CT-perfusion measurements in pancreatic carcinoma with different kinetic models: Is there a chance for tumour grading based on functional parameters?

Innovative imaging approaches for neuroendocrine tumor characterization: Combined dual energy CT and perfusion protocol implementation

The article addresses the diagnostic value of the combined use of computed tomography (CT) perfusion and dual-energy CT (DECT) in patients with neuroendocrine tumors. It emphasizes the heterogeneity and complexity of these neoplasms, primarily affecting the gastrointestinal tract, bronchopulmonary system, and pancreas. While conventional CT is widely employed in their diagnosis, the combination of CT perfusion and dual-energy CT offers greater precision, particularly in detecting synchronous tumors and characterizing their vascularization. A clinical case of a patient with chronic abdominal symptoms, whose diagnosis was facilitated using both combined techniques, is presented. The discussion explores how CT perfusion assesses tumor vascularization and how dual-energy CT improves soft tissue differentiation, resulting in increased diagnostic accuracy. It is highlighted that this approach not only enhances detection rates but also positively impacts clinical management and healthcare costs. Therefore, the importance of considering these advanced tools in the diagnosis of neuroendocrine tumors to improve diagnostic precision and efficiency in patient care is underscored.

Computed Tomography Perfusion Combined With Preoperative Embolization for Reducing Intraoperative Blood Loss in Separation Surgery for Thoracolumbar Metastases

STUDY DESIGN

A prospective consecutive case study.

OBJECTIVE

This study aimed to assess the accuracy of computed tomography perfusion (CTP) in evaluating the vascularity of thoracolumbar metastases and to determine the impact of combining CTP with preoperative embolization on reducing intraoperative blood loss during separation surgery.

SUMMARY OF BACKGROUND DATA

Surgery for thoracolumbar metastases is a complex procedure with the potential for substantial blood loss. Therefore, assessing tumor vascularity before surgery and taking measures to minimize intraoperative blood loss is essential.

METHODS

A total of 62 patients with thoracolumbar metastases were prospectively enrolled. All patients underwent separation surgery using the posterior approach. Before surgery, the vascularity of the metastases was evaluated using CTP. On the basis of the CTP results, patients were categorized into hypervascular and hypovascular groups. Preoperative angiography and embolization were performed for the hypervascular group. Clinical data were abstracted, including intraoperative blood loss, perioperative complications, visual analog scale score, neurological status, and the accuracy of vascularity evaluation by CTP confirmed by angiography. χ 2 testing was used to compare categorical variables, whereas independent sample t tests were used to compare continuous variables, with paired t tests used to assess differences from preoperative to postoperative time points.

RESULTS

The mean intraoperative blood loss was 485±167 and 455±127.6 mL in the two groups, respectively. The accuracy of vascularity evaluation by CTP was 100%. In the hypervascular group, 80.6% of the patients experienced at least one level of improvement in neurological status, while the hypovascular group had 81.5% of patients with similar improvement. None of the patients experienced neurological deterioration. There was a significant reduction in visual analog scale scores in both groups after the operation.

CONCLUSIONS

The vascularity of thoracolumbar metastases could be accurately evaluated using noninvasive CTP. When combined with preoperative embolization, this approach effectively and safely reduced intraoperative blood loss in the setting of separation surgery.

Automated Quantitative Analysis of CT Perfusion to Classify Vascular Phenotypes of Pancreatic Ductal Adenocarcinoma

CT perfusion (CTP) analysis is difficult to implement in clinical practice. Therefore, we investigated a novel semi-automated CTP AI biomarker and applied it to identify vascular phenotypes of pancreatic ductal adenocarcinoma (PDAC) and evaluate their association with overall survival (OS).

