Johannes G. Korporaal

Validation of functional imaging with pathology for tumor delineation in the prostate

INTRODUCTION A study was performed to validate magnetic resonance (MR) based prostate tumor delineations with pathology. MATERIAL AND METHODS Five patients with biopsy proven prostate cancer underwent a T2 weighted (T2w), diffusion weighted MRI (DW-MRI) and dynamic contrast-enhanced MRI (DCE-MRI) scan before prostatectomy. Suspicious regions were delineated based on all available MR information. After prostatectomy whole-mount hematoxylin-eosin stained (H&E) sections were made. Tumor tissue was delineated on the H&E stained sections and compared with the MR based delineations. The registration accuracy between the MR images and H&E stained sections was estimated. RESULTS A tumor coverage of 44-89% was reached by the MR based tumor delineations. The application of a margin of approximately 5mm to the MR based tumor delineations yielded a tumor coverage of 85-100% in all patients. Errors created during the registration procedure were 2-3mm, which cannot completely explain the limited tumor coverage. CONCLUSIONS An accurate tissue processing and registration method was presented (registration error 2-3mm), which enables the validation of MR based tumor delineations with pathology. Reasonable tumor coverage of about 85% and larger was found when applying a margin of approximately 5 mm to the MR based tumor delineations.

Optimizing contrast media injection protocols in state-of-the art computed tomographic angiography.

ObjectiveVery short acquisition times and the increasing use of low-kilovolt protocols in standard computed tomographic (CT) angiography (CTA) examinations demand modifications in the contrast media (CM) injection regimen. The aim of this study was to optimize the use of tube voltage–adjusted CM delivery parameters, especially injection duration and iodine delivery rate (IDR), in thoracoabdominal CTA in a porcine model. Materials and MethodsEight pigs (53–72 kg) were examined with a third-generation dual-source CT system with a dynamic CTA protocol (4-dimensional spiral, 454-mm scan length, 2.5-second temporal resolution, 70-second total acquisition time). Six CM injection protocols were applied in randomized order and intraindividually compared. The standard CTA protocol was performed at 120 kV, with an injection of 300 mg iodine/kg body weight and a flow of 5 mL/s (IDR, 1.5 g/s). On the basis of phantom measurements for the low-kilovolt CTA protocols, the iodine dosage was adjusted to 150 mg iodine/kg (70 kV) and 210 mg iodine/kg (90 kV). Therefore, either the IDR was kept constant and the injection time was reduced, or the injection time was kept constant and the IDR was reduced by modifying the CM flow or concentration. Time attenuation curves, time to peak, and peak enhancement were calculated for different locations within the aorta, renal arteries, and large veins. ResultsThe heart rates were comparable among the different injection protocols (66.9–78.1 beats per minute). The average injection peak pressure depended on the flow rate and CM concentration and ranged from 42.9 to 114.7 psi. The average arterial peak enhancement was comparable for protocols with identical injection times and reduced IDR (362.4 HU [standard 120-kV protocol; 300 mg iodine/kg; IDR, 1.5 g/s], 360.0 HU [70 kV; 150 mg iodine/kg; IDR, 0.75 g/s], 365.4 HU [70 kV; 150 mg iodine/kg; IDR, 0.75 g/s; CM, 150 mg iodine/mL], 344.3 HU [90 kV; 210 mg iodine/kg; IDR, 1.1 g/s]). Higher peak enhancements could be achieved by applying protocols with identical IDR and a reduced injection time (502.5 HU [70 kV; 150 mg iodine/kg; IDR, 1.5 g/s] and 394.6 HU [90 kV; 210 mg iodine/kg; IDR, 1.5 g/s]). ConclusionsBy adjusting the IDR, low-kilovolt CTA is able to achieve comparable aortic enhancement with a significant reduction in CM dosage. A shorter injection time at constant IDR results in higher enhancement and a narrower scan window and might be preferable for fast CTA acquisition techniques. CLINICAL RELEVANCE/APPLICATIONThe optimization of CM injection protocols is mandatory to achieve state-of the art CTA at low kilovolt and can reduce CM doses to patients.

