DCE-MRI Data Research Articles

Myocardial perfusion quantification using simultaneously acquired 13 NH3 -ammonia PET and dynamic contrast-enhanced MRI in patients at rest and stress.

Systematic differences with respect to myocardial perfusion quantification exist between DCE-MRI and PET. Using the potential of integrated PET/MRI, this study was conceived to compare perfusion quantification on the basis of simultaneously acquired 13 NH3 -ammonia PET and DCE-MRI data in patients at rest and stress. Twenty-nine patients were examined on a 3T PET/MRI scanner. DCE-MRI was implemented in dual-sequence design and additional T1 mapping for signal normalization. Four different deconvolution methods including a modified version of the Fermi technique were compared against 13 NH3 -ammonia results. Cohort-average flow comparison yielded higher resting flows for DCE-MRI than for PET and, therefore, significantly lower DCE-MRI perfusion ratios under the common assumption of equal arterial and tissue hematocrit. Absolute flow values were strongly correlated in both slice-average (R2 = 0.82) and regional (R2 = 0.7) evaluations. Different DCE-MRI deconvolution methods yielded similar flow result with exception of an unconstrained Fermi method exhibiting outliers at high flows when compared with PET. Thresholds for Ischemia classification may not be directly tradable between PET and MRI flow values. Differences in perfusion ratios between PET and DCE-MRI may be lifted by using stress/rest-specific hematocrit conversion. Proper physiological constraints are advised in model-constrained deconvolution.

Quantitative dynamic contrast-enhanced MR imaging for differentiating benign, borderline, and malignant ovarian tumors

This study aimed to investigate the diagnostic performance of quantitative DCE-MRI for characterizing ovarian tumors. We prospectively assessed the differences of quantitative DCE-MRI parameters (Ktrans, kep, and ve) among 15 benign, 28 borderline, and 66 malignant ovarian tumors; and between type I (n = 28) and type II (n = 29) of epithelial ovarian carcinomas (EOCs). DCE-MRI data were analyzed using whole solid tumor volume region of interest (ROI) method, and quantitative parameters were calculated based on a modified Tofts model. The non-parametric Kruskal-Wallis test, Mann-Whitney U test, Pearson's chi-square test, intraclass correlation coefficient (ICC), variance test, and receiver operating characteristic curves (ROC) were used for statistical analysis. The largest Ktrans and kep values were observed in ovarian malignant tumors, followed by borderline and benign tumors (all P < 0.001). Kep was the better parameter for differentiating benign tumors from borderline and malignant tumors, with a sensitivity of 89.3% and 95.5%, a specificity of 86.7% and 100%, an accuracy of 88.4% and 96.3%, and an area under the curve (AUC) of 0.94 and 0.992, respectively, whereas Ktrans was better for differentiating borderline from malignant tumors with a sensitivity of 60.7%, a specificity of 78.8%, an accuracy of 73.4%, and an AUC of 0.743. In addition, a combination with kep could further improve the sensitivity to 78.9%. The median Ktrans and kep values were significantly higher in type II than in type I EOCs. DCE-MRI with volume quantification is a technically feasible method, and can be used for the differentiation of ovarian tumors and for discriminating between type I and type II EOCs.

Toward a noninvasive estimate of interstitial fluid pressure by dynamic contrast-enhanced MRI in a rat model of cerebral tumor.

