Development of a dynamic quality assurance testing protocol for multisite clinical trial DCE-CT accreditation

Development of a dynamic myocardial perfusion phantom model for tracer kinetic measurements

BACKGROUND

Absolute myocardial perfusion imaging (MPI) is beneficial in the diagnosis and prognosis of patients with suspected or known coronary artery disease. However, validation and standardization of perfusion estimates across centers is needed to ensure safe and adequate integration into the clinical workflow. Physical myocardial perfusion models can contribute to this clinical need as these can provide ground-truth validation of perfusion estimates in a simplified, though controlled setup. This work presents the design and realization of such a myocardial perfusion phantom and highlights initial performance testing of the overall phantom setup using dynamic single photon emission computed tomography.

RESULTS

Due to anatomical and (patho-)physiological representation in the 3D printed myocardial perfusion phantom, we were able to acquire 22 dynamic MPI datasets in which ^99mTc-labelled tracer kinetics was measured and analyzed using clinical MPI software. After phantom setup optimization, time activity curve analysis was executed for measurements with normal myocardial perfusion settings (1.5 mL/g/min) and with settings containing a regional or global perfusion deficit (0.8 mL/g/min). In these measurements, a specific amount of activated carbon was used to adsorb radiotracer in the simulated myocardial tissue. Such mimicking of myocardial tracer uptake and retention over time satisfactorily matched patient tracer kinetics. For normal perfusion levels, the absolute mean error between computed myocardial blood flow and ground-truth flow settings ranged between 0.1 and 0.4 mL/g/min.

CONCLUSION

The presented myocardial perfusion phantom is a first step toward ground-truth validation of multimodal, absolute MPI applications in the clinical setting. Its dedicated and 3D printed design enables tracer kinetic measurement, including time activity curve and potentially compartmental myocardial blood flow analysis.

Phantom Validation of DCE-MRI Magnitude and Phase-Based Vascular Input Function Measurements

Accurate, patient-specific measurement of arterial input functions (AIF) may improve model-based analysis of vascular permeability. This study investigated factors affecting AIF measurements from magnetic resonance imaging (MRI) magnitude (AIF_MAGN) and phase (AIF_PHA) signals, and compared them against computed tomography (CT) (AIF_CT), under controlled conditions relevant to clinical protocols using a multimodality flow phantom. The flow phantom was applied at flip angles of 20° and 30°, flow rates (3-7.5 mL/s), and peak bolus concentrations (0.5-10 mM), for in-plane and through-plane flow. Spatial 3D-FLASH signal and variable flip angle T1 profiles were measured to investigate in-flow and radiofrequency-related biases, and magnitude- and phase-derived Gd-DTPA concentrations were compared. MRI AIF performance was tested against AIF_CT via Pearson correlation analysis. AIF_MAGN was sensitive to imaging orientation, spatial location, flip angle, and flow rate, and it grossly underestimated AIF_CT peak concentrations. Conversion to Gd-DTPA concentration using T1 taken at the same orientation and flow rate as the dynamic contrast-enhanced acquisition improved AIF_MAGN accuracy; yet, AIF_MAGN metrics remained variable and significantly reduced from AIF_CT at concentrations above 2.5 mM. AIF_PHA performed equivalently within 1 mM to AIF_CT across all tested conditions. AIF_PHA, but not AIF_MAGN, reported equivalent measurements to AIF_CT across the range of tested conditions. AIF_PHA showed superior robustness.