Phase-based arterial input function measurements in the femoral arteries for quantification of dynamic contrast-enhanced (DCE) MRI and comparison with DCE-CT

Dynamic contrast‐enhanced (DCE) MRI is useful for diagnosis, treatment monitoring and follow‐up of prostate cancer. However, large differences have been reported in the parameter range of the transfer constant Ktrans, making longitudinal studies and comparison of DCE‐MRI findings between studies difficult. Large part of this inconsistency in Ktrans values can be attributed to problems with the accurate measurement of the arterial input function (AIF) from the magnitude signal (AIFMAGN). Phase‐based AIF measurements (AIFPHASE) have been proposed as a more robust alternative to AIFMAGN measurements. This study compares AIFPHASE with AIFs measured with DCE‐CT (AIFCT), and the corresponding Ktrans maps in 12 prostate cancer patients. The shape of AIFPHASE and AIFCT are similar, although differences in the peak height and peak width exist as a result of differences in injection protocol. No significant differences in Ktrans values were found between the DCE‐MRI and DCE‐CT exams, with median Ktrans values of 0.10 and 0.08 min−1 for healthy peripheral zone tissue and 0.44 and 0.36 min−1 for regions suspected of tumor respectively. Therefore, robust quantification of Ktrans values from DCE‐MRI exams in the cancerous prostate is feasible with the use of AIFPHASE. Magn Reson Med, 2011.

Dynamic Contrast-enhanced CT for Prostate Cancer: Relationship between Image Noise, Voxel Size, and Repeatability

PURPOSE To evaluate the relationship between image noise, voxel size, and voxel-wise repeatability of a dynamic contrast agent-enhanced (DCE) computed tomographic (CT) examination for prostate cancer. MATERIALS AND METHODS This prospective study was approved by the local research ethics committee, and all patients gave written informed consent. Twenty-nine patients (mean age, 69.1 years; range, 56-80 years) with biopsy-proved prostate cancer underwent two DCE CT examinations within 1 week prior to radiation therapy. Parameter maps of transfer constant (K(trans)), the fraction of blood plasma (v(p)), the fraction of extravascular extracellular space (v(e)), and the flux rate constant between the extravascular extracellular space and plasma (k(ep)) were calculated at 15 different image resolutions, with kernel sizes ranging from 0.002 to 2.57 cm(3). Statistical analysis to quantify the voxel-wise repeatability was performed by using a Bland-Altman analysis on all tracer kinetic model parameter maps of each patient. From this analysis, the within-voxel standard deviation (wSD) was calculated as a function of spatial resolution. RESULTS A kernel size in the range of 0.1-0.3 cm(3) yields reliable information. At 0.15 cm(3), the median wSDs of K(trans), k(ep), v(p), and v(e) are 0.047 min(-1), 0.144 min(-1), 0.011, and 0.104, respectively. With increasing kernel size, these values reach stable levels of approximately 0.02 min(-1), 0.05 min(-1), 0.005, and 0.05, respectively. CONCLUSION There is a high voxel-wise repeatability of the DCE CT imaging technique for prostate cancer for kernel sizes as small as 0.1 cm(3). With the relationship between kernel size, image noise and voxel-wise repeatability, it becomes possible to estimate for alternative DCE CT protocols (eg, those with a reduced radiation dose) at what kernel size a sufficient repeatability can be obtained.

Tracer kinetic model selection for dynamic contrast-enhanced computed tomography imaging of prostate cancer.

Objectives:To investigate the conditions under which the Tofts, extended Tofts, and adiabatic approximation to the tissue homogeneity (AATH) model are the optimal tracer kinetic models (TKMs) for the quantification of dynamic contrast-enhanced (DCE) computed tomography (CT) examinations in prostate cancer. Materials and Methods:This prospective study was approved by the local research ethics committee, and all patients gave written informed consent. A total of 29 patients (mean age, 69.1 years; range, 56–80 years) with biopsy-proven prostate cancer underwent a DCE-CT examination prior to radiation therapy. TKM parameter maps were calculated for each patient with the Tofts, extended Tofts, and AATH models. For each voxel, corrected Akaike information criterion values were calculated, taking into account both the goodness-of-fit and the number of model parameters. We consider the optimal model as the model with the lowest corrected Akaike information criterion. Results:All 3 TKMs are the optimal models in part of the prostate. For individual patients, the AATH model was the optimal model in 25.0% to 88.9%, the Tofts in 2.7% to 71.8%, and the extended Tofts model in 0.7% to 68.7% of the prostate voxels. The Tofts model was optimal in low flow regions (<0.1 min−1), the extended Tofts model in regions with high flow (>0.4 min−1) and low transit time (<12 seconds), and the AATH model in the intermediate flow range (0.1–0.4 min−1). However, differences between the 3 models were small and TKM parameter estimates gave consistent results between the 3 models. Conclusions:All the 3 models gave reasonable fits of DCE-CT data from the prostate. In view of the small parameter range in which the Tofts and extended Tofts models outperform the AATH model, the latter seems the optimal model for quantification of DCE-CT data of the prostate.