This study demonstrates a DCE-MRI estimate of tumor interstitial fluid pressure (TIFP) and hydraulic conductivity in a rat model of glioblastoma, with validation against an invasive wick-in-needle (WIN) technique. An elevated TIFP is considered a mark of aggressiveness, and a decreased TIFP a predictor of response to therapy. The DCE-MRI studies were conducted in 36 athymic rats (controls and posttreatment animals) with implanted U251 cerebral tumors, and with TIFP measured using a WIN method. Using a model selection paradigm and a novel application of Patlak and Logan plots to DCE-MRI data, the MRI parameters required for estimating TIFP noninvasively were estimated. Two models, a fluid-mechanical model and a multivariate empirical model, were used for estimating TIFP, as verified against WIN-TIFP. Using DCE-MRI, the mean estimated hydraulic conductivity (MRI-K) in U251 tumors was (2.3 ± 3.1) × 10-5 (mm2 /mmHg-s) in control studies. Significant positive correlations were found between WIN-TIFP and MRI-TIFP in both mechanical and empirical models. For instance, in the control group of the fluid-mechanical model, MRI-TIFP was a strong predictor of WIN-TIFP (R2 = 0.76, p < .0001). A similar result was found in the bevacizumab-treated group of the empirical model (R2 = 0.93, p = .014). This research suggests that MRI dynamic studies contain enough information to noninvasively estimate TIFP in this, and possibly other, tumor models, and thus might be used to assess tumor aggressiveness and response to therapy.

Concurrent Respiratory Motion Correction of Abdominal PET and Dynamic Contrast-Enhanced-MRI Using a Compressed Sensing Approach.

We present an approach for concurrent reconstruction of respiratory motion-compensated abdominal dynamic contrast-enhanced (DCE)-MRI and PET data in an integrated PET/MR scanner. The MR and PET reconstructions share the same motion vector fields derived from radial MR data; the approach is robust to changes in respiratory pattern and does not increase the total acquisition time. Methods: PET and DCE-MRI data of 12 oncologic patients were simultaneously acquired for 6 min on an integrated PET/MR system after administration of 18F-FDG and gadoterate meglumine. Golden-angle radial MR data were continuously acquired simultaneously with PET data and sorted into multiple motion phases on the basis of a respiratory signal derived directly from the radial MR data. The resulting multidimensional dataset was reconstructed using a compressed sensing approach that exploits sparsity among respiratory phases. Motion vector fields obtained using the full 6-min (MC6-min) and only the last 1 min (MC1-min) of data were incorporated into the PET reconstruction to obtain motion-corrected PET images and in an MR iterative reconstruction algorithm to produce a series of motion-corrected DCE-MR images (moco_GRASP). The motion-correction methods (MC6-min and MC1-min) were evaluated by qualitative analysis of the MR images and quantitative analysis of SUVmax and SUVmean, contrast, signal-to-noise ratio (SNR), and lesion volume in the PET images. Results: Motion-corrected MC6-min PET images demonstrated 30%, 23%, 34%, and 18% increases in average SUVmax, SUVmean, contrast, and SNR and an average 40% reduction in lesion volume with respect to the non-motion-corrected PET images. The changes in these figures of merit were smaller but still substantial for the MC1-min protocol: 19%, 10%, 15%, and 9% increases in average SUVmax, SUVmean, contrast, and SNR; and a 28% reduction in lesion volume. Moco_GRASP images were deemed of acceptable or better diagnostic image quality with respect to conventional breath-hold Cartesian volumetric interpolated breath-hold examination acquisitions. Conclusion: We presented a method that allows the simultaneous acquisition of respiratory motion-corrected diagnostic quality DCE-MRI and quantitatively accurate PET data in an integrated PET/MR scanner with negligible prolongation in acquisition time compared with routine PET/DCE-MRI protocols.

Early Changes in Tumor Perfusion from T1-Weighted Dynamic Contrast-Enhanced MRI following Neural Stem Cell-Mediated Therapy of Recurrent High-Grade Glioma Correlate with Overall Survival.

