Estimation of tissue perfusion by dynamic contrast-enhanced imaging: simulation-based evaluation of the steepest slope method

Modeling normal mouse uterine contraction and placental perfusion with non-invasive longitudinal dynamic contrast enhancement MRI

BACKGROUND

The placenta is a transient organ critical for fetal development. Disruptions of normal placental functions can impact health throughout an individual's entire life. Although being recognized by the NIH Human Placenta Project as an important organ, the placenta remains understudied, partly because of a lack of non-invasive tools for longitudinally evaluation for key aspects of placental functionalities.

OBJECTIVE

Our goal is to create a non-invasive preclinical imaging pipeline that can longitudinally probe murine placental health in vivo. We use advanced imaging processing schemes to establish functional biomarkers for non-invasive longitudinal evaluation of placental development.

METHODOLOGY

We implement dynamic contrast enhancement magnetic resonance imaging (DCE-MRI) and analysis pipeline to quantify uterine contraction and placental perfusion dynamics. We use optic flow and time-frequency analysis to quantify and characterize contraction-related placental motion. Our novel imaging and analysis pipeline uses subcutaneous administration of gadolinium for steepest slope-based perfusion evaluation, enabling non-invasive longitudinal monitoring.

RESULTS

We demonstrate that the placenta exhibits spatially asymmetric contractile motion that develops from E14.5 to E17.5. Additionally, we see that placental perfusion, perfusion delivery rate, and substrate delivery all increase from E14.5 to E17.5, with the High Perfusion Chamber (HPC) leading the placental changes that occur from E14.5 to E17.5.

DISCUSSION

We advance the placental perfusion chamber paradigm with a novel, physiologically based threshold model for chamber localization and demonstrate spatially varying placental chambers using multiple functional metrics that assess mouse placental development and remodeling throughout gestation.

CONCLUSION

Our pipeline enables the non-invasive, longitudinal assessment of multiple placenta functions from a single imaging session. Our pipeline serves as a key toolbox for advancing research in mouse models of placental disease and disorder.

Modeling Normal Mouse Uterine Contraction and Placental Perfusion with Non-invasive Longitudinal Dynamic Contrast Enhancement MRI

The placenta is a transient organ critical for fetal development. Disruptions of normal placental functions can impact health throughout an individual's entire life. Although being recognized by the NIH Human Placenta Project as an important organ, the placenta remains understudied, partly because of a lack of non-invasive tools for longitudinally evaluation for key aspects of placental functionalities. Non-invasive imaging that can longitudinally probe murine placental health in vivo are critical to understanding placental development throughout pregnancy. We developed advanced imaging processing schemes to establish functional biomarkers for non-invasive longitudinal evaluation of placental development. We developed a dynamic contrast enhancement magnetic resonance imaging (DCE-MRI) pipeline combined with advanced image process methods to model uterine contraction and placental perfusion dynamics. Our novel imaging pipeline uses subcutaneous administration of gadolinium for steepest-slope based perfusion evaluation. This enables non-invasive longitudinal monitoring. Additionally, we advance the placental perfusion chamber paradigm with a novel physiologically-based threshold model for chamber localization and demonstrate spatially varying placental chambers using multiple functional metrics that assess mouse placental development and continuing remodeling throughout gestation. Lastly, using optic flow to quantify placental motions arisen from uterine contractions in conjunction with time-frequency analysis, we demonstrated that the placenta exhibited asymmetric contractile motion.

Electrical Impedance Tomography Identifies Evolution of Regional Perfusion in a Porcine Model of Acute Respiratory Distress Syndrome

BACKGROUND

Bedside electrical impedance tomography could be useful to visualize evolving pulmonary perfusion distributions when acute respiratory distress syndrome worsens or in response to ventilatory and positional therapies. In experimental acute respiratory distress syndrome, this study evaluated the agreement of electrical impedance tomography and dynamic contrast-enhanced computed tomography perfusion distributions at two injury time points and in response to increased positive end-expiratory pressure (PEEP) and prone position.

METHODS

Eleven mechanically ventilated (VT 8 ml · kg-1) Yorkshire pigs (five male, six female) received bronchial hydrochloric acid (3.5 ml · kg-1) to invoke lung injury. Electrical impedance tomography and computed tomography perfusion images were obtained at 2 h (early injury) and 24 h (late injury) after injury in supine position with PEEP 5 and 10 cm H2O. In eight animals, electrical impedance tomography and computed tomography perfusion imaging were also conducted in the prone position. Electrical impedance tomography perfusion (QEIT) and computed tomography perfusion (QCT) values (as percentages of image total) were compared in eight vertical regions across injury stages, levels of PEEP, and body positions using mixed-effects linear regression. The primary outcome was agreement between QEIT and QCT, defined using limits of agreement and Pearson correlation coefficient.