Accuracy and Performance of Functional Parameter Estimation Using a Novel Numerical Optimization Approach for GPU-Based Kinetic Compartmental Modeling

Quantitative kinetic parameters derived from dynamic contrast-enhanced (DCE) data are dependent on signal measurement quality and choice of pharmacokinetic model. However, the fundamental optimization analysis method is equally important and its impact on pharmacokinetic parameters has been mostly overlooked. We examine the effects of those choices on accuracy and performance of parameter estimation using both computer processing unit and graphical processing unit (GPU) numerical optimization implementations and evaluate the improvements offered by a novel optimization approach. A test framework was developed where experimentally derived population-average arterial input function and randomly sampled parameter sets {K_trans, K_ep, V_b, τ} were used to generate known tissue curves. Five numerical optimization algorithms were evaluated: sequential quadratic programming, downhill simplex (Nelder–Mead), pattern search, simulated annealing, and differential evolution. This was combined with various objective function implementation details: delay approximation, discretization and varying sampling rates. Then, impact of noise and CPU/GPU implementation was tested for speed and accuracy. Finally, the optimal method was compared to conventional implementation as applied to clinical DCE computed tomography. Nelder–Mead, differential evolution and sequential quadratic programming produced good results on clean and noisy input data outperforming simulated annealing and pattern search in terms of speed and accuracy in the respective order of 10⁻⁸%, 10⁻⁷%, and ×10⁻⁶%). A novel approach for DCE numerical optimization (infinite impulse response with fractional delay approximation) was implemented on GPU for speed increase of at least 2 orders of magnitude. Applied to clinical data, the magnitude of overall parameter error was <10%.

Unified platform for multimodal voxel-based analysis to evaluate tumour perfusion and diffusion characteristics before and after radiation treatment evaluated in metastatic brain cancer

OBJECTIVE:

Early changes in tumour behaviour following stereotactic radiosurgery) are potential biomarkers of response. To-date quantitative model-based measures of dynamic contrast-enhanced (DCE) and diffusion-weighted (DW) MRI parameters have shown widely variable findings, which may be attributable to variability in image acquisition, post-processing and analysis. Big data analytic approaches are needed for the automation of computationally intensive modelling calculations for every voxel, independent of observer interpretation.

METHODS:

This unified platform is a voxel-based, multimodality architecture that brings complimentary solute transport processes such as perfusion and diffusion into a common framework. The methodology was tested on synthetic data and digital reference objects and consequently evaluated in patients who underwent volumetric DCE-CT, DCE-MRI and DWI-MRI scans before and after treatment. Three-dimensional pharmacokinetic parameter maps from both modalities were compared as well as the correlation between apparent diffusion coefficient (ADC) values and the extravascular, extracellular volume (V_e). Comparison of histogram parameters was done via Bland-Altman analysis, as well as Student's t-test and Pearson's correlation using two-sided analysis.

RESULTS:

System testing on synthetic Tofts model data and digital reference objects recovered the ground truth parameters with mean relative percent error of 1.07 × 10^-7 and 5.60 × 10^-4 respectively. Direct voxel-to-voxel Pearson's analysis showed statistically significant correlations between CT and MR which peaked at Day 7 for K_trans (R = 0.74, p <= 0.0001). Statistically significant correlations were also present between ADC and Ve derived from both DCE-MRI and DCE-CT with highest median correlations found at Day 3 between median ADC and Ve,_MRI values (R = 0.6, p < 0.01) The strongest correlation to DCE-CT measurements was found with DCE-MRI analysis using voxelwise T₁₀ maps (R = 0.575, p < 0.001) instead of assigning a fixed T₁₀ value.

CONCLUSION:

The unified implementation of multiparametric transport modelling allowed for more robust and timely observer-independent data analytics. Utility of a common analysis platform has shown higher correlations between pharmacokinetic parameters obtained from different modalities than has previously been reported.

ADVANCES IN KNOWLEDGE:

Utility of a common analysis platform has shown statistically higher correlations between pharmacokinetic parameters obtained from different modalities than has previously been reported.

Use of patient outcome endpoints to identify the best functional CT imaging parameters in metastatic renal cell carcinoma patients

OBJECTIVE

To use the patient outcome endpoints overall survival and progression-free survival to evaluate functional parameters derived from dynamic contrast-enhanced CT.