Background The aim of this study was to correlate T1-weighted dynamic contrast-enhanced MRI- (DCE-MRI-) derived perfusion parameters with overall survival of recurrent high-grade glioma patients who received neural stem cell- (NSC-) mediated enzyme/prodrug gene therapy. Methods A total of 12 patients were included in this retrospective study. All patients were enrolled in a first-in-human study (NCT01172964) of NSC-mediated therapy for recurrent high-grade glioma. DCE-MRI data from all patients were collected and analyzed at three time points: MRI#1—day 1 postsurgery/treatment, MRI#2— day 7 ± 3 posttreatment, and MRI#3—one-month follow-up. Plasma volume (V p), permeability (K tr), and leakage (λ tr) perfusion parameters were calculated by fitting a pharmacokinetic model to the DCE-MRI data. The contrast-enhancing (CE) volume was measured from the last dynamic phase acquired in the DCE sequence. Perfusion parameters and CE at each MRI time point were recorded along with their relative change between MRI#2 and MRI#3 (Δ32). Cox regression was used to analyze patient survival. Results At MRI#1 and at MRI#3, none of the parameters showed a significant correlation with overall survival (OS). However, at MRI#2, CE and λ tr were significantly associated with OS (p < 0.05). The relative λ tr and V p from timepoint 2 to timepoint 3 (Δ32 λ tr and Δ32 V p) were each associated with a higher hazard ratio (p < 0.05). All parameters were highly correlated, resulting in a multivariate model for OS including only CE at MRI#2 and Δ32 V p, with an R 2 of 0.89. Conclusion The change in perfusion parameter values from 1 week to 1 month following NSC-mediated therapy combined with contrast-enhancing volume may be a useful biomarker to predict overall survival in patients with recurrent high-grade glioma.

Spatiotemporal features of DCE-MRI for breast cancer diagnosis

Background and ObjectiveBreast cancer is a major cause of mortality among women if not treated in early stages. Previous works developed non-invasive diagnosis methods using imaging data, focusing on specific sets of features that can be called spatial features or temporal features. However, limited set of features carry limited information, requiring complex classification methods to diagnose the disease. For non-invasive diagnosis, different imaging modalities can be used. DCE-MRI is one of the best imaging techniques that provides temporal information about the kinetics of the contrast agent in suspicious lesions along with acceptable spatial resolution. MethodsWe have extracted and studied a comprehensive set of features from spatiotemporal space to obtain maximum available information from the DCE-MRI data. Then, we have applied a feature fusion technique to remove common information and extract a feature set with maximum information to be used by a simple classification method. We have also implemented conventional feature selection and classification methods and compared them with our proposed approach. ResultsExperimental results obtained from DCE-MRI data of 26 biopsy or short-term follow-up proven patients illustrate that the proposed method outperforms alternative methods. The proposed method achieves a classification accuracy of 99% without missing any of the malignant cases. ConclusionsThe proposed method may help physicians determine the likelihood of malignancy in breast cancer using DCE-MRI without biopsy.

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.

Performance of Ultrafast DCE-MRI for Diagnosis of Prostate Cancer

This study aimed to test high temporal resolution dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) for different zones of the prostate and evaluate its performance in the diagnosis of prostate cancer (PCa). Determine whether the addition of ultrafast DCE-MRI improves the performance of multiparametric MRI. Patients (n = 20) with pathologically confirmed PCa underwent preoperative 3T MRI with T2-weighted, diffusion-weighted, and high temporal resolution (~2.2 seconds) DCE-MRI using gadoterate meglumine (Guerbet, Bloomington, IN) without an endorectal coil. DCE-MRI data were analyzed by fitting signal intensity with an empirical mathematical model to obtain parameters: percent signal enhancement, enhancement rate (α), washout rate (β), initial enhancement slope, and enhancement start time along with apparent diffusion coefficient (ADC) and T2 values. Regions of interests were placed on sites of prostatectomy verified malignancy (n = 46) and normal tissue (n = 71) from different zones. Cancer (α = 6.45 ± 4.71 s-1, β = 0.067 ± 0.042 s-1, slope = 3.78 ± 1.90 s-1) showed significantly (P <.05) faster signal enhancement and washout rates than normal tissue (α = 3.0 ± 2.1 s-1, β = 0.034 ± 0.050 s-1, slope = 1.9 ± 1.4 s-1), but showed similar percentage signal enhancement and enhancement start time. Receiver operating characteristic analysis showed area under the curve for DCE parameters was comparable to ADC and T2 in the peripheral (DCE 0.67-0.82, ADC 0.80, T2 0.89) and transition zones (DCE 0.61-0.72, ADC 0.69, T2 0.75), but higher in the central zone (DCE 0.79-0.88, ADC 0.45, T2 0.45) and anterior fibromuscular stroma (DCE 0.86-0.89, ADC 0.35, T2 0.12). Importantly, combining DCE with ADC and T2 increased area under the curve by ~30%, further improving the diagnostic accuracy of PCa detection. Quantitative parameters from empirical mathematical model fits to ultrafast DCE-MRI improve diagnosis of PCa. DCE-MRI with higher temporal resolution may capture clinically useful information for PCa diagnosis that would be missed by low temporal resolution DCE-MRI. This new information could improve the performance of multiparametric MRI in PCa detection.