RESULTS

Pao2/Fio2 decreased over the course of the experiment (healthy to early injury, -253 [95% CI, -317 to -189]; early to late injury, -88 [95% CI, -151 to -24]). The limits of agreement between QEIT and QCT were -4.66% and 4.73% for the middle 50% quantile of average regional perfusion, and the correlation coefficient was 0.88 (95% CI, 0.86 to 0.90]; P < 0.001). Electrical impedance tomography and computed tomography showed similar perfusion redistributions over injury stages and in response to increased PEEP. QEIT redistributions after positional therapy underestimated QCT in ventral regions and overestimated QCT in dorsal regions.

CONCLUSIONS

Electrical impedance tomography closely approximated computed tomography perfusion measures in experimental acute respiratory distress syndrome, in the supine position, over injury progression and with increased PEEP. Further validation is needed to determine the accuracy of electrical impedance tomography in measuring perfusion redistributions after positional changes.

EDITOR’S PERSPECTIVE

DSA-Based 2D Perfusion Measurements in Delayed Cerebral Ischemia to Estimate the Clinical Outcome in Patients with Aneurysmal Subarachnoid Hemorrhage: A Technical Feasibility Study

(1) Background: To predict clinical outcomes in patients with aneurysmal subarachnoid hemorrhage (aSAH) and delayed cerebral ischemia (DCI) by assessment of the cerebral perfusion using a 2D perfusion angiography (2DPA) time-contrast agent (CA) concentration model. (2) Methods: Digital subtraction angiography (DSA) data sets of n = 26 subjects were acquired and post-processed focusing on changes in contrast density using a time-concentration model at three time points: (i) initial presentation with SAH (T0); (ii) vasospasm-associated acute clinical impairment (T1); and (iii) directly after endovascular treatment (T2) of SAH-associated large vessel vasospasm (LVV), which resulted in n = 78 data sets. Maximum slope (MS in SI/ms), time-to-peak (TTP in ms), and maximum amplitude of a CA bolus (dSI) were measured in brain parenchyma using regions of interest (ROIs). First, acquired parameters were standardized to the arterial input function (AIF) and then statistically analyzed as mean values. Additionally, data were clustered into two subsets consisting of patients with regredient or with stable/progredient symptoms (or Doppler signals) after endovascular treatment (n = 10 vs. n = 16). (3) Results: Perfusion parameters (MS, TTP, and dSI) differed significantly between T0 and T1 (p = 0.003 each). Significant changes between T1 and T2 were only detectable for MS (0.041 ± 0.016 vs. 0.059 ± 0.026; p = 0.011) in patients with regredient symptoms at T2 (0.04 ± 0.012 vs. 0.066 ± 0.031; p = 0.004). For dSI, there were significant differences between T0 and T2 (5095.8 ± 2541.9 vs. 3012.3 ± 968.3; p = 0.001), especially for those with stable symptoms at T2 (5685.4 ± 2967.2 vs. 3102.8 ± 1033.2; p = 0.02). Multiple linear regression analysis revealed that a) the difference in MS between T1 and T2 and b) patient's age (R = 0.6; R² = 0.34; p = 0.009) strongly predict the modified Rankin Scale (mRS) at discharge. (4) Conclusions: 2DPA allows the direct measurement of treatment effects in SAH associated DCI and may be used to predict outcomes in these critically ill patients.

Improving pulmonary perfusion assessment by dynamic contrast-enhanced computed tomography in an experimental lung injury model

Pulmonary perfusion has been poorly characterized in acute respiratory distress syndrome (ARDS). Optimizing protocols to measure pulmonary blood flow (PBF) via dynamic contrast-enhanced (DCE) computed tomography (CT) could improve understanding of how ARDS alters pulmonary perfusion. In this study, comparative evaluations of injection protocols and tracer-kinetic analysis models were performed based on DCE-CT data measured in ventilated pigs with and without lung injury. Ten Yorkshire pigs (five with lung injury, five healthy) were anesthetized, intubated, and mechanically ventilated; lung injury was induced by bronchial hydrochloric acid instillation. Each DCE-CT scan was obtained during a 30-s end-expiratory breath-hold. Reproducibility of PBF measurements was evaluated in three pigs. In eight pigs, undiluted and diluted Isovue-370 were separately injected to evaluate the effect of contrast viscosity on estimated PBF values. PBF was estimated with the peak-enhancement and the steepest-slope approach. Total-lung PBF was estimated in two healthy pigs to compare with cardiac output measured invasively by thermodilution in the pulmonary artery. Repeated measurements in the same animals yielded a good reproducibility of computed PBF maps. Injecting diluted isovue-370 resulted in smaller contrast-time curves in the pulmonary artery (P < 0.01) and vein (P < 0.01) without substantially diminishing peak signal intensity (P = 0.46 in the pulmonary artery) compared with the pure contrast agent since its viscosity is closer to that of blood. As compared with the peak-enhancement model, PBF values estimated by the steepest-slope model with diluted contrast were much closer to the cardiac output (R2 = 0.82) as compared with the peak-enhancement model. DCE-CT using the steepest-slope model and diluted contrast agent provided reliable quantitative estimates of PBF.NEW & NOTEWORTHY Dynamic contrast-enhanced CT using a lower-viscosity contrast agent in combination with tracer-kinetic analysis by the steepest-slope model improves pulmonary blood flow measurements and assessment of regional distributions of lung perfusion.

