirmed glioma with 23 high-grade (HG) and 12 low-grade (LG). Data for B1-mapping, T1-mapping and DCE-MRI were acquired. Relative B1 maps (B1rel) were generated using the saturated-double-angle method. T1-maps were computed using the variable flip-angle method. Post-processing was performed for conversion of signal–intensity time (S(t)) curve to concentration–time (C(t)) curve followed by tracer kinetic analysis (Ktrans, Ve, Vp, Kep) and first pass analysis (CBV, CBF) using the general tracer-kinetic model. DCE-MRI data was analyzed without and with B1inh correction and errors in PPs were computed. Receiver-operating-characteristic (ROC) analysis was performed on HG and LG patients. Simulations were carried out to understand the effect of B1 inhomogeneity on DCE-MRI data analysis in a systematic way. S(t) curves mimicking those in tumor tissue, were generated and FA errors were introduced followed by error analysis of PPs. Dependence of FA-based errors on the concentration of contrast agent and on the duration of DCE-MRI data was also studied. Simulations were also done to obtain Ktrans of glioma patients at different B1rel values and see whether grading is affected or not.ResultsCurrent study shows that B1rel value higher than nominal results in an overestimation of C(t) curves as well as derived PPs and vice versa. Moreover, at same B1rel values, errors were large for larger values of C(t). Simulation results showed that grade of patients can change because of B1inh.ConclusionsB1inh in the human brain at 3T-MRI can introduce substantial errors in PPs derived from DCE-MRI data that might affect the accuracy of tumor grading, particularly for border zone cases. These errors can be mitigated using B1inh correction during DCE-MRI data analysis.

Comparison of region-of-interest-averaged and pixel-averaged analysis of DCE-MRI data based on simulations and pre-clinical experiments

Differences between region-of-interest (ROI) and pixel-by-pixel analysis of dynamic contrast enhanced (DCE) MRI data were investigated in this study with computer simulations and pre-clinical experiments. ROIs were simulated with 10, 50, 100, 200, 400, and 800 different pixels. For each pixel, a contrast agent concentration as a function of time, C(t), was calculated using the Tofts DCE-MRI model with randomly generated physiological parameters (Ktrans and ve) and the Parker population arterial input function. The average C(t) for each ROI was calculated and then Ktrans and ve for the ROI was extracted. The simulations were run 100 times for each ROI with new Ktrans and ve generated. In addition, white Gaussian noise was added to C(t) with 3, 6, and 12 dB signal-to-noise ratios to each C(t). For pre-clinical experiments, Copenhagen rats (n = 6) with implanted prostate tumors in the hind limb were used in this study. The DCE-MRI data were acquired with a temporal resolution of ~5 s in a 4.7 T animal scanner, before, during, and after a bolus injection (<5 s) of Gd-DTPA for a total imaging duration of ~10 min. Ktrans and ve were calculated in two ways: (i) by fitting C(t) for each pixel, and then averaging the pixel values over the entire ROI, and (ii) by averaging C(t) over the entire ROI, and then fitting averaged C(t) to extract Ktrans and ve. The simulation results showed that in heterogeneous ROIs, the pixel-by-pixel averaged Ktrans was ~25% to ~50% larger (p < 0.01) than the ROI-averaged Ktrans. At higher noise levels, the pixel-averaged Ktrans was greater than the ‘true’ Ktrans, but the ROI-averaged Ktrans was lower than the ‘true’ Ktrans. The ROI-averaged Ktrans was closer to the true Ktrans than pixel-averaged Ktrans for high noise levels. In pre-clinical experiments, the pixel-by-pixel averaged Ktrans was ~15% larger than the ROI-averaged Ktrans. Overall, with the Tofts model, the extracted physiological parameters from the pixel-by-pixel averages were larger than the ROI averages. These differences were dependent on the heterogeneity of the ROI.

Separation of benign and malignant breast lesions using dynamic contrast enhanced MRI in a biopsy cohort.