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.

Pattern recognition and pharmacokinetic methods on DCE-MRI data for tumor hypoxia mapping in sarcoma

The main purpose of this study is to analyze the intrinsic tumor physiologic characteristics in patients with sarcoma through model-free analysis of dynamic contrast enhanced MR imaging data (DCE-MRI). Clinical data were collected from three patients with two different types of histologically proven sarcomas who underwent conventional and advanced MRI examination prior to excision. An advanced matrix factorization algorithm has been applied to the data, resulting in the identification of the principal time-signal uptake curves of DCE-MRI data, which were used to characterize the physiology of the tumor area, described by three different perfusion patterns i.e. hypoxic, well-perfused and necrotic one. The performance of the algorithm was tested by applying different initialization approaches with subsequent comparison of their results. The algorithm was proven to be robust and led to the consistent segmentation of the tumor area in three regions of different perfusion, i.e. well-perfused, hypoxic and necrotic. Results from the model-free approach were compared with a widely used pharmacokinetic (PK) model revealing significant correlations.

Optimized Multistable Stochastic Resonance for the Enhancement of Pituitary Microadenoma in MRI.

Magnetic resonance imaging (MRI) is the modality of choice as far as imaging diagnosis of pathologies in the pituitary gland is concerned. Furthermore, the advent of dynamic contrast enhanced (DCE) has enhanced the capability of this modality in detecting minute benign but endocrinologically significant tumors called microadenoma. These lesions are visible with difficulty and a low confidence level in routine MRI sequences, even after administration of intravenous gadolinium. Techniques to enhance the visualization of such foci would be an asset in improving the overall accuracy of DCE-MRI for detection of pituitary microadenomas. The present study proposes an algorithm for postprocessing DCE-MRI data using multistable stochastic resonance (MSSR) technique. Multiobjective ant lion optimization optimizes the contrast enhancement factor (CEF) and anisotropy of an image by varying the parameters associated with the dynamics of MSSR. The marked regions of interest (ROIs) are labeled as normal and microadenoma of pituitary obtained with increased level of accuracy and confidence using proposed algorithm. The increased difference between the mean intensity curves obtained using these ROIs validated the obtained subjective results. Furthermore, the proposed MSSR-based algorithm has been evaluated on standard T1 and T2 weighted BrainWeb dataset images and quantified in terms of CEF, peak signal to noise ratio (PSNR), structure similarity index measure (SSIM), and universal quality index (UQI). The obtained mean values of CEF 1.22, PSNR 27.68, SSIM 0.75, UQI 0.83 for twenty dataset images were highest among considered contrast enhancement algorithms for the comparison.

Modeling Dynamic Contrast-Enhanced MRI Data with a Constrained Local AIF.

This study aims to develop a constrained local arterial input function (cL-AIF) to improve quantitative analysis of dynamic contrast-enhanced (DCE)-magnetic resonance imaging (MRI) data by accounting for the contrast-agent bolus amplitude error in the voxel-specific AIF. Bayesian probability theory-based parameter estimation and model selection were used to compare tracer kinetic modeling employing either the measured remote-AIF (R-AIF, i.e., the traditional approach) or an inferred cL-AIF against both in silico DCE-MRI data and clinical, cervical cancer DCE-MRI data. When the data model included the cL-AIF, tracer kinetic parameters were correctly estimated from in silico data under contrast-to-noise conditions typical of clinical DCE-MRI experiments. Considering the clinical cervical cancer data, Bayesian model selection was performed for all tumor voxels of the 16 patients (35,602 voxels in total). Among those voxels, a tracer kinetic model that employed the voxel-specific cL-AIF was preferred (i.e., had a higher posterior probability) in 80% of the voxels compared to the direct use of a single R-AIF. Maps of spatial variation in voxel-specific AIF bolus amplitude and arrival time for heterogeneous tissues, such as cervical cancer, are accessible with the cL-AIF approach. The cL-AIF method, which estimates unique local-AIF amplitude and arrival time for each voxel within the tissue of interest, provides better modeling of DCE-MRI data than the use of a single, measured R-AIF. The Bayesian-based data analysis described herein affords estimates of uncertainties for each model parameter, via posterior probability density functions, and voxel-wise comparison across methods/models, via model selection in data modeling.