A proof-of-concept pipeline to guide evaluation of tumor tissue perfusion by dynamic contrast-agent imaging: Direct simulation and inverse tracer-kinetic procedures

Dynamic contrast-enhanced (DCE) perfusion imaging has shown great potential to non-invasively assess cancer development and its treatment by their characteristic tissue signatures. Different tracer kinetics models are being applied to estimate tissue and tumor perfusion parameters from DCE perfusion imaging. The goal of this work is to provide an in silico model-based pipeline to evaluate how these DCE imaging parameters may relate to the true tissue parameters. As histology data provides detailed microstructural but not functional parameters, this work can also help to better interpret such data. To this aim in silico vasculatures are constructed and the spread of contrast agent in the tissue is simulated. As a proof of principle we show the evaluation procedure of two tracer kinetic models from in silico contrast-agent perfusion data after a bolus injection. Representative microvascular arterial and venous trees are constructed in silico. Blood flow is computed in the different vessels. Contrast-agent input in the feeding artery, intra-vascular transport, intra-extravascular exchange and diffusion within the interstitial space are modeled. From this spatiotemporal model, intensity maps are computed leading to in silico dynamic perfusion images. Various tumor vascularizations (architecture and function) are studied and show spatiotemporal contrast imaging dynamics characteristic of in vivo tumor morphotypes. The Brix II also called 2CXM, and extended Tofts tracer-kinetics models common in DCE imaging are then applied to recover perfusion parameters that are compared with the ground truth parameters of the in silico spatiotemporal models. The results show that tumor features can be well identified for a certain permeability range. The simulation results in this work indicate that taking into account space explicitly to estimate perfusion parameters may lead to significant improvements in the perfusion interpretation of the current tracer-kinetics models.

Computation of contrast-enhanced perfusion using only two CT scan phases: a proof-of-concept study on abdominal organs

Background

Computed tomography perfusion imaging (CTPI) by repeated scanning has clinical relevance but implies relatively high radiation exposure. We present a method to measure perfusion from two CT scan phases only, considering tissue enhancement, feeding vessel (aortic) peak enhancement, and bolus shape.

Methods

CTPI scans (each with 40 frames acquired every 1.5 s) of 11 patients with advanced hepatocellular carcinoma (HCC) enrolled between 2012 and 2016 were retrospectively analysed (aged 69 ± 9 years, 8/11 males). Perfusion was defined as the maximal slope of the time-enhancement curve divided by the peak enhancement of the feeding vessel (aorta). Perfusion was computed two times, first using the maximum slope derived from all data points and then using the peak tissue enhancement and the bolus shape obtained from the aortic curve.

Results

Perfusion values from the two methods were linearly related (r2 = 0.92, p < 0.001; Bland–Altman analysis bias -0.12). The mathematical model showed that the perfusion ratio of two ROIs with the same feeding vessel (aorta) corresponds to their peak enhancement ratio (r2 = 0.55, p < 0.001; Bland–Altman analysis bias -0.68). The relationship between perfusion and tissue enhancement is predicted to be linear in the clinical range of interest, being only function of perfusion, peak feeding vessel enhancement, and bolus shape.

Conclusions

This proof-of-concept study showed that perfusion values of HCC, kidney, and pancreas could be computed using enhancement measured only with two CT scan phases, if aortic peak enhancement and bolus shape are known.

Imaging-based characterization of convective tissue properties

Convective transport is an important phenomenon for nanomedicine delivery. We present an imaging-based approach to recover tissue properties that are significant in the accumulation of nanoparticles delivered via systemic methods. The classical pharmacokinetic analysis develops governing equations for the particle transport from a first principle mass balance. Fundamentally, the governing equations for compartmental mass balance represent a spatially invariant mass transport between compartments and do not capture spatially variant convection phenomena. Further, the parameters recovered from this approach do not necessarily have direct meaning with respect to the governing equations for convective transport. In our approach, a framework is presented for directly measuring permeability in the sense of Darcy flow through porous tissue. Measurements from our approach are compared to an extended Tofts model as a control. We demonstrate that a pixel-wise iterative clustering algorithm may be applied to reduce the parameter space of the measurements. We show that measurements obtained from our approach are correlated with measurements obtained from the extended Tofts model control. These correlations demonstrate that the proposed approach contains similar information to an established compartmental model and may be useful in providing an alternative theoretical framework for parameterizing mathematical models for treatment planning and diagnostic studies involving nanomedicine where convection dominated effects are important.