METHODS

From January 2018 to November 2022, 107 PDAC patients were prospectively included, who needed to undergo CTP and a diagnostic contrast-enhanced CT (CECT). We developed a semi-automated CTP AI biomarker, through a process that involved deformable image registration, a deep learning segmentation model of tumor and pancreas parenchyma volume, and a trilinear non-parametric CTP curve model to extract the enhancement slope and peak enhancement in segmented tumors and pancreas. The biomarker was validated in terms of its use to predict vascular phenotypes and their association with OS. A receiver operating characteristic (ROC) analysis with five-fold cross-validation was performed. OS was assessed with Kaplan-Meier curves. Differences between phenotypes were tested using the Mann-Whitney U test.

RESULTS

The final analysis included 92 patients, in whom 20 tumors (21%) were visually isovascular. The AI biomarker effectively discriminated tumor types, and isovascular tumors showed higher enhancement slopes (2.9 Hounsfield unit HU/s vs. 2.0 HU/s, p < 0.001) and peak enhancement (70 HU vs. 47 HU, p < 0.001); the AUC was 0.86. The AI biomarker's vascular phenotype significantly differed in OS (p < 0.01).

CONCLUSIONS

The AI biomarker offers a promising tool for robust CTP analysis. In PDAC, it can distinguish vascular phenotypes with significant OS prognostication.

Pancreatic CT perfusion: quantitative meta-analysis of disease discrimination, protocol development, and effect of CT parameters

BACKGROUND

This study provides a quantitative meta-analysis of pancreatic CT perfusion studies, investigating choice of study parameters, ability for quantitative discrimination of pancreatic diseases, and influence of acquisition and reconstruction parameters on reported results.

METHODS

Based on a PubMed search with key terms 'pancreas' or 'pancreatic,' 'dynamic' or 'perfusion,' and 'computed tomography' or 'CT,' 491 articles published between 1982 and 2020 were screened for inclusion in the study. Inclusion criteria were: reported original data, human subjects, five or more datasets, measurements of pancreas or pancreatic pathologies, and reported quantitative perfusion parameters. Study parameters and reported quantitative measurements were extracted, and heterogeneity of study parameters and trends over time are analyzed. Pooled data were tested with weighted ANOVA and ANCOVA models for differences in perfusion results between normal pancreas, pancreatitis, PDAC (pancreatic ductal adenocarcinoma), and non-PDAC (e.g., neuroendocrine tumors, insulinomas) and based on study parameters.

RESULTS

Reported acquisition parameters were heterogeneous, except for contrast agent amount and injection rate. Tube potential and slice thickness decreased, whereas tube current time product and scan coverage increased over time. Blood flow and blood volume showed significant differences between pathologies (both p < 0.001), unlike permeability (p = 0.11). Study parameters showed a significant effect on reported quantitative measurements (p < 0.05).

CONCLUSIONS

Significant differences in perfusion measurements between pathologies could be shown for pooled data despite observed heterogeneity in study parameters. Statistical analysis indicates most influential parameters for future optimization and standardization of acquisition protocols.

CRITICAL RELEVANCE STATEMENT

Quantitative CT perfusion enables differentiation of pancreatic pathologies despite the heterogeneity of study parameters in current clinical practice.

CT perfusion as a potential biomarker for pancreatic ductal adenocarcinoma during routine staging and restaging

PURPOSE

To evaluate the significance of CT perfusion parameters predicting response to neoadjuvant therapy in patients with pancreatic ductal adenocarcinoma (PDAC).

MATERIALS AND METHODS

Seventy patients with PDAC prospectively had CT perfusion acquisition incorporated into baseline multiphase staging CT. Twenty-eight who were naïve to therapy were retained for further investigation. Perfusion was performed 5-42.5 s after contrast, followed by parenchymal and portal venous phases. Blood flow (BF), blood volume (BV), and permeability surface area product (PS) were calculated using deconvolution algorithms. Patients were categorized as responders or non-responders per RECIST 1.1. Perfusion variables with AUC ≥ 0.70 in differentiating responders from non-responders were retained. Logistic regression was used to assess associations between baseline perfusion variables and response.

