CT Perfusion Data Research Articles

Differences in Automated Perfusion Software: Do They Matter Clinically?

Differences in Automated Perfusion Software: Do They Matter Clinically?

Application of quantitative EEG in acute ischemic stroke patients who underwent thrombectomy: A comparison with CT perfusion

ObjectiveThis study aimed to evaluate the predictive value of quantitative electroencephalography (QEEG) in the outcome of patients with acute ischemic stroke (AIS) who underwent mechanical thrombectomy (MT) and to assess the correlation between clinical outcome and QEEG and CT perfusion (CTP) data. MethodsTwenty-nine MT patients were included in this prospective study. Continuous electroencephalography (EEG) monitoring was performed, in which delta power, the δ/α ratio (DAR), and the (θ + δ)/(α + β) ratio (DTABR) were calculated. The clinical scores at different points were recorded. Based on the modified Ranking scale, the patients were divided into good and poor outcome groups. Several CTP parameters were recorded before MT. The correlation between QEEG, CTP parameters, and clinical scores was analyzed using the Spearman correlation analysis. The predictive value of QEEG indices and CTP parameters for the 3-month outcome was compared using the receiver operating characteristic (ROC) curve. ResultsDelta power except for 7 days after MT, DAR, DATBR, and several CTP parameters were all significantly associated with the clinical scores. Although some CTP parameters were associated with the clinical scores, they were less powerful than QEEG in predicting a good or poor outcome at 3 months. Among the different explored EEG indicators, the predictive value of delta 24 h after MT was the highest. ConclusionsQEEG indices may have a certain predictive value for the outcome of AIS patients who underwent MT. SignificanceQEEG may become a new prognostic tool in AIS patients who underwent MT, facilitating the planning and management of related rehabilitation plans.

Fractal Analysis of Dynamic Stress CT-Perfusion Imaging for Detection of Hemodynamically Relevant Coronary Artery Disease

BackgroundCombined computed tomography–derived myocardial blood flow (CTP-MBF) and computed tomography angiography (CTA) has shown good diagnostic performance for detection of coronary artery disease (CAD). However, fractal analysis might provide additional insight into ischemia pathophysiology by characterizing multiscale perfusion patterns and, therefore, may be useful in diagnosing hemodynamically significant CAD. ObjectivesThe purpose of this study was to investigate, in a multicenter setting, whether fractal analysis of perfusion improves detection of hemodynamically relevant CAD over myocardial blood flow quantification (CTP-MBF) using dynamic, 4-dimensional, dynamic stress myocardial computed tomography perfusion (CTP) imaging. MethodsIn total, 7 centers participating in the prospective AMPLIFiED (Assessment of Myocardial Perfusion Linked to Infarction and Fibrosis Explored with Dual-source CT) study acquired CTP and CTA data in patients with suspected or known CAD. Hemodynamically relevant CAD was defined as ≥90% stenosis on invasive coronary angiography or fractional flow reserve <0.80. Both fractal analysis and CTP-MBF quantification were performed on CTP images and were combined with CTA results. ResultsThis study population included 127 participants, among them 61 patients, or 79 vessels, with CAD as per invasive reference standard. Compared with the combination of CTP-MBF and CTA, combined fractal analysis and CTA improved sensitivity on the per-patient level from 84% (95% CI: 72%-92%) to 95% (95% CI: 86%-99%; P = 0.01) and specificity from 70% (95% CI: 57%-82%) to 89% (95% CI: 78%-96%; P = 0.02). The area under the receiver-operating characteristic curve improved from 0.83 (95% CI: 0.75-0.90) to 0.92 (95% CI: 0.86-0.98; P = 0.01). ConclusionsFractal analysis constitutes a quantitative and pathophysiologically meaningful approach to myocardial perfusion analysis using dynamic stress CTP, which improved diagnostic performance over CTP-MBF when combined with anatomical information from CTA.

End-to-End Deep Learning Approach for Perfusion Data: A Proof-of-Concept Study to Classify Core Volume in Stroke CT.