Diffusion and perfusion MRI of normal, preeclamptic and growth-restricted mice models reveal clear fetoplacental differences

Diffusion-weighted MRI on rodents could be valuable to evaluate pregnancy-related dysfunctions, particularly in knockout models whose biological nature is well understood. Echo Planar Imaging’s sensitivity to motions and to air/water/fat heterogeneities, complicates these studies in the challenging environs of mice abdomens. Recently developed MRI methodologies based on SPatiotemporal ENcoding (SPEN) can overcome these obstacles, and deliver diffusivity maps at ≈150 µm in-plane resolutions. The present study exploits these capabilities to compare the development in wildtype vs vascularly-altered mice. Attention focused on the various placental layers—deciduae, labyrinth, trophoblast, fetal vessels—that the diffusivity maps could resolve. Notable differences were then observed between the placental developments of wildtype vs diseased mice; these differences remained throughout the pregnancies, and were echoed by perfusion studies relying on gadolinium-based dynamic contrast-enhanced MRI. Longitudinal monitoring of diffusivity in the animals throughout the pregnancies also showed differences between the development of the fetal brains in the wildtype and vascularly-altered mice, even if these disparities became progressively smaller as the pregnancies progressed. These results are analyzed on the basis of the known physiology of normal and preeclamptic pregnancies, as well as in terms of the potential that they might open for the early detection of disorders in human pregnancies.

Dual-input tracer kinetic modeling of dynamic contrast-enhanced MRI in thoracic malignancies

Pulmonary perfusion with dynamic contrast‐enhanced (DCE‐) MRI is typically assessed using a single‐input tracer kinetic model. Preliminary studies based on perfusion CT are indicating that dual‐input perfusion modeling of lung tumors may be clinically valuable as lung tumors have a dual blood supply from the pulmonary and aortic system. This study aimed to investigate the feasibility of fitting dual‐input tracer kinetic models to DCE‐MRI datasets of thoracic malignancies, including malignant pleural mesothelioma (MPM) and nonsmall cell lung cancer (NSCLC), by comparing them to single‐input (pulmonary or systemic arterial input) tracer kinetic models for the voxel‐level analysis within the tumor with respect to goodness‐of‐fit statistics. Fifteen patients (five MPM, ten NSCLC) underwent DCE‐MRI prior to radiotherapy. DCE‐MRI data were analyzed using five different single‐ or dual‐input tracer kinetic models: Tofts‐Kety (TK), extended TK (ETK), two compartment exchange (2CX), adiabatic approximation to the tissue homogeneity (AATH) and distributed parameter (DP) models. The pulmonary blood flow (BF), blood volume (BV), mean transit time (MTT), permeability‐surface area product (PS), fractional interstitial volume (v_I), and volume transfer constant (K^Trans) were calculated for both single‐ and dual‐input models. The pulmonary arterial flow fraction (γ), pulmonary arterial blood flow (BF_PA) and systemic arterial blood flow (BF_A) were additionally calculated for only dual‐input models. The competing models were ranked and their Akaike weights were calculated for each voxel according to corrected Akaike information criterion (cAIC). The optimal model was chosen based on the lowest cAIC value. In both types of tumors, all five dual‐input models yielded lower cAIC values than their corresponding single‐input models. The 2CX model was the best‐fitted model and most optimal in describing tracer kinetic behavior to assess microvascular properties in both MPM and NSCLC. The dual‐input 2CX‐model‐derived BF_A was the most significant parameter in differentiating adenocarcinoma from squamous cell carcinoma histology for NSCLC patients.

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

PURPOSE

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.

METHODS

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.

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.

CONCLUSION

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.

DCE MRI reveals early decreased and later increased placenta perfusion after a stress challenge during pregnancy in a mouse model

OBJECTIVES

Stress during pregnancy is known to have negative effects on fetal outcome. The purpose of this exploratory study was to examine placental perfusion alterations after stress challenge during pregnancy in a mouse model.

MATERIAL AND METHODS

Seven Tesla MRI was performed on pregnant mice at embrionic day (ED) 14.5 and 16.5. Twenty dams were exposed to an established acoustic stress challenge model while twenty non-exposed dams served as controls. Placental perfusion was analyzed in dynamic contrast-enhanced (DCE) MRI using the steepest slope model. The two functional placental compartments, the highly vascularized labyrinth and the endocrine junctional zone, were assessed separately.

RESULTS

Statistical analysis revealed decreased perfusion levels in the stress group at ED 14.5 compared to controls in both placenta compartments. On ED 16.5, the perfusion level increased significantly in the stress group while placenta perfusion in controls remained similar or even slightly decreased leading to an overall increased perfusion in the stress group on ED 16.5 compared to controls.

CONCLUSION

MR imaging allows noninvasive placenta perfusion assessment in this fetal stress mimicking animal model. In this exploratory study, we demonstrated that stress challenge during pregnancy leads to an initial reduction followed by an increase of placenta perfusion.