RESULTS

18 of 28 patients showed favorable response to therapy. Baseline heterogeneity variables in tumor max ROI were higher in non-responders than responders [median BF coefficient of variation (CV) 0.91 vs. 0.51 respectively, odds ratio (OR) 6.8 per one standard deviation (1-SD) increase, P = 0.047; median PS CV 1.6 vs. 0.68, OR 3.9 per 1-SD increase, P = 0.047; and median BV CV 0.75 vs. 0.54, OR = 4.0 per 1-SD increase, P = 0.047]. Baseline BV mean in tumor center was lower in non-responders than responders (median BV mean: 0.74 vs. 2.9 ml/100 g respectively, OR 0.28 per 1-SD increase, P = 0.047).

CONCLUSION

For patients with PDAC receiving neoadjuvant therapy, lower and more heterogeneous perfusion parameters correlated with an unfavorable response to therapy. Such quantitative information can be acquired utilizing a comprehensive protocol interleaving perfusion CT acquisition with standard of care multiphase CT scans using a single contrast injection, which could be used to identify surgical candidates and predict outcome.

Quantitative CT perfusion imaging in patients with pancreatic cancer: a systematic review

Pancreatic ductal adenocarcinoma (PDAC) is the third leading cause of cancer-related death with a 5-year survival rate of 10%. Quantitative CT perfusion (CTP) can provide additional diagnostic information compared to the limited accuracy of the current standard, contrast-enhanced CT (CECT). This systematic review evaluates CTP for diagnosis, grading, and treatment assessment of PDAC. The secondary goal is to provide an overview of scan protocols and perfusion models used for CTP in PDAC. The search strategy combined synonyms for 'CTP' and 'PDAC.' Pubmed, Embase, and Web of Science were systematically searched from January 2000 to December 2020 for studies using CTP to evaluate PDAC. The risk of bias was assessed using QUADAS-2. 607 abstracts were screened, of which 29 were selected for full-text eligibility. 21 studies were included in the final analysis with a total of 760 patients. All studies comparing PDAC with non-tumorous parenchyma found significant CTP-based differences in blood flow (BF) and blood volume (BV). Two studies found significant differences between pathological grades. Two other studies showed that BF could predict neoadjuvant treatment response. A wide variety in kinetic models and acquisition protocol was found among included studies. Quantitative CTP shows a potential benefit in PDAC diagnosis and can serve as a tool for pathological grading and treatment assessment; however, clinical evidence is still limited. To improve clinical use, standardized acquisition and reconstruction parameters are necessary for interchangeability of the perfusion parameters.

Feasibility of wide detector CT perfusion imaging performed during routine staging and restaging of pancreatic ductal adenocarcinoma

PURPOSE

To evaluate the feasibility of CT perfusion performed during routine multiphase contrast-enhanced CT on a 160 mm wide-coverage 256-slice scanner in patients with pancreatic ductal adenocarcinoma (PDAC).

METHODS

Fifty-seven patients had a CT perfusion acquisition during their routine multiphase CT. Perfusion was performed 5 to 42.5 s (15 passes at 2.5 s intervals) after intravenous contrast administration (4.2-5 ml/s), followed by pancreatic parenchymal and portal venous phases for clinical interpretation. Perfusion maps were generated and blood flow (BF), blood volume (BV), and permeability surface area product (PS) for tumor and uninvolved pancreas were calculated using deconvolution algorithms and compared to existing similar publications. Radiation dose information was recorded and size-specific dose estimate (SSDE) was calculated using body dimensions.

RESULTS

Diagnostic quality of standard images was unaffected by performing the perfusion acquisition. Average tumor center BF was 20.8 ± 12.1 ml/100 g/min, BV 2.5 ± 2.1 ml/100 g and PS 15.5 ± 39.4 ml/100 g/min. Average pancreas BF was 90.8 ± 50.2 ml/100 g/min, BV 11.9 ± 4.3 ml/100 g and PS 33.6 ± 27.7 ml/100 g/min. For the perfusion acquisition, mean SSDE was 57 ± 11 mGy, CTDI_vol 43 ± 6 mGy and DLP 685 ± 100 mGy-cm.