To assess the diagnostic utility of contrast kinetic analysis for breast lesions and background parenchyma of women undergoing MRI-guided biopsies, for whom standard clinical analysis had failed to separate benign and malignant lesions. This study included 115 women who had indeterminate lesions based on routine diagnostic breast MRI exams and underwent an MRI (3 Tesla) -guided biopsy of one or more lesions suspicious for breast cancer. Breast dynamic contrast-enhanced (DCE)-MRI was performed using a radial stack-of-stars three-dimensional spoiled gradient echo pulse sequence and modified k-space weighted image contrast image reconstruction. Contrast kinetic model analysis was conducted to characterize the contrast enhancement patterns measured in lesions and background parenchyma (BP). The transfer rate (Ktrans ), interstitial volume fraction (ve ), and vascular volume fraction (vp ) estimated from the lesion and BP were used to separate malignant from benign lesions. The patients with malignant lesions had significantly (P < 0.05) higher median lesion-Ktrans (0.081 min-1 ), higher median BP-Ktrans (0.032 min-1 ), and BP-vp (0.020) than those without malignant lesions (0.056 min-1 , 0.017 min-1 and 0.012, respectively). The area under the receiver operating characteristic curve (AUC) of the BP-Ktrans (0.687) was highest among the single parameters and higher than that of the lesion-Ktrans (0.664). The combined logistic regression model of lesion-Ktrans , lesion-ve , BP-Ktrans , BP-ve , and BP-vp had the highest AUC of 0.730. Our results suggest that the contrast kinetic analysis of DCE-MRI data can be used to differentiate the malignant lesions from the benign and high-risk lesions among the indeterminate breast lesions recommended for MRI-guided biopsy exams. 3 J. MAGN. RESON. IMAGING 2017;45:1385-1393.

Abstract 2710: Model evolution technique as a novel concept for characterization of tumor heterogeneity in dynamic contrast enhanced MRI studies

Abstract Introduction: Many studies have shown that tumor vascular network and the assortment of tumorous cells inside and on the periphery of solid tumors are spatially heterogeneous. Variation in cell packing density (VCPD), hypoxia, acidosis, and elevated interstitial fluid pressure (IFP) are main characteristic features of solid tumors. Elevated IFP and VCPD in solid tumors can be generally relevant to the pathological structures at the cellular level that is fundamental to understanding the chance of response to treatment and recurrence. Therefore, non-invasive quantification of tumor heterogeneity for the same types of tumors can play an important role in diagnosis and treatment planning. Hypothesis: In this pilot study, using Nested Model (NM) selection technique, Model Evolution (ME) concept is framed and introduced to quantify the evolutions of 3 different physiologically NM that are derived from standard Tofts model, throughout the course of Dynamic Contrast Enhanced (DCE) Magnetic Resonance Imaging (MRI) experiment. Using ME technique for pharmacokinetic (PK) modeling and DCE-MRI data analysis, a heterogeneity measure is formulated and introduced based on the evolutionary profile of the estimated extra-cellular extra-vascular (ve) volume. We hypothesized that the ME profiles in the course of DCE-MRI experiment, highly depend on the inward diffusion and outward convection of contrast agent concentration and contain abundant information for describing the compartmentalization and heterogeneity levels of solid tumors. Material and Methods: 24 athymic Nude rats with U251n rat tumor model of cerebral tumor were studied. Look-Locker T1 mapping and DCE-MRI experiments (Dual Gradient Echo, 150 image sets at 4.0 sec intervals over 10 min: matrix = 128×64, five 2.0 mm slices, NE = 2, NA = 1, TE/TE/TR = 2.0/4.0/40ms with bolus intravenous injection of the Magnevist at 0.25 mmol/kg) acquired at 7T field strengths. In each animal, in-vivo measurement of tumor IFP was done right after the DCE-MRI experiment using a wick-in-needle technique. The ME technique was applied on DCE-MR data of 24 U251n rat tumors to characterize the heterogeneity of each tumor and then the results were compared to their known in vivo measure of IFPs. Results and Conclusions: Results of this pilot study clearly attest that the evolutionary profile of ve can be used to characterize the heterogeneity level of solid tumors. The ME results imply that as the slop of the evolutionary profile increases, the IFP of tumor increases. Also, the latency of the profile during the course of MR experiment can reliably explain the tumor compartmentalization and their elevated IFP. This pilot study confirms that the ME concept can make a paradigm shift in non-invasive quantification of tumor heterogeneity from DCE-MRI studies Citation Format: Hassan Bagher-Ebadian, Azimeh NV Dehkordi, Rasha Alamgharibi, David Nathanson, Hamid Soltanian-Zadeh, Arbab S. Ali, Stephen Brown, Meser M. Ali, Tom Mikkelsen, James R. Ewing. Model evolution technique as a novel concept for characterization of tumor heterogeneity in dynamic contrast enhanced MRI studies. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 2710.