Renal Cell Carcinoma Perfusion before and after Radiofrequency Ablation Measured with Dynamic Contrast Enhanced MRI: A Pilot Study

Aim: To investigate if the early treatment effects of radiofrequency ablation (RFA) on renal cell carcinoma (RCC) can be detected with dynamic contrast enhanced (DCE)-MRI and to correlate RCC perfusion with RFA treatment time. Materials and methods: 20 patients undergoing RFA of their 21 RCCs were evaluated with DCE-MRI before and at one month after RFA treatment. Perfusion was estimated using the maximum slope technique at two independent sittings. Total RCC blood flow was correlated with total RFA treatment time, tumour location, size and histology. Results: DCE-MRI examinations were successfully evaluated for 21 RCCs (size from 1.3 to 4 cm). Perfusion of the RCCs decreased significantly (p < 0.0001) from a mean of 203 (±80) mL/min/100 mL before RFA to 8.1 (±3.1) mL/min/100 mL after RFA with low intra-observer variability (r ≥ 0.99, p < 0.0001). There was an excellent correlation (r = 0.95) between time to complete ablation and pre-treatment total RCC blood flow. Tumours with an exophytic location exhibit the lowest mean RFA treatment time. Conclusion: DCE-MRI can detect early treatment effects by measuring RCC perfusion before and after RFA. Perfusion significantly decreases in the zone of ablation, suggesting that it may be useful for the assessment of treatment efficacy. Pre-RFA RCC blood flow may be used to predict RFA treatment time.

Understanding K trans: a simulation study based on a multiple-pathway model

The transfer constant K ^trans is commonly employed in dynamic contrast-enhanced MRI studies, but the utility and interpretation of K ^trans as a potential biomarker of tumor vasculature remains unclear. In this study, computer simulations based on a comprehensive tracer kinetic model with multiple pathways was used to provide clarification on the interpretation and application of K ^trans. Tissue concentration-time curves pertaining to a wide range of transport conditions were simulated using the multiple-pathway (MP) model and fitted using the generalized kinetic (GK) and extended GK models. Relationships between K ^trans and plasma flow F _p, vessel permeability PS and extraction rate EF _p under various transport conditions were assessed by correlation and regression analysis. Results show that the MP model provides an alternative two-tier interpretation of K ^trans based on the vascular transit time. K ^trans is primarily associated with F _p and EF _p respectively, in the slow and rapid vascular transit states, independent of the magnitude of PS. The relative magnitudes of PS and F _p only serve as secondary constraints for which K ^trans can be further associated with EF _p and PS in the slow and rapid transit states, respectively.

Placental perfusion in uterine ischemia model as evaluated by dynamic contrast enhanced MRI

BACKGROUND

To validate DCE MRI method of placental perfusion estimation and to demonstrate application of the method in a rabbit model of fetal antenatal hypoxia-ischemia.

METHODS

Placental perfusion was estimated by dynamic contrast imaging with bolus injection of Gd-DTPA in 3 Tesla GE magnet in a rabbit model of placental ischemia-reperfusion in rabbit dams at embryonic day 25 gestation age. Placental perfusion was measured using steepest slope method on DCE MRI before and after intermittent 40 min uterine ischemia. Antioxidants (n = 2 dams, 9 placentas imaged) or vehicle (n = 5 dams, 23 placenta imaged) were given systemically in a separate group of dams during reperfusion-reoxygenation. Placental perfusion was also measured in two dams from the antioxidant group (10 placentas) and two dams from the control group (12 placentas) by fluorescent microspheres method.

RESULTS

While placental perfusion estimates between fluorescent microspheres and DCE MRI were significantly correlated (R(2)  = 0.85; P < 0.01), there was approximately 33% systematic underestimation by the latter technique. DCE MRI showed a significant decrease in maternal placental perfusion in reperfusion-reoxygenation phase in the saline, 0.44 ± 0.06 mL/min/g (P = 0.012, t-test), but not in the antioxidant group, 0.62 ± 0.06 mL/min/g, relative to pre-occlusion values (0.77 ± 0.07 and 0.84 ± 0.12 mL/min/g, correspondingly).

CONCLUSION

Underestimation of true perfusion in placenta by steepest slope DCE MRI is significant and the error appears to be systematic.

Patient characteristics influencing the variability of distributed parameter-based models in DCE-CT kinetic analysis

Kinetic parameter variability may be sensitive to kinetic model choice, kinetic model implementation or patient-specific effects. The purpose of this study was to assess their impact on the variability of dynamic contrast-enhanced computed tomography (DCE-CT) kinetic parameters. A total of 11 canine patients with sinonasal tumours received high signal-to-noise ratio, test-double retest DCE-CT scans. The variability for three distributed parameter (DP)-based models was assessed by analysis of variance. Mixed-effects modelling evaluated patient-specific effects. Inter-model variability (CV_inter ) was comparable to or lower than intra-model variability (CV_intra ) for blood flow (CV_inter :[4-28%], CV_intra :[28-31%]), fractional vascular volume (CV_inter :[3-17%], CV_intra :[16-19%]) and permeability-surface area product (CV_inter :[5-12%], CV_intra :[14-15%]). The kinetic models were significantly (P<0.05) impacted by patient characteristics for patient size, area underneath the curve of the artery and of the tumour. In conclusion, DP-based models demonstrated good agreement with similar differences between models and scans. However, high variability in the kinetic parameters and their sensitivity to patient size may limit certain quantitative applications.

Effects of guided random sampling of TCCs on blood flow values in CT perfusion studies of lung tumors

RATIONALE AND OBJECTIVES

Tissue perfusion is commonly used to evaluate lung tumor lesions through dynamic contrast-enhanced computed tomography (DCE-CT). The aim of this study was to improve the reliability of the blood flow (BF) maps by means of a guided sampling of the tissue time-concentration curves (TCCs).