CONCLUSION

Adding a perfusion CT acquisition to standard pancreatic CT protocol is feasible using a wide-detector 256-slice CT scanner and adds quantitative information while maintaining diagnostic quality of the standard of care examination. This novel protocol adds no time or cost to the examination and yields perfusion parameters that are comparable to existing literature using a separate dedicated perfusion protocol.

Computed Tomography Perfusion Analysis of Pancreatic Adenocarcinoma using Deconvolution, Maximum Slope, and Patlak Methods - Evaluation of Diagnostic Accuracy and Interchangeability of Cut-Off Values

PURPOSE

 The goal of this study was to evaluate the diagnostic accuracy of perfusion computed tomography (CT) parameters obtained by different mathematical-kinetic methods for distinguishing pancreatic adenocarcinoma from normal tissue. To determine cut-off values and to assess the interchangeability of cut-off values, which were determined by different methods.

MATERIALS AND METHODS

 Perfusion CT imaging of the pancreas was prospectively performed in 23 patients. 19 patients with histopathologically confirmed pancreatic adenocarcinoma were included in the study. Blood flow (BF), blood volume (BV) and permeability-surface area product (PS) were measured in pancreatic adenocarcinoma and normal tissue with the deconvolution (BF, BV, PS), maximum slope (BF), and Patlak methods (BV, PS). The interchangeability of cut-off values was examined by assessing agreement between BF, BV, and PS measured with different mathematical-kinetic methods.

RESULTS

 Bland-Altman analysis demonstrated poor agreement between perfusion parameters, measured with different mathematical-kinetic methods. According to receiver operating characteristic (ROC) analysis, PS measured with the Patlak method had the significantly lowest diagnostic accuracy (area under ROC curve = 0.748). All other parameters were of high diagnostic accuracy (area under ROC curve = 0.940-0.997), although differences in diagnostic accuracy were not statistically different. Cut-off values for BF of ≤ 91.83 ml/100 ml/min and for BV of ≤ 5.36 ml/100 ml, both measured with the deconvolution method, appear to be the most appropriate cut-off values to distinguish pancreatic adenocarcinoma from normal tissue.

CONCLUSION

 Perfusion parameters obtained by different methods are not interchangeable. Therefore, cut-off values, which were determined using different methods, are not interchangeable either. Perfusion parameters can help to distinguish pancreatic adenocarcinoma from normal tissue with high diagnostic accuracy, except for PS measured with the Patlak method.

KEY POINTS

  · Perfusion CT parameters showed high diagnostic accuracy in differentiating between pancreatic adenocarcinoma and normal tissue.. · Only PS measured with the Patlak method showed a significantly lower diagnostic accuracy.. · Perfusion parameters measured with different mathematical-kinetic methods are not interchangeable.. · A specific cut-off value must be determined for each method and each perfusion parameter..

CITATION FORMAT

· Koell M, Klauss M, Skornitzke S et al. Computed Tomography Perfusion Analysis of Pancreatic Adenocarcinoma with the Deconvolution, Maximum Slope, and Patlak Methods - Evaluation of Diagnostic Accuracy and Interchangeability of Cut-Off Values. Fortschr Röntgenstr 2021; 193: 1062 - 1073.

Dynamic perfusion CT - A promising tool to diagnose pancreatic ductal adenocarcinoma

Background and objective

This study deals with an important issue of setting the role and value of the dynamic computed tomography (CT) perfusion analysis in diagnosing pancreatic ductal adenocarcinoma (PDAC). The study aimed to assess the efficacy of perfusion CT in identifying PDAC, even isodense or hardly depicted in conventional multidetector computed tomography.