NIMG-04ADAPTIVE AND STANDARD PHARMACOKINETIC MODEL SELECTION TECHNIQUES IN QUANTITATIVE ANALYSIS OF DYNAMIC CONTRAST ENHANCED MRI STUDIES

In quantitative analysis of tissue vascular permeability using Dynamic-Contrast-Enhanced (DCE) MR data analysis, one of the main challenges is to choose the best Pharmacokinetic (PK) model among competing models to describe the behavior of the time trace of contrast agent (CA) concentration. The F-Statistic (F-test), Akaike-Information-Criterion (AIC), Bayesian-Information-Criterion (BIC), and Log-Likelihood-Ratio (LLR) tests are the most widely used classical Model-Selection (MS) techniques. Our group has recently introduced an Adaptive-MS (AMS) method to perform PK-MS in DCE-MRI data. This study investigates the performance of different MS techniques (Classical and Adaptive) for selection of the PK model as well as the impact of the recruited technique on the estimation of PK parameters. In this study, three physiologically Nested Models were used to describe possible physiological conditions of underlying tissue pathology as Model 1, 2, and 3 respectively: The tissue-vascular-compartment is filled with CA with no outward-leakage, with outward-leakage but no evidence of back-flux, and with both outward-and backward-flux. Plasma volume vp, vp, and ktrans, vp, ktrans and kep, are the PK parameters for Models 1, 2 and 3 respectively. 21 Arterial Input Function curves were used to simulate the CA profile. By varying PK parameters (time-resolution = 5.03 sec), 9072 time traces for 12 levels of SNRs were generated. Then confusion matrices were computed for all the MS techniques over 9072 tested hypotheses. The performance of the AMS is superior to the other classical techniques. Other techniques show performances in the following order: LLR, BIC, AICc, AIC and F-Test. All techniques never miss any leaky tissue. Also the AMS generates significantly less-biased estimates of PK parameters compared to the classical techniques while the LLR and BIC methods outperform the other classical techniques. This study suggests that AMS, LLR, and BIC are the best candidates among the MS techniques for PK analysis of DCE-MRI data.

GPU-accelerated compartmental modeling analysis of DCE-MRI data from glioblastoma patients treated with bevacizumab.

The compartment model analysis using medical imaging data is the well-established but extremely time consuming technique for quantifying the changes in microvascular physiology of targeted organs in clinical patients after antivascular therapies. In this paper, we present a first graphics processing unit-accelerated method for compartmental modeling of medical imaging data. Using this approach, we performed the analysis of dynamic contrast-enhanced magnetic resonance imaging data from bevacizumab-treated glioblastoma patients in less than one minute per slice without losing accuracy. This approach reduced the computation time by more than 120-fold comparing to a central processing unit-based method that performed the analogous analysis steps in serial and more than 17-fold comparing to the algorithm that optimized for central processing unit computation. The method developed in this study could be of significant utility in reducing the computational times required to assess tumor physiology from dynamic contrast-enhanced magnetic resonance imaging data in preclinical and clinical development of antivascular therapies and related fields.

A new approach in DCE MRI data analysis for differentiating benign and malignant breast lesions.