Prognostic Role of Conventional and Dynamic Contrast-Enhanced MRI in Optic Pathway Gliomas.

The natural history of optic pathway glioma (OPG) is highly variable and unpredictable. We present a pilot study of the prognostic role of conventional and dynamic contrast-enhanced magnetic resonance imaging (DCE MRI) in the evaluation of OPG. We retrospectively reviewed 17 patients with 20 pretreatment OPG lesions who underwent conventional and DCE MRI between January 2010 and December 2016. Conventional MRI was evaluated for enhancement pattern, cystic component, optic nerve tortuosity, optic nerve dural ectasia, and optic nerve perineural thickening. The DCE MRI data were analyzed for quantitative parameters using the two-compartment pharmacokinetic model (Ktrans , kep , and ve ) and for semiquantitative parameters based on time-signal intensity curve (AUC60 , peak, and wash-in). The results were compared with the clinically progressive or nonprogressive tumor. Five progressive OPGs and 15 nonprogressive OPGs were included. Conventional MRI findings of diffuse enhancement and cystic component were significantly correlated with progressive OPGs (both P = .01). Conventional MRI yielded sensitivity of 60%, specificity of 100%, and accuracy of 90%. Ktrans , kep , and ve as well as AUC60 , peak, and wash-in were significantly higher in progressive OPGs (P < .05). Using DCE MRI increased diagnostic performance up to a sensitivity of 100%, specificity of 93%, and accuracy of 95%. DCE MRI accurately distinguished progressive and nonprogressive OPGs, with high sensitivities and specificities. DCE MRI has a significant prognostic role in predicting progressive OPGs, thus making it useful for the identification of patients who need close clinical and imaging follow-up.

Movement correction in DCE-MRI through windowed and reconstruction dynamic mode decomposition

Images of the kidneys using dynamic contrast-enhanced magnetic resonance renography (DCE-MRR) contains unwanted complex organ motion due to respiration. This gives rise to motion artefacts that hinder the clinical assessment of kidney function. However, due to the rapid change in contrast agent within the DCE-MR image sequence, commonly used intensity-based image registration techniques are likely to fail. While semi-automated approaches involving human experts are a possible alternative, they pose significant drawbacks including inter-observer variability, and the bottleneck introduced through manual inspection of the multiplicity of images produced during a DCE-MRR study. To address this issue, we present a novel automated, registration-free movement correction approach based on windowed and reconstruction variants of dynamic mode decomposition (WR-DMD). Our proposed method is validated on ten different healthy volunteers’ kidney DCE-MRI data sets. The results, using block-matching-block evaluation on the image sequence produced by WR-DMD, show the elimination of 99% of mean motion magnitude when compared to the original data sets, thereby demonstrating the viability of automatic movement correction using WR-DMD.