(1) Background: CT perfusion (CTP) is used to quantify cerebral hypoperfusion in acute ischemic stroke. Conventional attenuation curve analysis is not standardized and might require input from expert users, hampering clinical application. This study aims to bypass conventional tracer-kinetic analysis with an end-to-end deep learning model to directly categorize patients by stroke core volume from raw, slice-reduced CTP data. (2) Methods: In this retrospective analysis, we included patients with acute ischemic stroke due to proximal occlusion of the anterior circulation who underwent CTP imaging. A novel convolutional neural network was implemented to extract spatial and temporal features from time-resolved imaging data. In a classification task, the network categorized patients into small or large core. In ten-fold cross-validation, the network was repeatedly trained, evaluated, and tested, using the area under the receiver operating characteristic curve (ROC-AUC). A final model was created in an ensemble approach and independently validated on an external dataset. (3) Results: 217 patients were included in the training cohort and 23 patients in the independent test cohort. Median core volume was 32.4 mL and was used as threshold value for the binary classification task. Model performance yielded a mean (SD) ROC-AUC of 0.72 (0.10) for the test folds. External independent validation resulted in an ensembled mean ROC-AUC of 0.61. (4) Conclusions: In this proof-of-concept study, the proposed end-to-end deep learning approach bypasses conventional perfusion analysis and allows to predict dichotomized infarction core volume solely from slice-reduced CTP images without underlying tracer kinetic assumptions. Further studies can easily extend to additional clinically relevant endpoints.

Probability maps classify ischemic stroke regions more accurately than CT perfusion summary maps

ObjectivesTo compare single parameter thresholding with multivariable probabilistic classification of ischemic stroke regions in the analysis of computed tomography perfusion (CTP) parameter maps.MethodsPatients were included from two multicenter trials and were divided into two groups based on their modified arterial occlusive lesion grade. CTP parameter maps were generated with three methods—a commercial method (ISP), block-circulant singular value decomposition (bSVD), and non-linear regression (NLR). Follow-up non-contrast CT defined the follow-up infarct region. Conventional thresholds for individual parameter maps were established with a receiver operating characteristic curve analysis. Probabilistic classification was carried out with a logistic regression model combining the available CTP parameters into a single probability.ResultsA total of 225 CTP data sets were included, divided into a group of 166 patients with successful recanalization and 59 with persistent occlusion. The precision and recall of the CTP parameters were lower individually than when combined into a probability. The median difference [interquartile range] in mL between the estimated and follow-up infarct volume was 29/23/23 [52/50/52] (ISP/bSVD/NLR) for conventional thresholding and was 4/6/11 [31/25/30] (ISP/bSVD/NLR) for the probabilistic classification.ConclusionsMultivariable probability maps outperform thresholded CTP parameter maps in estimating the infarct lesion as observed on follow-up non-contrast CT. A multivariable probabilistic approach may harmonize the classification of ischemic stroke regions.Key Points• Combining CTP parameters with a logistic regression model increases the precision and recall in estimating ischemic stroke regions.• Volumes following from a probabilistic analysis predict follow-up infarct volumes better than volumes following from a threshold-based analysis.• A multivariable probabilistic approach may harmonize the classification of ischemic stroke regions.

Comparison of multiphase data from CT perfusion vs clinical 4-phase CT scans with respect to image quality, lesion detection, and LI-RADS classification in HCC patients

PurposeThe aim of this study was to assess the image quality and diagnostic performance of reconstructed arterial (A) and portal venous (PV) phases in computed tomography perfusion (CTP) scans compared to the corresponding phases in standard 4-phase CT and to assess the utility for LI-RADS classification using CTP and 4-phase 4CT. MethodsA total of 26 scans with each method (CTP and 4-phase CT) from 19 hepatocellular carcinoma patients were analyzed and compared. Arterial and PV phases reconstructed by advanced modeled iterative reconstruction at strength 4 (ADMIRE 4) from raw CTP data were compared with image sets from arterial and PV phases of 4-phase CT (ADMIRE 3) in the same patient with respect to image quality. ResultsQuantitative image analysis showed that reconstructed CTP datasets were equivalent to 4-phase CT image sets. Qualitative image analysis revealed similar lesion detection rates with the 2 methods for patients with an abdominal diameter ≤36 cm and body weight <90 kg, but lower detection rates with CTP for patients with an abdominal diameter >37 cm. There was no difference in Liver Imaging Reporting and Data System (LI-RADS) classifications between the 2 methods. ConclusionReconstructed CTP images can potentially replace 4-phase CT images in patients weighing <90 kg and with a body diameter <37 cm, as the 2 methods are comparable in terms of quantitative image quality and ability to detect and classify lesions based on LI-RADS criteria.