Dynamic contrast enhanced MRI (DCE MRI) is able to reflect changes in vascularity, vessel permeability and extracellular diffusion space of tissues. The goal of this study was to investigate the use of DCE MRI to differentiate benign and malignant breast lesions. From a database, five patients with malignant and five patients with benign lesions were randomly chosen. All patients underwent measurement in a 3T MR scanner using a breast coil. A series of T1-weighted MRI were performed using an intravenously delivered contrast agent. Then, 17 postcontrast sets were acquired within a timeframe of 13 seconds. All DCE MRI data were evaluated using the JIM image analysis package. We observed changes in signal intensity over the acquisition time - curves of dynamic contrast enhancement. We investigated parts of the curves with the largest increase in signal intensity during the timeframe. For further comparison, we used values of the highest signal intensity increases between the timeframes. Analysis of these results led to the proposal that the threshold between benign and malignant lesion had a relative value of 100. Furthermore, there was a significant difference between these two types of lesions.

A diffusion-compensated model for the analysis of DCE-MRI data: theory, simulations and experimental results

Accurate quantification of pharmacokinetic parameters in dynamic contrast-enhanced (DCE) MRI may be affected by the passive diffusion of contrast agent (CA) within the tissue. By introducing an additional term into the standard Tofts–Kety (STK) model, we correct for the effects of CA diffusion. We first develop the theory describing a CA diffusion corrected STK model (DTK). The model is then tested in simulation with simple models of diffusion. The DTK model is also fit to 18 in vivo DCE-MRI acquisitions from murine models of cancer and results are compared to those from the STK model. The DTK model returned estimates with significantly lower error than the STK model (p ≪ 0.001). In poorly perfused (i.e., Ktrans ≤ 0.05 min−1) regions the STK model returned unphysical ve values, while the DTK model estimated ve with less than 7% error in noise-free simulations. Results in vivo data revealed similar trends. For voxels with low Ktrans values and late peak concentration times the STK model returned ve estimates >1.0 in 40% of the voxels as compared to only 16% for the DTK model. The DTK model presented here shows promise in estimating accurate kinetic parameters in the presence of passive contrast agent diffusion.

Spatially regularized estimation for the analysis of DCE-MRI data

Competing compartment models of different complexities have been used for the quantitative analysis of Dynamic Contrast-Enhanced Magnetic Resonance Imaging data. We present a spatial Elastic Net approach that allows to estimate the number of compartments for each voxel such that the model complexity is not fixed a priori. A multi-compartment approach is considered, which is translated into a restricted least square model selection problem. This is done by using a set of basis functions for a given set of candidate rate constants. The form of the basis functions is derived from a kinetic model and thus describes the contribution of a specific compartment. Using a spatial Elastic Net estimator, we chose a sparse set of basis functions per voxel, and hence, rate constants of compartments. The spatial penalty takes into account the voxel structure of an image and performs better than a penalty treating voxels independently. The proposed estimation method is evaluated for simulated images and applied to an in-vivo data set.

SU-D-BRB-06: Comprehensive Population-Averaged Arterial Input Function (AIF) for DCE-MRI of Head and Neck Cancer.

To generate a composite population-averaged(PA)-AIF for quantitative analysis of DCE-MRI data in head and neck (H&N) patients that is based on the right (RT) and left (LT) carotids, two pre-treatment scans, and one post-treatment scan. Twenty patients were imaged while undergoing concurrent chemoradiation (CRT) for H&N malignancies. The imaging protocol (1) included two baseline scans one week apart (Base1, Base2), and one scan 1 week post-CRT (Post). For each patient and time point, regions of interest (ROIs) in both the RT and LT carotids were drawn on coronal images. The plasma concentration curves of all ROIs were averaged and fit to a bi-exponential decay function to obtain the final PA- AIF (AvgAll). The ROIs were also divided by time point to obtain AvgBasel, AvgBase2, and AvgPost AIFs. The vascular transfer constant for both primary and nodes, Ktrans , was calculated (iCAD, Inc.) using the 4 AIFs, as well as the generic Weinmann's AIF. The median Ktrans values resulting from the AvgAll AIF were compared using Bland-Altman plots with the ones obtained from each individual time point. The Wilcoxon signed-rank test was used to compare the proposed AvgAll AIF and the generic AIF. The plasma parameters for the AvgAll AIF were a1,2=27.1135/17.6486 kg/liter, m 1,2=11.7525/0.2054 min-1 . The differences in Ktrans values using these coefficients vs. Weinmann's were statistically significant (p<0.0001). The median Ktrans values from the AvgBasel,AvgBase2, and AvgPost AIFs were, in most cases, not significantly different from the AvgAll values, indicating that the latter is appropriate foruse at all time points. A population-averaged AIF for H&N was generated that accounts for differences in RT vs. LT carotids, day-today AIF fluctuations, and treatment-induced AIF changes. It is not necessary to measure a post-treatment AIF to evaluate treatment-induced Ktrans changes.l. Craciunescu et al., MedPhys, 37, 6, 2683, 2010.