In Vivo DCE-MRI for the Discrimination Between Glioblastoma and Radiation Necrosis in Rats

In this study, the potential of semiquantitative and quantitative analysis of dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) was investigated to differentiate glioblastoma (GB) from radiation necrosis (RN) in rats. F98 GB growth was seen on MRI 8-23days post-inoculation (n=15). RN lesions developed 6-8months post-irradiation (n=10). DCE-MRI was acquired using a fast low-angle shot (FLASH) sequence. Regions of interest (ROIs) encompassed peripheral contrast enhancement in GB (n=15) and RN (n=10) as well as central necrosis within these lesions (GB (n=4), RN (n=3)). Dynamic contrast-enhanced time series, obtained from the DCE-MRI data, were fitted to determine four function variables (amplitude A, offset from zero C, wash-in rate k, and wash-out rate D) as well as maximal intensity (ImaxF) and time to peak (TTPF). Secondly, maps of semiquantitative and quantitative parameters (extended Tofts model) were created using Olea Sphere (O). Semiquantitative DCE-MRI parameters included wash-inO, wash-outO, area under the curve (AUCO), maximal intensity (ImaxO), and time to peak (TTPO). Quantitative parameters included the rate constant plasma to extravascular-extracellular space (EES) (K trans), the rate constant EES to plasma (K ep), plasma volume (V p), and EES volume (V e). All (semi)quantitative parameters were compared between GB and RN using the Mann-Whitney U test. ROC analysis was performed. Wash-in rate (k) and wash-out rate (D) were significantly higher in GB compared to RN using curve fitting (p=0.016 and p=0.014). TTPF and TTPO were significantly lower in GB compared to RN (p=0.001 and p=0.005, respectively). The highest sensitivity (87%) and specificity (80%) were obtained for TTPF by applying a threshold of 581s. K trans, K ep, and V e were not significantly different between GB and RN. A trend towards higher V p values was found in GB compared to RN, indicating angiogenesis in GB (p=0.075). Based on our results, in a rat model of GB and RN, wash-in rate, wash-out rate, and the time to peak extracted from DCE-MRI time series data may be useful to discriminate GB from RN.

An adaptive model for rapid and direct estimation of extravascular extracellular space in dynamic contrast enhanced MRI studies.

Extravascular extracellular space (ve ) is a key parameter to characterize the tissue of cerebral tumors. This study introduces an artificial neural network (ANN) as a fast, direct, and accurate estimator of ve from a time trace of the longitudinal relaxation rate, ΔR1 (R1 =1/T1 ), in DCE-MRI studies. Using the extended Tofts equation, a set of ΔR1 profiles was simulated in the presence of eight different signal to noise ratios. A set of gain- and noise-insensitive features was generated from the simulated ΔR1 profiles and used as the ANN training set. A K-fold cross-validation method was employed for training, testing, and optimization of the ANN. The performance of the optimal ANN (12:6:1, 12 features as input vector, six neurons in hidden layer, and one output) in estimating ve at a resolution of 10% (error of ±5%) was 82%. The ANN was applied on DCE-MRI data of 26 glioblastoma patients to estimate ve in tumor regions. Its results were compared with the maximum likelihood estimation (MLE) of ve . The two techniques showed a strong agreement (r=0.82, p<0.0001). Results implied that the perfected ANN was less sensitive to noise and outperformed the MLE method in estimation of ve .

Repeatability and response to therapy of dynamic contrast-enhanced magnetic resonance imaging biomarkers in rheumatoid arthritis in a large multicentre trial setting

ObjectivesTo determine the repeatability and response to therapy of dynamic contrast-enhanced (DCE) MRI biomarkers of synovitis in the hand and wrist of rheumatoid arthritis (RA) patients, and in particular the performance of the transfer constant Ktrans, in a multicentre trial setting.MethodsDCE-MRI and RA MRI scoring (RAMRIS) were performed with meticulous standardisation at baseline and 6 and 24 weeks in a substudy of fostamatinib monotherapy in reducing synovitis compared with placebo or adalimumab. Analysis employed statistical shape modelling to avoid biased regions-of-interest, kinetic modelling and heuristic analyses. Repeatability was also evaluated.ResultsAt early study termination, DCE-MRI data had been acquired from 58 patients in 19 imaging centres. Ktrans intra-subject coefficient of variation (N = 14) was 30%. Ktrans change demonstrated inferiority of fostamatinib (N = 11) relative to adalimumab (N = 10) after 6 weeks (treatment ratio = 1.92, p = 0.003), and failed to distinguish fostamatinib from placebo (N = 10, p = 0.79). RAMRIS showed superiority of fostamatinib relative to placebo at 6 weeks (p = 0.023), and did not distinguish fostamatinib from adalimumab at either 6 (p = 0.175) or 24 (p = 0.230) weeks.ConclusionThis demonstrated repeatability of Ktrans and its ability to distinguish treatment groups show that DCE-MRI biomarkers are suitable for use in multicentre RA trials.Key Points• DCE-MRI biomarkers are feasible in large multicentre studies of joint inflammation.• DCE-MRI Ktransshowed fostamatinib inferior to adalimumab after 6 weeks.• Ktransrepeatability coefficient of variation was 30% multicentre.