Methods

A total of 56 patients with PDAC and 56 control group patients were evaluated in this study. A local perfusion assessment, involving the main perfusion parameters, was evaluated for all the patients. Sensitivity, specificity, positive, and negative predictive values for each perfusion CT parameter were defined using cutoff values calculated using receiver operating characteristic curve analysis. We accomplished logistic regression to identify the probability of PDAC.

Results

Blood flow (BF) and blood volume (BV) values were significant independent diagnostic criteria for the presence of PDAC. If both values exceed the determined cutoff point, the estimated probability for the presence of PDAC was 97.69%.

Conclusions

Basic CT perfusion parameters are valuable in providing the radiological diagnosis of PDAC. The estimated BF and BV parameters may serve as independent diagnostic criteria predicting the probability of PDAC.

Dynamic Perfusion Computed Tomography and Apparent Diffusion Coefficient as Potential Markers for Poorly Differentiated Pancreatic Adenocarcinoma

INTRODUCTION

Tumor grade, one of the most important risk factors for survival, is routinely determined after examining the biopsy material or a surgically removed specimen. The aim of the study was to analyze computed tomography (CT) perfusion parameters and diffusion-weighted imaging apparent diffusion coefficient (ADC) values in pancreatic ductal adenocarcinoma (PDAC) and to establish the diagnostic value of these modalities determining the tumor grade.

MATERIALS AND METHODS

A prospective clinical study included 56 subjects with PDAC. All the patients had a local perfusion assessment and ADC measurement of the tumor. For the prediction of poor tumor differentiation sensitivity, specificity, positive, and negative predictive values for each perfusion CT and ADC parameters based on cutoff values from ROC analysis were calculated.

RESULTS

Mean transit time (MTT) and ADC values were found to be independent prognosticators for the presence of G3 PDAC. MTT and ADC at the cutoff of 17.37 s and 1.15 × 10-3 mm2/s, respectively, appeared to be significant parameters discriminating against the differentiation grade of PDAC. If both values exceeded the defined cutoff point, the estimated probability for the presence of G3 PDAC was 89.29%.

CONCLUSION

The MTT parameter, calculated with the deconvolution method, and the ADC value may serve as effective independent prognosticators identifying poorly differentiated PDAC.

Assessment of tissue perfusion of pancreatic cancer as potential imaging biomarker by means of Intravoxel incoherent motion MRI and CT perfusion: correlation with histological microvessel density as ground truth

Background/objectives

The aim of this study was to compare intravoxel incoherent motion (IVIM) diffusion weighted (DW) MRI and CT perfusion to assess tumor perfusion of pancreatic ductal adenocarcinoma (PDAC).

Methods

In this prospective study, DW-MRI and CT perfusion were conducted in nineteen patients with PDAC on the day before surgery. IVIM analysis of DW-MRI was performed and the parameters perfusion fraction f, pseudodiffusion coefficient D*, and diffusion coefficient D were extracted for tumors, upstream, and downstream parenchyma. With a deconvolution-based analysis, the CT perfusion parameters blood flow (BF) and blood volume (BV) were estimated for tumors, upstream, and downstream parenchyma. In ten patients, intratumoral microvessel density (MVD_tumor) and microvessel area (MVA_tumor) were analyzed microscopically in resection specimens. Correlation coefficients between IVIM parameters, CT perfusion parameters, and histological microvessel parameters in tumors were calculated. Receiver operating characteristic (ROC) analysis was performed for differentiation of tumors and upstream parenchyma.

Results

f_tumor significantly positively correlated with BF_tumor (r = 0.668, p = 0.002) and BV_tumor (r = 0.672, p = 0.002). There were significant positive correlations between f_tumor and MVD_tumor/ MVA_tumor (r ≥ 0.770, p ≤ 0.009) as well as between BF_tumor and MVD_tumor/ MVA_tumor (r ≥ 0.697, p ≤ 0.025). Correlation coefficients between f_tumor and MVD_tumor/ MVA_tumor were not significantly different from correlation coefficients between BF_tumor and MVD_tumor/ MVA_tumor (p ≥ 0.400). Moreover, f, BF, BV, and permeability values (PEM) showed excellent performance in distinguishing tumors from upstream parenchyma (area under the ROC curve ≥0.874).