MATERIALS AND METHODS

Fourteen selected CT perfusion (CTp) examinations from different patients with lung lesions were considered, according to different degrees of motion compensation. For each examination, two regions of interest (ROIs) referring to the target lesion and the arterial input were manually segmented. To obtain the perfusion parameters, we computed the maximum slope of the Hill equation, describing the pharmacokinetics of the contrast agent, and the TCC was fitted for each voxel. A guided iterative approach based on the Random Sample Consensus method was used to detect and exclude samples arising from motion artifacts through the assessment of the confidence level of each single temporal sample of the TCC compared to the model. Removing these samples permits to refine the model fitting, thus exploiting more reliable data. Goodness-of-fit measures of the fitted TCCs to the original data (eg, root mean square error and correlation distance) were used to assess the reliability of the BF values, so as to preserve the functional structure of the resulting perfusion map. We devised a quantitative index, the local coefficient of variation (lCV), to measure the spatial coherence of perfusion maps, from local to regional and global resolution. The effectiveness of the algorithm was tested under three different degrees of motion yielded by as many alignment procedures.

RESULTS

At pixel level, the proposed approach improved the reliability of BF values, quantitatively assessed through the correlation index. At ROI level, a comparative analysis emphasized how our approach "replaced" the noisy pixels, providing smoother parametric maps while preserving the main functional structure. Moreover, the implemented algorithm provides a more meaningful effect in correspondence of a higher motion degree. This was confirmed both quantitatively, using the lCV, and qualitatively, through visual inspection by expert radiologists.

CONCLUSIONS

Perfusion maps achieved with the proposed approach can now be used as a valid tool supporting radiologists in DCE-CTp studies. This represents a step forward to clinical utilization of these studies for staging, prognosis, and monitoring values of therapeutic regimens.

Radiation protection issues in dynamic contrast-enhanced (perfusion) computed tomography

Dynamic contrast-enhanced (DCE) CT studies are increasingly used in both medical care and clinical trials to improve diagnosis and therapy management of the most common life-threatening diseases: stroke, coronary artery disease and cancer. It is thus the aim of this review to briefly summarize the current knowledge on deterministic and stochastic radiation effects relevant for patient protection, to present the essential concepts for determining radiation doses and risks associated with DCE-CT studies as well as representative results, and to discuss relevant aspects to be considered in the process of justification and optimization of these studies. For three default DCE-CT protocols implemented at a latest-generation CT system for cerebral, myocardial and cancer perfusion imaging, absorbed doses were measured by thermoluminescent dosimeters at an anthropomorphic body phantom and compared with thresholds for harmful (deterministic) tissue reactions. To characterize stochastic radiation risks of patients from these studies, life-time attributable cancer risks (LAR) were estimated using sex-, age-, and organ-specific risk models based on the hypothesis of a linear non-threshold dose-response relationship. For the brain, heart and pelvic cancer studies considered, local absorbed doses in the imaging field were about 100-190 mGy (total CTDI(vol), 200 mGy), 15-30 mGy (16 mGy) and 80-270 mGy (140 mGy), respectively. According to a recent publication of the International Commission on Radiological Protection (ICRP Publication 118, 2012), harmful tissue reactions of the cerebro- and cardiovascular systems as well as of the lenses of the eye become increasingly important at radiation doses of more than 0.5 Gy. The LARs estimated for the investigated cerebral and myocardial DCE-CT scenarios are less than 0.07% for males and 0.1% for females at an age of exposure of 40 years. For the considered tumor location and protocol, the corresponding LARs are more than 6 times as high. Stochastic radiation risks decrease substantially with age and are markedly higher for females than for males. To balance the diagnostic needs and patient protection, DCE-CT studies have to be strictly justified and carefully optimized in due consideration of the various aspects discussed in some detail in this review.

ANTONIA perfusion and stroke. A software tool for the multi-purpose analysis of MR perfusion-weighted datasets and quantitative ischemic stroke assessment

OBJECTIVES

The objective of this work is to present the software tool ANTONIA, which has been developed to facilitate a quantitative analysis of perfusion-weighted MRI (PWI) datasets in general as well as the subsequent multi-parametric analysis of additional datasets for the specific purpose of acute ischemic stroke patient dataset evaluation.

METHODS

Three different methods for the analysis of DSC or DCE PWI datasets are currently implemented in ANTONIA, which can be case-specifically selected based on the study protocol. These methods comprise a curve fitting method as well as a deconvolution-based and deconvolution-free method integrating a previously defined arterial input function. The perfusion analysis is extended for the purpose of acute ischemic stroke analysis by additional methods that enable an automatic atlas-based selection of the arterial input function, an analysis of the perfusion-diffusion and DWI-FLAIR mismatch as well as segmentation-based volumetric analyses.

RESULTS

For reliability evaluation, the described software tool was used by two observers for quantitative analysis of 15 datasets from acute ischemic stroke patients to extract the acute lesion core volume, FLAIR ratio, perfusion-diffusion mismatch volume with manually as well as automatically selected arterial input functions, and follow-up lesion volume. The results of this evaluation revealed that the described software tool leads to highly reproducible results for all parameters if the automatic arterial input function selection method is used.