METHODS

69 patients with metastatic renal cell carcinoma had dynamic contrast-enhanced CT scans at baseline and after 5 and 10 weeks of treatment. Blood volume, blood flow and standardized perfusion values were calculated using deconvolution (BV_deconv, BF_deconv and SPV_deconv), blood flow and standardized perfusion values using maximum slope (BF_max and SPV_max) and blood volume and permeability surface area product using the Patlak model (BV_patlak and PS). Histogram data for each were extracted and associated to patient outcomes. Correlations and agreements were also assessed.

RESULTS

The strongest associations were observed between patient outcome and medians and modes for BV_deconv, BV_patlak and BF_deconv at baseline and during the early ontreatment period (p < 0.05 for all). For the relative changes in median and mode between baseline and weeks 5 and 10, PS seemed to have opposite associations dependent on treatment. Interobserver correlations were excellent (r ≥ 0.9, p < 0.001) with good agreement for BF_deconv, BF_max, SPV_deconv and SPV_max and moderate to good (0.5 < r < 0.7, p < 0.001) for BV_deconv and BV_patlak. Medians had a better reproducibility than modes.

CONCLUSION

Patient outcome was used to identify the best functional imaging parameters in patients with metastatic renal cell carcinoma. Taking patient outcome and reproducibility into account, BV_deconv, BV_patlak and BF_deconv provide the most clinically meaningful information, whereas PS seems to be treatment dependent. Standardization of acquisition protocols and post-processing software is necessary for future clinical utilization. Advances in knowledge: Taking patient outcome and reproducibility into account, BV_deconv, BV_patlak and BF_deconv provide the most clinically meaningful information. PS seems to be treatment dependent.

Validation of a Multimodality Flow Phantom and Its Application for Assessment of Dynamic SPECT and PET Technologies

Simple and robust techniques are lacking to assess performance of flow quantification using dynamic imaging. We therefore developed a method to qualify flow quantification technologies using a physical compartment exchange phantom and image analysis tool. We validate and demonstrate utility of this method using dynamic PET and SPECT. Dynamic image sequences were acquired on two PET/CT and a cardiac dedicated SPECT (with and without attenuation and scatter corrections) systems. A two-compartment exchange model was fit to image derived time-activity curves to quantify flow rates. Flowmeter measured flow rates (20-300 mL/min) were set prior to imaging and were used as reference truth to which image derived flow rates were compared. Both PET cameras had excellent agreement with truth ( [Formula: see text]). High-end PET had no significant bias (p > 0.05) while lower-end PET had minimal slope bias (wash-in and wash-out slopes were 1.02 and 1.01) but no significant reduction in precision relative to high-end PET (<15% vs. <14% limits of agreement, p > 0.3). SPECT (without scatter and attenuation corrections) slope biases were noted (0.85 and 1.32) and attributed to camera saturation in early time frames. Analysis of wash-out rates from non-saturated, late time frames resulted in excellent agreement with truth ( [Formula: see text], slope = 0.97). Attenuation and scatter corrections did not significantly impact SPECT performance. The proposed phantom, software and quality assurance paradigm can be used to qualify imaging instrumentation and protocols for quantification of kinetic rate parameters using dynamic imaging.

Comparison of Voxel-Wise Tumor Perfusion Changes Measured With Dynamic Contrast-Enhanced (DCE) MRI and Volumetric DCE CT in Patients With Metastatic Brain Cancer Treated with Radiosurgery