Conclusions

The study shows that IVIM derived f_tumor and CT perfusion derived BF_tumor similarly reflect vascularity of PDAC and seem to be comparably applicable for the evaluation of tumor perfusion for tumor characterization and as potential quantitative imaging biomarker.

Trial registration

DRKS, DRKS00022227, Registered 26 June 2020, retrospectively registered. https://www.drks.de/drks_web/navigate.do?navigationId=trial. HTML&TRIAL_ID=DRKS00022227.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40644-021-00382-x.

Low-Dose Perfusion Computed Tomography for Breast Cancer to Quantify Tumor Vascularity: Correlation With Prognostic Biomarkers

OBJECTIVES

The aim of this study was to investigate the feasibility of using low-dose perfusion computed tomography (CT) in breast cancers for quantification of tumor vascularity and to correlate perfusion indexes with prognostic biomarkers.

MATERIALS AND METHODS

This preliminary study was approved by our institutional review board. Signed informed consent was obtained from all 70 enrolled patients with invasive breast cancers. Low-dose perfusion CT was performed with the patient in the prone position using a spectral CT device set at 80 kVp and 30 mAs (1.30-1.40 mSv). Images were analyzed using commercial software applying the maximum slope algorithm. On CT perfusion maps, perfusion (mL/min per 100 mL), blood volume (mL/100 g), time-to-peak enhancement (second), and peak enhancement intensity (HU) were measured in the tumor, normal breast glandular tissues, and fat. Tumor grade, estrogen receptor (ER), human epidermal growth factor receptor 2 (HER2), and Ki67 level were evaluated using histopathology. Statistically, CT perfusion indexes of the tumor and normal glandular tissues or fat were compared using the Wilcoxon signed-rank test, and CT indexes were correlated with histological characteristics using the Mann-Whitney U or Kruskal-Wallis tests. We also correlated CT indexes with magnetic resonance imaging enhancement characteristics.

RESULTS

In breast cancers, perfusion, blood volume, and peak enhancement intensity values were significantly higher, and time to peak was shorter than in normal glandular tissues and fat (P < 0.001). Perfusion increased significantly in high-grade, ER-, or HER2+ cancers (P < 0.05). Time to peak decreased in ER-, HER2+, and high-grade cancers or in those with high Ki67 levels (P < 0.05). Peak enhancement intensity significantly increased in high-grade cancers (P < 0.05). HER2 overexpressing cancers showed significantly higher perfusion and shorter time to peak than luminal-type cancers (P < 0.05). Perfusion increased and time to peak decreased significantly in cancers with washout enhancement patterns on magnetic resonance imaging.

CONCLUSIONS

Low-dose perfusion CT in the prone position is feasible to quantify tumor vascularity in breast cancers, and CT perfusion indexes are significantly correlated with prognostic biomarkers and molecular subtypes of breast cancer.

Computed Tomography Perfusion Measurements in Renal Lesions Obtained by Bayesian Estimation, Advanced Singular-Value Decomposition Deconvolution, Maximum Slope, and Patlak Models: Intermodel Agreement and Diagnostic Accuracy of Tumor Classification

OBJECTIVES

The aims of this study were to evaluate the agreement of computed tomography (CT)-perfusion parameter values of the normal renal cortex and various renal tumors, which were obtained by different mathematical models, and to evaluate their diagnostic accuracy.