CONCLUSION

Due to the broad selection of processing methods that are available in the software tool, ANTONIA is especially helpful to support image-based perfusion and acute ischemic stroke research projects.

Computational fluid dynamics modelling of perfusion measurements in dynamic contrast-enhanced computed tomography: development, validation and clinical applications

Dynamic contrast-enhanced computed tomography (DCE-CT) is an imaging tool that aids in evaluating functional characteristics of tissue at different stages of disease management: diagnostic, radiation treatment planning, treatment effectiveness, and monitoring. Clinical validation of DCE-derived perfusion parameters remains an outstanding problem to address prior to perfusion imaging becoming a widespread standard as a non-invasive quantitative measurement tool. One approach to this validation process has been the development of quality assurance phantoms in order to facilitate controlled perfusion ex vivo. However, most of these systems fail to establish and accurately replicate physiologically relevant capillary permeability and exchange performance. The current work presents the first step in the development of a prospective suite of physics-based perfusion simulations based on coupled fluid flow and particle transport phenomena with the goal of enhancing the understanding of clinical contrast agent kinetics. Existing knowledge about a controllable, two-compartmental fluid exchange phantom was used to validate the computational fluid dynamics (CFD) simulation model presented herein. The sensitivity of CFD-derived contrast uptake curves to contrast injection parameters, including injection duration and flow rate, were quantified and found to be within 10% accuracy. The CFD model was employed to evaluate two commonly used clinical kinetic algorithms used to derive perfusion parameters: Fick's principle and the modified Tofts model. Neither kinetic model was able to capture the true transport phenomena it aimed to represent but if the overall contrast concentration after injection remained identical, then successive DCE-CT evaluations could be compared and could indeed reflect differences in regional tissue flow. This study sets the groundwork for future explorations in phantom development and pharmaco-kinetic modelling, as well as the development of novel contrast agents for DCE imaging.

Assessment of tumor blood flow distribution by dynamic contrast-enhanced CT

A distinct feature of the tumor vasculature is its tortuosity and irregular branching of vessels, which can translate to a wider dispersion and higher variability of blood flow in the tumor. To enable tumor blood flow variability to be assessed in vivo by imaging, a tracer kinetic model that accounts for flow dispersion is developed for use with dynamic contrast-enhanced (DCE) CT. The proposed model adopts a multiple-pathway approach and allows for the quantification of relative dispersion in the blood flow distribution, which reflects flow variability in the tumor vasculature. Monte Carlo simulation experiments were performed to study the possibility of reducing the number of model parameters based on the Akaike information criterion approach and to explore possible noise and tissue conditions in which the model might be applicable. The model was used for region-of-interest analysis and to generate perfusion parameter maps for three patient DCE CT cases with cerebral tumors, to illustrate clinical applicability.

Classic models for dynamic contrast-enhanced MRI

Dynamic contrast-enhanced MRI (DCE-MRI) is a functional MRI method where T1 -weighted MR images are acquired dynamically after bolus injection of a contrast agent. The data can be interpreted in terms of physiological tissue characteristics by applying the principles of tracer-kinetic modelling. In the brain, DCE-MRI enables measurement of cerebral blood flow (CBF), cerebral blood volume (CBV), blood-brain barrier (BBB) permeability-surface area product (PS) and the volume of the interstitium (ve ). These parameters can be combined to form others such as the volume-transfer constant K(trans) , the extraction fraction E and the contrast-agent mean transit times through the intra- and extravascular spaces. A first generation of tracer-kinetic models for DCE-MRI was developed in the early 1990s and has become a standard in many applications. Subsequent improvements in DCE-MRI data quality have driven the development of a second generation of more complex models. They are increasingly used, but it is not always clear how they relate to the models of the first generation or to the model-free deconvolution methods for tissues with intact BBB. This lack of understanding is leading to increasing confusion on when to use which model and how to interpret the parameters. The purpose of this review is to clarify the relation between models of the first and second generations and between model-based and model-free methods. All quantities are defined using a generic terminology to ensure the widest possible scope and to reveal the link between applications in the brain and in other organs.

Evaluation of the tibial tunnel after intraoperatively administered platelet-rich plasma gel during anterior cruciate ligament reconstruction using diffusion weighted and dynamic contrast-enhanced MRI

PURPOSE

To evaluate effect of platelet-rich plasma gel (PRPG), locally administered during the anterior cruciate ligament (ACL) reconstruction, with two MRI methods. The proximal tibial tunnel was assessed with diffusion weighted imaging (DWI) and with dynamic contrast-enhanced imaging (DCE-MRI).

MATERIALS AND METHODS

In 50 patients, standard arthroscopic ACL reconstructions were performed. The patients in the PRPG group (n = 25) received a local application of PRPG. The proximal tibial tunnel was examined by DWI and DCE-MRI, which were used to calculate apparent diffusion coefficient (ADC) values, as well as the contrast enhancement gradient (G(enh)) and enhancement factor (F(enh)) values.