Dynamic contrast-enhanced (DCE)-MRI metrics are evaluated against volumetric DCE-CT quantitative parameters as a standard for tracer-kinetic validation using a common 4-dimensional temporal dynamic analysis platform in tumor perfusion measurements following stereotactic radiosurgery (SRS) for brain metastases. Patients treated with SRS as part of Research Ethics Board-approved clinical trials underwent volumetric DCE-CT and DCE-MRI at baseline, then at 7 and 21 days after SRS. Temporal dynamic analysis was used to create 3-dimensional pharmacokinetic parameter maps for both modalities. Individual vascular input functions were selected for DCE-CT and a population function was used for DCE-MRI. Semiquantitative and pharmacokinetic DCE parameters were assessed using a modified Tofts model within each tumor at every time point for both modalities for characterization of perfusion and capillary permeability, as well as their dependency on precontrast relaxation times (TRs), T₁₀, and input function. Direct voxel-to-voxel Pearson analysis showed statistically significant correlations between CT and magnetic resonance which peaked at day 7 for K_trans (R = 0.74, P ≤ .0001). The strongest correlation to DCE-CT measurements was found with DCE-MRI analysis using voxel-wise T₁₀ maps (R = 0.575, P < .001) instead of assigning a fixed T₁₀ value. Comparison of histogram features showed statistically significant correlations between modalities over all tumors for median K_trans (R = 0.42, P = .01), median area under the enhancement curve (iAUC₉₀) (R = 0.55, P < .01), and median iAUC₉₀ skewness (R = 0.34, P = .03). Statistically significant, strong correlations were found for voxel-wise K_trans, iAUC₉₀, and v_e values between DCE-CT and DCE-MRI. For DCE-MRI, the implementation of voxel-wise T₁₀ maps plays a key role in ensuring the accuracy of heterogeneous pharmacokinetic maps.

Feasibility of 4D perfusion CT imaging for the assessment of liver treatment response following SBRT and sorafenib

Objectives

To evaluate the feasibility of 4-dimensional perfusion computed tomography (CT) as an imaging biomarker for patients with hepatocellular carcinoma and metastatic liver disease.

Methods and materials

Patients underwent volumetric dynamic contrast-enhanced CT on a 320-slice scanner before and during stereotactic body radiation therapy and sorafenib, and at 1 and 3 months after treatment. Quiet free breathing was used in the CT acquisition and multiple techniques (rigid or deformable registration as well as outlier removal) were applied to account for residual liver motion. Kinetic modeling was performed on a voxel-by-voxel basis in the gross tumor volume and normal liver resulting in 3-dimensional parameter maps of blood perfusion, capillary permeability, blood volume, and mean transit time. Perfusion characteristics in the tumor and adjacent liver were correlated with radiation dose distributions to evaluate dose-response. Paired t tests assessed change in spatial and histogram parameters from baseline to different time points during and after treatment. Technique reproducibility as well as the impact of arterial and portal vein input functions was also investigated using intra- and inter-subject variance and Bland-Altman analysis.

Results

Quantitative perfusion parameters were reproducible (±5.7%; range, 2%-10%) depending on tumor/normal liver type and kinetic parameter. Statistically significant reductions in tumor perfusion were measurable over the course of treatment and as early as 1 week after sorafenib administration (P < .05). Marked liver parenchyma perfusion reduction was seen with a strong dose-response effect (R² = 0.95) that increased significantly over the course treatment.

Conclusions

The proposed methodology demonstrated feasibility of evaluating spatiotemporal changes in liver tumor perfusion and normal liver function following antiangiogenic therapy and radiation treatment warranting further evaluation of biomarker prognostication.

Quantitative Imaging in Radiation Oncology: An Emerging Science and Clinical Service

Radiation oncology has long required quantitative imaging approaches for the safe and effective delivery of radiation therapy. The past 10 years has seen a remarkable expansion in the variety of novel imaging signals and analyses that are starting to contribute to the prescription and design of the radiation treatment plan. These include a rapid increase in the use of magnetic resonance imaging, development of contrast-enhanced imaging techniques, integration of fluorinated deoxyglucose-positron emission tomography, evaluation of hypoxia imaging techniques, and numerous others. These are reviewed with an effort to highlight challenges related to quantification and reproducibility. In addition, several of the emerging applications of these imaging approaches are also highlighted. Finally, the growing community of support for establishing quantitative imaging approaches as we move toward clinical evaluation is summarized and the need for a clinical service in support of the clinical science and delivery of care is proposed.