MATERIALS AND METHODS

Perfusion imaging was performed prospectively in 35 patients to analyze 144 regions of interest of the normal renal cortex and 144 regions of interest of renal tumors, including 21 clear-cell renal cell carcinomas (RCC), 6 papillary RCCs, 5 oncocytomas, 1 chromophobe RCC, 1 angiomyolipoma with minimal fat, and 1 tubulocystic RCC. Identical source data were postprocessed and analyzed on 2 commercial software applications with the following implemented mathematical models: maximum slope, Patlak plot, standard singular-value decomposition (SVD), block-circulant SVD, oscillation-limited block-circulant SVD, and Bayesian estimation technique. Results for blood flow (BF), blood volume (BV), and mean transit time (MTT) were recorded. Agreement and correlation between pairs of models and perfusion parameters were assessed. Diagnostic accuracy was evaluated by receiver operating characteristic (ROC) analysis.

RESULTS

Significant differences and poor agreement of BF, BV, and MTT values were noted for most of model comparisons in both the normal renal cortex and different renal tumors. The correlations between most model pairs and perfusion parameters ranged between good and perfect (Spearman ρ = 0.79-1.00), except for BV values obtained by Patlak method (ρ = 0.61-0.72). All mathematical models computed BF and BV values, which differed significantly between clear cell RCCs, papillary RCCs, and oncocytomas, which introduces them as useful diagnostic tests to differentiate between different histologic subgroups (areas under ROC curve, 0.83-0.99). The diagnostic accuracy to discriminate between clear-cell RCCs and the renal cortex was the lowest based on the Patlak plot model (area under ROC curve, 0.76); BF and BV values obtained by other algorithms did not differ significantly in their diagnostic accuracy.

CONCLUSIONS

Quantitative perfusion parameters obtained from different mathematical models cannot be used interchangeably. Based on BF and BV estimates, all models are a useful tool in the differential diagnosis of kidney tumors, with the Patlak plot model yielding a significantly lower diagnostic accuracy.

Is There a Direct Correlation Between Microvascular Wall Structure and k-Trans Values Obtained From Perfusion CT Measurements in Lymphomas?

RATIONALE AND OBJECTIVES

This study aimed to test the hypothesis that ultrastructural wall abnormalities of lymphoma vessels correlate with perfusion computed tomography (PCT) kinetics.

MATERIALS AND METHODS

Our local institutional review board approved this prospective study. Between February 2013 and June 2016, we included 23 consecutive subjects with newly diagnosed lymphoma, who were referred for computed tomography-guided biopsy (6 women, 17 men; mean age, 60.61 ± 12.43 years; range, 28-74 years) and additionally agreed to undergo PCT of the target lymphoma tissues. PCT was obtained for 40 seconds using 80 kV, 120 mAs, 64 × 0.6-mm collimation, 6.9-cm z-axis coverage, and 26 volume measurements. Mean and maximum k-trans (mL/100 mL/min), blood flow (BF; mL/100 mL/min) and blood volume (BV) were quantified using the deconvolution and the maximum slope + Patlak calculation models. Immunohistochemical staining was performed for microvessel density quantification (vessels/m²), and electron microscopy was used to determine the presence or absence of tight junctions, endothelial fenestration, basement membrane, and pericytes, and to measure extracellular matrix thickness.

RESULTS

Extracellular matrix thickness as well as the presence or absence of tight junctions, basal lamina, and pericytes did not correlate with computed tomography perfusion parameters. Endothelial fenestrations correlated significantly with mean BF_{deconvolution} (P = .047, r = 0.418) and additionally was significantly associated with higher mean BV_{deconvolution} (P < .005). Mean k-trans_Patlak correlated strongly with mean k-trans_{deconvolution} (r = 0.939, P = .001), and both correlated with mean BF_{deconvolution} (P = .001, r = 0.748), max BF_{deconvolution} (P = .028, r = 0.564), mean BV_{deconvolution} (P = .001, r = 0.752), and max BV_{deconvolution} (P = .001, r = 0.771). Microvessel density correlated with max k-trans_{deconvolution} (r = 0.564, P = .023). Vascular endothelial growth factor receptor-3 expression (receptor specific for lymphatics) correlated significantly with max k-trans_Patlak (P = .041, r = 0.686) and mean BF_{deconvolution} (P = .038, r = 0.695).