Dynamic contrast-enhanced MRI in clinical trials of antivascular therapies

About 100 early-phase clinical trials and investigator-led studies of targeted antivascular therapies--both anti-angiogenic and vascular-targeting agents--have reported data derived from T1-weighted dynamic contrast-enhanced (DCE)-MRI. However, the role of DCE-MRI for decision making during the drug-development process remains controversial. Despite well-documented guidelines on image acquisition and analysis, several key questions concerning the role of this technique in early-phase trial design remain unanswered. This Review describes studies of single-agent antivascular therapies, in which DCE-MRI parameters are incorporated as pharmacodynamic biomarkers. We discuss whether these parameters, such as volume transfer constant (K(trans)), are reproducible and reliable biomarkers of both drug efficacy and proof of concept, and whether they assist in dose selection and drug scheduling for subsequent phase II trials. Emerging evidence indicates that multiparametric analysis of DCE-MRI data offers greater insight into the mechanism of drug action than studies measuring a single parameter, such as K(trans). We also provide an overview of current data and appraise the future directions of this technique in oncology trials. Finally, major hurdles in imaging biomarker development, validation and qualification that hinder a wide application of DCE-MRI techniques in clinical trials are addressed.

Improved correlation to quantitative DCE‐MRI pharmacokinetic parameters using a modified initial area under the uptake curve (mIAUC) approach

To propose a modified initial area under the uptake curve (mIAUC) dynamic contrast-enhanced (DCE)-MRI approach to achieve better distinction of underlying physiology. The mIAUC is formulated on common characteristics of tissue contrast uptake curves observed over a wide range of physiological conditions. The new metrics, IAUC(Ktrans) and IAUC(ve), are related to the transfer constant (K(trans)) and interstitial volume (v(e)), respectively. Tissue uptake curves were simulated over a range of physiological values and analyzed using the proposed mIAUC, conventional IAUC, and Tofts' pharmacokinetic model. IAUC(Ktrans) and IAUC(ve) are highly correlated to the true K(trans) and v(e) (rho = 0.97 and 0.95, respectively), approaching the performance of Tofts' model based on a 1.5-s sampled arterial input function (AIF) (rho = 0.98 and 0.98) under noise conditions typical in DCE-MRI experiments. Lower correlations were obtained with conventional IAUC(60) and IAUC(120) (rho = 0.82 and 0.61) and Tofts' parameters fitted using a biexponential AIF (rho = 0.81 and 0.90). The proposed mIAUC approach retains advantages associated with nonmodel based methods (robust to noise and model fit failure, obviates need for an AIF) while providing better distinction of underlying physiological parameters. It can be a valuable alternative to pharmacokinetic modelling in the analysis of DCE-MRI data.

Incorporating a vascular term into a reference region model for the analysis of DCE-MRI data: a simulation study

A vascular term was incorporated into a reference region (RR) model analysis of DCE-MRI data, and its effect on the accuracy of the model in estimating tissue kinetic parameters in a tissue of interest (TOI) was systematically investigated through computer simulations. Errors in the TOI volume transfer constant (Ktrans,TOI) and TOI extravascular extracellular volume (ve,TOI) that result when the fractional plasma volume (vp) was included in (1) neither region, (2) TOI only (3) both regions were investigated. For nominal values of tumor kinetic parameters (ve,TOI = 0.40 and Ktrans,TOI = 0.25 min−1), if the vascular term was included in neither region or the TOI only, Ktrans,TOI error was within 20% for 0.03 < vp,TOI < 0.10, and ve,TOI error was within 20% for the range of vp,TOI studied (0.01–0.10). The effects of temporal resolution were shown to be complex, and in some cases errors increased with increasing temporal resolution.