RESULTS

At 1 month, the calculated average ADC value in the PRPG group was significantly lower than in the control group. At 2.5 and at 6 months, G(enh) was significantly higher in the PRPG group. There were no significant differences in F(enh) between the groups at any control examination.

CONCLUSION

DWI and DCE-MRI measurements indicate a reduced extent of edema during the first postoperative month as well as an increased vascular density and microvessel permeability in the proximal tibial tunnel at 1 and 2.5 postoperative months as the effect of the application of PRPG.

[Dynamic contrast-enhanced computed tomography. Tracer kinetics and radiation hygienic principles]

Technical innovations in multislice computed tomography (CT) allow for larger volume coverage in ever shorter scan times. This progress has stimulated the clinical application of dynamic contrast-enhanced (DCE) CT techniques, which offer the possibility to noninvasively characterize tissue microcirculation in terms of well-defined physiological quantities. This educational review imparts to radiologists the essential physiological terms and definitions as well as the basic tracer kinetic concepts required for the analysis of DCE-CT data. In particular, four different approaches are presented and exemplified by the analysis of representative DCE data: the steepest-gradient method, model-free algebraic deconvolution in combination with the indicator-dilution theory, two-compartment modelling and the so-called adiabatic approximation to the homogeneity model. Even though DCE-CT offers substantial methodological and practical advantages as compared to DCE-MRI (magnetic resonance imaging), there are also two serious and interconnected shortcomings: the low contrast enhancement in relation to the noise level and the high exposure of patients to ionizing radiation. These limiting aspects are considered in detail from a radiation hygienic point of view, emphasizing the basic principles of justification and optimization. Clinically established as well as potential future applications of DCE-CT will be presented in a subsequent paper.

Quantitative evaluation of the tibial tunnel after anterior cruciate ligament reconstruction using diffusion weighted and dynamic contrast enhanced MRI: a follow-up feasibility study

OBJECTIVE

The aim of the study was to evaluate the feasibility of two quantitative MRI methods: diffusion weighted imaging (DWI) and dynamic contrast enhanced imaging (DCEI), for follow-up assessment of the tibial tunnel after reconstruction of the anterior cruciate ligament (ACL).

MATERIALS AND METHODS

Twenty-three patients were examined by MRI at 1 and 6 months following ACL reconstruction. DWI and DCEI were utilized for evaluating the region of interest (ROI) within the proximal part of the tibial tunnel. From the resulting apparent diffusion coefficient (ADC) maps, ADC values were calculated. DCEI data were used to extract the enhancement factor (f(enh)) and the enhancement gradient (g(enh)) for the same ROI.

RESULTS

Calculated ADC as well as the f(enh) and g(enh) had diminished to a statistically significant extent by 6 months after ACL reconstruction. The average ADC value diminished from 1.48 (10(-3) mm(2)/s) at 1 month to 1.30 (10(-3) mm(2)/s) at 6 months after reconstruction. The average f(enh) value decreased from 1.21 at 1 month to 0.50 at 6 months and the average g(enh) value decreased from 2.01%/s to 1.15%/s at 6 months, respectively.

CONCLUSION

The study proved feasibility of DWI and DCEI for quantitative assessment of the tibial tunnel at 1 and 6 months after ACL reconstruction. Both methods have the potential for use as an additional tool in the evaluation of new methods of ACL reconstruction. To our knowledge, this is the first time quantitative MRI has been used in the follow-up to the ACL graft healing process.

Tracer kinetic modelling of tumour angiogenesis based on dynamic contrast-enhanced CT and MRI measurements

PURPOSE

Technical developments in both magnetic resonance imaging (MRI) and computed tomography (CT) have helped to reduce scan times and expedited the development of dynamic contrast-enhanced (DCE) imaging techniques. Since the temporal change of the image signal following the administration of a diffusible, extracellular contrast agent (CA) is related to the local blood supply and the extravasation of the CA into the interstitial space, DCE imaging can be used to assess tissue microvasculature and microcirculation. It is the aim of this review to summarize the biophysical and tracer kinetic principles underlying this emerging imaging technique offering great potential for non-invasive characterization of tumour angiogenesis.

METHODS

In the first part, the relevant contrast mechanisms are presented that form the basis to relate signal variations measured by serial CT and MRI to local tissue concentrations of the administered CA. In the second part, the concepts most widely used for tracer kinetic modelling of concentration-time courses derived from measured DCE image data sets are described in a consistent and unified manner to highlight their particular structure and assumptions as well as the relationships among them. Finally, the concepts presented are exemplified by the analysis of representative DCE data as well as discussed with respect to present and future applications in cancer diagnosis and therapy.

RESULTS

Depending on the specific protocol used for the acquisition of DCE image data and the particular model applied for tracer kinetic analysis of the derived concentration-time courses, different aspects of tumour angiogenesis can be quantified in terms of well-defined physiological tissue parameters.

CONCLUSIONS

DCE imaging offers promising prospects for improved tumour diagnosis, individualization of cancer treatment as well as the evaluation of novel therapeutic concepts in preclinical and early-stage clinical trials.