CONCLUSION

k-Trans values of PCT do not correlate with ultrastructural microvessel features, whereas endothelial fenestrations correlate with increased intra-tumoral BVs.

Evaluation of the effect of image noise on CT perfusion measurements using digital perfusion phantoms

OBJECTIVES

To assess the influence of image noise on computed tomography (CT) perfusion studies, CT perfusion software algorithms were evaluated for susceptibility to image noise and results applied to clinical perfusion studies.

METHODS

Digital perfusion phantoms were generated using a published deconvolution model to create time-attenuation curves (TACs) for 16 different combinations of blood flow (BF; 30/60/90/120 ml/100 ml/min) and flow extraction product (FEP; 10/20/30/40 ml/100 ml/min) corresponding to values encountered in clinical studies. TACs were distorted with Gaussian noise at 50 different strengths to approximate image noise, performing 200 repetitions for each noise level. A total of 160,000 TACs were evaluated by measuring BF and FEP with CT perfusion software, comparing results for the maximum slope and Patlak models with those obtained with a deconvolution model. To translate results to clinical practice, data of 23 patients from a CT perfusion study were assessed for image noise, and the accuracy of reported CT perfusion measurements was estimated.

RESULTS

Perfusion measurements depend on image noise as means and standard deviations of BF and FEP over repetitions increase with increasing image noise, especially for low BF and FEP values. BF measurements derived by deconvolution show larger standard deviations than those performed with the maximum slope model. Image noise in the evaluated CT perfusion study was 26.46 ± 3.52 HU, indicating possible overestimation of BF by up to 85% in a clinical setting.

CONCLUSIONS

Measurements of perfusion parameters depend heavily upon the magnitude of image noise, which has to be taken into account during selection of acquisition parameters and interpretation of results, e.g., as a quantitative imaging biomarker.

KEY POINTS

• CT perfusion results depend heavily upon the magnitude of image noise. • Different CT perfusion models react differently to the presence of image noise. • Blood flow may be overestimated by 85% in clinical CT perfusion studies.

The feasibility of low-concentration contrast and low tube voltage in computed tomography perfusion imaging: an animal study

Aim: To investigate the feasibility of low-concentration contrast (270 mg/ml) together with low tube voltage (80 kV) and adaptive iterative dose reduction (AIDR)-3D reconstruction in liver computed tomography (CT) perfusion imaging.

Method: A total of 15 healthy New Zealand rabbits received two CT scans each. The first scan (control) was acquired at 100 kV and 100 mA with iopromide (370 mg/ml), while the second scan (experimental) was acquired at 80 kV and 100 mA with iodixanol (270 mg/ml) 24 h after the first scan. The obtained images were reconstructed with filtered back projection (FBP) and AIDR-3D in the control and experimental groups respectively. The perfusion parameters (hepatic artery perfusion [HAP], portal vein perfusion [PVP], hepatic perfusion index [HPI], and total liver perfusion [TLP]) and image quality (image quality score, average CT value of abdomen aorta, signal-to-noise ratio [SNR], contrast-to-noise ratio [CNR], and figure of merit [FOM]) were compared using a paired t-test or Mann–Whitney U test between the two groups, when appropriate. The effective radiation dose and iodine intake were also recorded and compared.

Results: With the exception of the FOM criteria, the image quality and perfusion parameters were not significantly different between the two groups. The effective radiation dose and iodine intake were 38.79% and 27.03% lower respectively, in the experimental group.

Conclusion: Low-concentration contrast (iodixanol, 270 mg/ml) together with low tube voltage (80 kV) and AIDR-3D reconstruction help to reduce radiation dose and iodine intake without compromising perfusion parameters and image quality in liver CT perfusion imaging.