The use of probability maps to deal with the uncertainties in prostate cancer delineation

BACKGROUND AND PURPOSE The use of dynamic contrast-enhanced (DCE) imaging for delineation of prostate tumors requires that decisions are made on a voxel wise basis about the presence of tumor. While the sensitivity and specificity of this technique is high, we propose a probabilistic approach to deal with the intrinsic imaging uncertainty. MATERIAL AND METHODS Twenty-nine patients with biopsy-proven prostate cancer underwent a DCE-CT exam prior to radiotherapy. From a logistic regression on K(trans) values from healthy and diseased appearing prostate regions we obtained a probability function for the presence of tumor. K(trans) parameter maps were converted into probability maps and a stratification was applied at the 5% and 95% probability level, to identify low-, intermediate-, and high-risk areas for the presence of tumor. RESULTS In all patients, regions with high-, intermediate-, and low-risk were identified, with median volume percentages of 7.6%, 40.0%, and 52.1%, respectively. The contiguous areas that resulted from the voxel wise stratification can be interpreted as GTV, high-risk CTV, and CTV. CONCLUSIONS K(trans) parameter maps from a DCE-CT exam can be converted into probability maps for the presence of tumor. In this way, the intrinsic uncertainty that a voxel contains tumor can be incorporated into the treatment planning process.

The effect of hormonal treatment on conspicuity of prostate cancer: Implications for focal boosting radiotherapy

BACKGROUND AND PURPOSE For focal boosting of prostate tumors, three questions are important regarding the use of hormonal therapy. Does prolonged hormonal treatment affect the conspicuity of tumor tissue on diffusion weighted imaging (DWI) and dynamic contrast-enhanced (DCE-MRI) images? Is tumor delineation possible in patients using hormonal treatment? Can we identify specific imaging thresholds for tumor delineation in patients after prolonged androgen deprivation? MATERIALS AND METHODS Ninety-six patients were included. Using multivariate linear regression analyses, we investigated if DWI and DCE-MRI parameter maps are different in patients receiving hormonal treatment for 0-3 or >3 months. Furthermore, logistic regression was performed to obtain specific imaging thresholds for tumor tissue for the two patient groups. RESULTS We found a significantly higher diffusion and lower perfusion of tumor tissue in the >3 months hormonal treatment group compared to the 0-3 group. This resulted in lower tumor conspicuity. Nevertheless, in 18/21 of the patients in the >3 months treatment group, a suspicious lesion could be defined based on the MR images. Based on logistic regression, different imaging thresholds should be set for tumor detection in the two treatment groups. CONCLUSIONS Prolonged androgen deprivation decreases tumor conspicuity. Different imaging thresholds need to be set to delineate tumor in patients who have had prolonged hormonal treatment.

Quantitative evaluation of the performance of a new test bolus-based computed tomographic angiography contrast-enhancement-prediction algorithm.

ObjectivesThe objective of this study was to assess the robustness of a novel test bolus (TB)–based computed tomographic angiography (CTA) contrast-enhancement–prediction (CEP) algorithm by retrospectively quantifying the systematic and random errors between the predicted and true enhancements. Materials and MethodsAll local institutional review boards approved this retrospective study, in which a total of 72 (3 × 24) anonymized cardiac CTA examinations were collected from 3 hospitals. All patients (46 men; median age, 62 years [range, 31–81 years]) underwent a TB scan and a cardiac CTA according to local scan and injection protocols. For each patient, a shorter TB signal and TB signals with lower temporal resolution were derived from the original TB signal. The CEP algorithm predicted the enhancement in the descending aorta (DAo) on the basis of the TB signals in the DAo, the injection protocols and kilovolt settings, as well as population-averaged blood circulation characteristics. The true enhancement was extracted with a region of interest along the DAo centerline. For each patient, the errors in timing and amplitude were calculated; differences between the hospitals were assessed using the 1-way analysis of variance (P < 0.05) and variations between the TB signals were assessed using the within-subject standard deviation. ResultsNo significant differences were found between the 3 hospitals for any of the TB signals. With errors in the amplitude and timing of 0.3% ± 15.6% and −0.2 ± 2.0 seconds, respectively, no clinically relevant systematic errors existed. Shorter- and coarser-time–sampled TB signals introduced a within-subject standard deviation of 4.0% and 0.5 seconds, respectively. ConclusionsThis TB-based CEP algorithm has no systematic errors in the timing and amplitude of predicted enhancements and is robust against coarser-time–sampled and incomplete TB scans.

