Perfusion Maps Of Blood Flow Research Articles

Virtual monoenergetic reconstructions of dynamic DECT acquisitions for calculation of perfusion maps of blood flow: Quantitative comparison to conventional, dynamic 80 kVp CT perfusion

PurposeInvestigation of potential improvements in dynamic CT perfusion measurements by exploitation of improved visualization of contrast agent in virtual monoenergetic reconstructions of images acquired with dual-energy computed tomography (DECT). MethodFor 17 patients with pancreatic carcinoma, dynamic dual-source DECT acquisitions were performed at 80kVp/Sn140kVp every 1.5 s over 51 s. Virtual monoenergetic images (VMI) were reconstructed for photon energies between 40 keV and 150 keV (5 keV steps). Using the maximum-slope model, perfusion maps of blood flow were calculated from VMIs and 80kVp images and compared quantitatively with regard to blood flow measured in regions of interest in healthy tissue and carcinoma, standard deviation (SD), and absolute-difference-to-standard-deviation ratio (ADSDR) of measurements. ResultsOn average, blood flow calculated from VMIs increased with increasing energy levels from 114.3 ± 37.2 mL/100 mL/min (healthy tissue) and 45.6 ± 25.3 mL/100 mL/min (carcinoma) for 40 keV to 128.6 ± 58.9 mL/100 mL/min (healthy tissue) and 75.5 ± 49.8 mL/100 mL/min (carcinoma) for 150 keV, compared to 114.2 ± 37.4 mL/100 mL/min (healthy tissue) and 46.5 ± 26.6 mL/100 mL/min (carcinoma) for polyenergetic 80kVp. Differences in blood flow between tissue types were significant for all energies. Differences between perfusion maps calculated from VMIs and 80kVp images were not significant below 110 keV. SD and ADSDR were significantly better for perfusion maps calculated from VMIs at energies between 40 keV and 55 keV than for those calculated from 80kVp images. Compared to effective dose of dynamic 80kVp acquisitions (4.6 ± 2.2 mSv), dose of dynamic DECT/VMI acquisitions (8.0 ± 3.7 mSv) was higher. ConclusionsPerfusion maps of blood flow based on low-energy VMIs between 40 keV and 55 keV offer improved robustness and quality of quantitative measurements over those calculated from 80kVp image data (reference standard), albeit at increased patient radiation exposure.

Measuring Dynamic CT Perfusion Based on Time-Resolved Quantitative DECT Iodine Maps

Using dual-energy computed tomography (DECT) for quantifying iodine content after injection of contrast agent could provide a quantitative basis for dynamic computed tomography (CT) perfusion measurements by means of established mathematical models of contrast agent kinetics, thus improving results by combining the strength of both techniques, which was investigated in this study. A dynamic DECT acquisition over 51 seconds performed at 80/Sn140 kVp in 17 patients with pancreatic carcinoma was used to calculate iodine-enhancement images for each time point by means of 3-material decomposition. After motion correction, perfusion maps of blood flow were calculated using the maximum-slope model from both 80 kVp image data and iodine-enhancement images. Blood flow was measured in regions of interest placed in healthy pancreatic tissue and carcinoma for both of the derived perfusion maps. To assess image quality of input data, an adjusted contrast-to-noise ratio was calculated for 80 kVp images and iodine-enhancement images. Susceptibility of perfusion results to residual patient breathing motion during acquisition was investigated by measuring blood flow in fatty tissue surrounding the pancreas, where blood flow should be negligible compared with the pancreas. For both 80 kVp and iodine-enhancement images, blood flow was significantly higher in healthy tissue (114.2 ± 37.4 mL/100 mL/min or 115.1 ± 36.2 mL/100 mL/min, respectively) than in carcinoma (46.5 ± 26.6 mL/100 mL/min or 49.7 ± 24.7 mL/100 mL/min, respectively). Differences in blood flow between 80 kVp image data and iodine-enhancement images were statistically significant in healthy tissue, but not in carcinoma. For 80 kVp images, adjusted contrast-to-noise ratio was significantly higher (1.3 ± 1.1) than for iodine-enhancement images (1.1 ± 0.9). When evaluating fatty tissue surrounding the pancreas for estimating influence of patient motion, measured blood flow was significantly lower for iodine-enhancement images (30.7 ± 12.0 mL/100 mL/min) than for 80 kVp images (39.0 ± 19.1 mL/100 mL/min). Average patient radiation exposure was 8.01 mSv for dynamic DECT acquisition, compared with 4.60 mSv for dynamic 80 kVp acquisition. Iodine enhancement images can be used to calculate CT perfusion maps of blood flow, and compared with 80 kVp images, results showed only a small difference of 1 mL/100 mL/min in blood flow in healthy tissue, whereas patient radiation exposure was increased for dynamic DECT. Perfusion maps calculated based on iodine-enhancement images showed lower blood flow in fatty tissues surrounding the pancreas, indicating reduced susceptibility to residual patient breathing motion during the acquisition.

Dual-energy CT iodine maps as an alternative quantitative imaging biomarker to abdominal CT perfusion: determination of appropriate trigger delays for acquisition using bolus tracking.