Functional MRI and CT biomarkers in oncology

Imaging biomarkers derived from MRI or CT describe functional properties of tumours and normal tissues. They are finding increasing numbers of applications in diagnosis, monitoring of response to treatment and assessment of progression or recurrence. Imaging biomarkers also provide scope for assessment of heterogeneity within and between lesions. A wide variety of functional parameters have been investigated for use as biomarkers in oncology. Some imaging techniques are used routinely in clinical applications while others are currently restricted to clinical trials or preclinical studies. Apparent diffusion coefficient, magnetization transfer ratio and native T1 relaxation time provide information about structure and organization of tissues. Vascular properties may be described using parameters derived from dynamic contrast-enhanced MRI, dynamic contrast-enhanced CT, transverse relaxation rate (R2*), vessel size index and relative blood volume, while magnetic resonance spectroscopy may be used to probe the metabolic profile of tumours. This review describes the mechanisms of contrast underpinning each technique and the technical requirements for robust and reproducible imaging. The current status of each biomarker is described in terms of its validation, qualification and clinical applications, followed by a discussion of the current limitations and future perspectives.

Multimodality functional imaging in radiation therapy planning: relationships between dynamic contrast-enhanced MRI, diffusion-weighted MRI, and 18F-FDG PET

OBJECTIVES

Biologically guided radiotherapy needs an understanding of how different functional imaging techniques interact and link together. We analyse three functional imaging techniques that can be useful tools for achieving this objective.

MATERIALS AND METHODS

The three different imaging modalities from one selected patient are ADC maps, DCE-MRI, and 18F-FDG PET/CT, because they are widely used and give a great amount of complementary information. We show the relationship between these three datasets and evaluate them as markers for tumour response or hypoxia marker. Thus, vascularization measured using DCE-MRI parameters can determine tumour hypoxia, and ADC maps can be used for evaluating tumour response.

RESULTS

ADC and DCE-MRI include information from 18F-FDG, as glucose metabolism is associated with hypoxia and tumour cell density, although 18F-FDG includes more information about the malignancy of the tumour. The main disadvantage of ADC maps is the distortion, and we used only low distorted regions, and extracellular volume calculated from DCE-MRI can be considered equivalent to ADC in well-vascularized areas.

CONCLUSION

A dataset for achieving the biologically guided radiotherapy must include a tumour density study and a hypoxia marker. This information can be achieved using only MRI data or only PET/CT studies or mixing both datasets.

Quality control within the multicentre perfusion CT study of primary colorectal cancer (PROSPeCT): results of an iodine density phantom study

OBJECTIVES

To assess the cross-centre consistency of iodine enhancement, contrast-to-noise ratio and radiation dose in a multicentre perfusion CT trial of colorectal cancer.

MATERIALS AND METHODS

A cylindrical water phantom containing different iodine inserts was examined on seven CT models in 13 hospitals. The relationship between CT number (Hounsfield units, HU) and iodine concentration (milligrams per millilitre) was established and contrast-to-noise ratios (CNRs) calculated. Radiation doses (CTDIvol, DLP) were compared across all sites.

RESULTS

There was a linear relationship between CT number and iodine density. Iodine enhancement varied by a factor of at most 1.10, and image noise by at most 1.5 across the study sites. At an iodine concentration of 1 mg ml(-1) and 100 kV, CNRs ranged from 3.6 to 4.8 in the 220-mm phantom and from 1.4 to 1.9 in the 300-mm phantom. Doses varied by a factor of at most 2.4, but remained within study dose constraints. Iterative reconstruction algorithms did not alter iodine enhancement but resulted in reduced image noise by a factor of at most 2.2, allowing a potential dose decrease of at most 80% compared to filtered back projection (FBP).

CONCLUSIONS

Quality control of CT performance across centres indicates that CNR values remain relatively consistent across all sites, giving acceptable image quality within the agreed dose constraints.

KEY POINTS

Quality control is essential in a multicentre setting to enable CT quantification. CNRs in a body-sized phantom had the recommended value of at least 1.5. CTDIs and DLPs varied by factors of 1.8 and 2.4 respectively.

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.