Evaluation of Soft Tissue Sarcoma Response to Preoperative Chemoradiotherapy Using Dynamic Contrast-Enhanced Magnetic Resonance Imaging.

This study aims to assess the utility of quantitative dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) parameters in comparison with imaging tumor size for early prediction and evaluation of soft tissue sarcoma response to preoperative chemoradiotherapy. In total, 20 patients with intermediate- to high-grade soft tissue sarcomas received either a phase I trial regimen of sorafenib + chemoradiotherapy (n = 8) or chemoradiotherapy only (n = 12), and underwent DCE-MRI at baseline, after 2 weeks of treatment with sorafenib or after the first chemotherapy cycle, and after therapy completion. MRI tumor size in the longest diameter (LD) was measured according to the RECIST (Response Evaluation Criteria In Solid Tumors) guidelines. Pharmacokinetic analyses of DCE-MRI data were performed using the Shutter-Speed model. After only 2 weeks of treatment with sorafenib or after 1 chemotherapy cycle, Ktrans (rate constant for plasma/interstitium contrast agent transfer) and its percent change were good early predictors of optimal versus suboptimal pathological response with univariate logistic regression C statistics values of 0.90 and 0.80, respectively, whereas RECIST LD percent change was only a fair predictor (C = 0.72). Post-therapy Ktrans, ve (extravascular and extracellular volume fraction), and kep (intravasation rate constant), not RECIST LD, were excellent (C > 0.90) markers of therapy response. Several DCE-MRI parameters before, during, and after therapy showed significant (P < .05) correlations with percent necrosis of resected tumor specimens. In conclusion, absolute values and percent changes of quantitative DCE-MRI parameters provide better early prediction and evaluation of the pathological response of soft tissue sarcoma to preoperative chemoradiotherapy than the conventional measurement of imaging tumor size change.

Direct estimation of tracer-kinetic parameter maps from highly undersampled brain dynamic contrast enhanced MRI.

The purpose of this work was to develop and evaluate a T1 -weighted dynamic contrast enhanced (DCE) MRI methodology where tracer-kinetic (TK) parameter maps are directly estimated from undersampled (k,t)-space data. The proposed reconstruction involves solving a nonlinear least squares optimization problem that includes explicit use of a full forward model to convert parameter maps to (k,t)-space, utilizing the Patlak TK model. The proposed scheme is compared against an indirect method that creates intermediate images by parallel imaging and compressed sensing before to TK modeling. Thirteen fully sampled brain tumor DCE-MRI scans with 5-second temporal resolution are retrospectively undersampled at rates R = 20, 40, 60, 80, and 100 for each dynamic frame. TK maps are quantitatively compared based on root mean-squared-error (rMSE) and Bland-Altman analysis. The approach is also applied to four prospectively R = 30 undersampled whole-brain DCE-MRI data sets. In the retrospective study, the proposed method performed statistically better than indirect method at R ≥ 80 for all 13 cases. This approach provided restoration of TK parameter values with less errors in tumor regions of interest, an improvement compared to a state-of-the-art indirect method. Applied prospectively, the proposed method provided whole-brain, high-resolution TK maps with good image quality. Model-based direct estimation of TK maps from k,t-space DCE-MRI data is feasible and is compatible up to 100-fold undersampling. Magn Reson Med 78:1566-1578, 2017. © 2016 International Society for Magnetic Resonance in Medicine.

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.