Quantitative evaluation of different bolustracking trigger delays for acquisition of dualenergy (DE) CT iodine maps as an alternative to CT perfusion. Prior to this retrospective analysis of prospectively acquired data, DECT perfusion sequences were dynamically acquired in 22 patients with pancreatic carcinoma using dualsource CT at 80/140kVp with tin filtration. After deformable motion-correction, perfusion maps of blood flow (BF) were calculated from 80kVp image series of DECT, and iodine maps were calculated for each of the 34 DECT acquisitions per patient. BF and iodine concentrations were measured in healthy pancreatic tissue and carcinoma. To evaluate potential DECT acquisition triggered by bolus tracking, measured iodine concentrations from the 34 DECT acquisitions per patient corresponding to different trigger delays were assessed for correlation to BF and intergroup differences between tissue types depending on acquisition time. Average BF measured in healthy pancreatic tissue and carcinoma was 87.6 ± 28.4 and 38.6 ± 22.2 ml/100 mlmin-1, respectively. Correlation between iodine concentrations and BF was statistically significant for bolus tracking with trigger delay greater than 0 s (rmax = 0.89; p < 0.05). Differences in iodine concentrations between healthy pancreatic tissue and carcinoma were statistically significant for DECT acquisitions corresponding to trigger delays of 15-21 s (p < 0.05). An acquisition window between 15 and21 s after exceeding bolus tracking threshold shows promising results for acquisition of DECT iodine maps as an alternative to CTperfusion measurements of BF. Advances in knowledge: After clinical validation, DECT iodine maps of pancreas acquired using bolus tracking with appropriate trigger delay as determined in this study could offer an alternative quantitative imaging biomarker providing functional information for tumor assessment at reduced patient radiation exposure compared to CTperfusion measurements of BF.

Quantitative Assessment of Foot Blood Flow by Using Dynamic Volume Perfusion CT Technique: A Feasibility Study.

To demonstrate the feasibility of foot blood flow measurement by using dynamic volume perfusion computed tomographic (CT) technique with the upslope method in an animal experiment and a human study. The human study was approved by the institutional review board, and written informed consent was obtained from all patients. The animal study was approved by the research animal care and use committee. A perfusion CT experiment was first performed by using rabbits. A color-coded perfusion map was reconstructed by using in-house perfusion analysis software based on the upslope method, and the measured blood flow on the map was compared with the reference standard microsphere method by using correlation analysis. A total of 17 perfusion CT sessions were then performed (a) once in five human patients and (b) twice (before and after endovascular revascularization) in six human patients. Perfusion maps of blood flow were reconstructed and analyzed. The Wilcoxon signed rank test was used to prove significant differences in blood flow before and after treatment. The animal experiment demonstrated a strong correlation (R(2) = 0.965) in blood flow between perfusion CT and the microsphere method. Perfusion maps were obtained successfully in 16 human clinical sessions (94%) with the use of 32 mL of contrast medium and an effective radiation dose of 0.31 mSv (k factor for the ankle, 0.0002). The plantar dermis showed the highest blood flow among all anatomic structures of the foot, including muscle, subcutaneous tissue, tendon, and bone. After a successful revascularization procedure, the blood flow of the plantar dermis increased by 153% (P = .031). The interpretations of the color-coded perfusion map correlated well with the clinical and angiographic findings. Perfusion CT could be used to measure foot blood flow in both animals and humans. It can be a useful modality for the diagnosis of peripheral arterial disease by providing quantitative information on foot perfusion status.

Fractal Analysis of CT Perfusion Images for Evaluation of Antiangiogenic Treatment and Survival in Hepatocellular Carcinoma

Tumor vascular heterogeneity is a recognized biomarker for cancer progression. Our purpose was to assess the tumor perfusion heterogeneity during antiangiogenic therapy in hepatocellular carcinoma (HCC) by means of fractal analysis on computed tomography perfusion (CTP) images. Twenty-two patients (15 men and 7 women; mean age: 61.5 years) with advanced HCC underwent CTP at baseline and 2 weeks after administration of bevacizumab. Perfusion maps of blood flow (BF) were generated by the adiabatic approximation to the tissue homogeneity model with a motion registration, and fractal analyses were applied to gray-scale perfusion maps using a plugin tool on ImageJ software (NIH, Bethesda, MD). A differential box-counting method was applied, and the fractal dimension (FD) was calculated as a heterogeneity parameter. Patients were grouped into favorable progression-free survival (PFS) group (PFS>6 months, 11 patients) and unfavorable PFS group (PFS≤6, 11 patients). After 2 weeks of antiangiogenic therapy, the BF decreased significantly (P < .0001), whereas the FD showed no significant change (P = .69). The percent change of the FD in tumor BF was significantly different between patients with favorable PFS and those without (-2.52% vs. 3.72%, P = .01), whereas the change of tumor BF showed no significant difference between them (-28.93% vs. -25.47%, P = .64). In Kaplan-Meier analysis, patients with greater reduction in the percent change of FD and lower baseline FD in tumor BF showed significantly longer overall survival (P = .009, P = .005). Fractal analysis of tumor BF can be a biomarker for antiangiogenic therapy.