Evaluation of A New Bolus Tracking-Based Algorithm for Predicting A Patient-Specific Time of Arterial Peak Enhancement in Computed Tomography Angiography.

ObjectivesThe aim of this study was to evaluate the systematic and random errors of a new bolus tracking–based algorithm that predicts a patient-specific time of peak arterial enhancement and compare its performance with a best-case scenario for the current bolus tracking technique. Materials and MethodsAll local institutional review boards approved this retrospective study, in which the test bolus signals of cardiac computed tomography angiographies of 72 patients (46 men; median age, 62 years [range, 31–81 years]) were used to simulate contrast enhancement curves for a multitude of injection protocols with iodine delivery rates (IDRs) varying between 0.5 and 2.5 gI/s, injection durations between 4 and 30 seconds, and tube voltages of 100 and 120 kV. From these simulated curves, bolus tracking signals with statistical errors of 4 Hounsfield units (HU) (standard deviation) were derived with trigger values of 100 and 150 HU at 100 and 120 kV, respectively. The new algorithm then matched the actual bolus tracking signal with a database of expected enhancement curves for that particular injection protocol, taking into account population-averaged blood circulation characteristics with variations in patient weight and cardiac output. Posttrigger delays (PTDs) were calculated as the time difference between the last bolus tracking point and the time of peak enhancement. The systematic and random errors between the predicted and true PTDs were assessed and compared with a best-case scenario for the current bolus tracking method. ResultsWith the current bolus tracking technique, interpatient variations decrease with higher IDRs and earlier triggering (lower tube voltage and/or lower trigger value), and the true PTDs increase linearly with injection duration. Compared with the current bolus tracking method, the systematic and random errors of the algorithm-predicted PTDs are smaller, do not depend on the IDR, and are predictable over a large range of total iodine doses. The median difference between the true and algorithm-predicted PTD is less than ±1 second for all IDRs and injection durations, and the algorithm was able to predict patient-specific PTDs within ±2 seconds from the true PTD in more than 90% of patients for almost all injection protocols. ConclusionsThe new algorithm can robustly predict a patient-specific time of arterial peak enhancement and is better than a best-case scenario for the current bolus tracking technique because interpatient variations are taken into account. It offers a new framework for scan timing optimization and can potentially be used for personalized scan timing in real time.

Contrast Gradient-Based Blood Velocimetry With Computed Tomography: Theory, Simulations, and Proof of Principle in a Dynamic Flow Phantom.

ObjectivesThe aim of this study was to introduce a new theoretical framework describing the relationship between the blood velocity, computed tomography (CT) acquisition velocity, and iodine contrast enhancement in CT images, and give a proof of principle of contrast gradient-based blood velocimetry with CT. Materials and MethodsThe time-averaged blood velocity (vblood) inside an artery along the axis of rotation (z axis) is described as the mathematical division of a temporal (Hounsfield unit/second) and spatial (Hounsfield unit/centimeter) iodine contrast gradient. From this new theoretical framework, multiple strategies for calculating the time-averaged blood velocity from existing clinical CT scan protocols are derived, and contrast gradient-based blood velocimetry was introduced as a new method that can calculate vblood directly from contrast agent gradients and the changes therein. Exemplarily, the behavior of this new method was simulated for image acquisition with an adaptive 4-dimensional spiral mode consisting of repeated spiral acquisitions with alternating scan direction. In a dynamic flow phantom with flow velocities between 5.1 and 21.2 cm/s, the same acquisition mode was used to validate the simulations and give a proof of principle of contrast gradient-based blood velocimetry in a straight cylinder of 2.5 cm diameter, representing the aorta. ResultsIn general, scanning with the direction of blood flow results in decreased and scanning against the flow in increased temporal contrast agent gradients. Velocity quantification becomes better for low blood and high acquisition speeds because the deviation of the measured contrast agent gradient from the temporal gradient will increase. In the dynamic flow phantom, a modulation of the enhancement curve, and thus alternation of the contrast agent gradients, can be observed for the adaptive 4-dimensional spiral mode and is in agreement with the simulations. The measured flow velocities in the downslopes of the enhancement curves were in good agreement with the expected values, although the accuracy and precision worsened with increasing flow velocities. ConclusionsThe new theoretical framework increases the understanding of the relationship between the blood velocity, CT acquisition velocity, and iodine contrast enhancement in CT images, and it interconnects existing blood velocimetry methods with research on transluminary attenuation gradients. With these new insights, novel strategies for CT blood velocimetry, such as the contrast gradient-based method presented in this article, may be developed.