Vessel-GAN: Angiographic reconstructions from myocardial CT perfusion with explainable generative adversarial networks

Dynamic CT angiography derived from CT perfusion data can obviate a separate coronary CT angiography and the use of ionizing radiation and contrast agent, thereby enhancing patient safety. However, the image quality of dynamic CT angiography is inferior to standard CT angiography images in many studies. This paper proposes an explainable generative adversarial network named vessel-GAN, which resorts to explainable knowledge-based artificial intelligence to perform image translation with increased trustworthiness. Specifically, we design a loss term to better learn the representations of blood vessels in CT angiography images. The loss term based on expert knowledge guides the generator to focus its training on the important features predicted by the discriminator. Additionally, we propose a generator architecture that effectively fuses spatio-temporal representations and further enhances temporal consistency, thereby improving the quality of the generated CT angiography images. The experiment is conducted on a dataset consisting of 232 patients with suspected coronary artery stenosis. Experimental results show that the PSNR value of vessel-GAN is 28.32 dB, SSIM value is 0.91 and MAE value is 47.36. To validate the effectiveness of the proposed synthesis method, we compare that with other image translation frameworks and GAN-based methods. Compared to other image translation methods, the proposed method vessel-GAN can generate more clearly visible blood vessels from source perfusion images. The CTA images generated by vessel-GAN are closer to the real CTA due to the use of adversarial learning. Compared with other GAN-based methods, vessel-GAN can produce sharper and more homogeneous outputs, including realistic vascular structures. The experiment demonstrates that the explainable generative adversarial network has superior performance for it can better control how models learn. Overall, the CT angiography images generated by vessel-GAN can potentially replace a separate standard CT angiography, allowing the possibility of “one-stop” cardiac examination for high-risk coronary artery disease patients who need assessment of myocardial ischemia.

Comparative analysis of two mathematical algorithms for the calculation of computed tomography perfusion parameters in the healthy and diseased pancreas.

BackgroundThe maximum slope (MS) and deconvolution (DC) algorithms are commonly used to post‐process computed tomography perfusion (CTP) data. This study aims to analyze the differences between MS and DC algorithms for the calculation of pancreatic CTP parameters.MethodsThe pancreatic CTP data of 57 patients were analyzed using MS and DC algorithms. Two blinded radiologists calculated pancreatic blood volume (BV) and blood flow (BF). Interobserver correlation coefficients were used to evaluate the consistency between two radiologists. Paired t‐tests, Pearson linear correlation analysis, and Bland–Altman analysis were performed to evaluate the correlation and consistency of the CTP parameters between the two algorithms.ResultsAmong the 30 subjects with normal pancreas, the BV values in the three pancreatic regions were higher in the case of the MS algorithm than in the case of the DC algorithm (t = 39.35, p < 0.001), and the BF values in the three pancreatic regions were slightly higher for the MS algorithm than for the DC algorithm (t = 2.19, p = 0.031). Similarly, among the 27 patients with acute pancreatitis, the BV values obtained using the MS methods were higher than those obtained using the DC methods (t = 54.14, p < 0.001). Furthermore, the BF values were higher with the MS methods than the DC methods (t = 8.45, p < 0.001). Besides, Pearson linear correlation and Bland–Altman analysis showed that the BF and BV values showed a good correlation and a bad consistency between the two algorithms.ConclusionsThe BF and BV values measured using MS and DC algorithms had a good correlation but were not consistent.

Prognostic Value of a New Integrated Parameter-Both Collateral Circulation and Permeability Surface-in Hemorrhagic Transformation of Middle Cerebral Artery Occlusion Acute Ischemic Stroke: Retrospective Cohort Study.

BackgroundMultimodal CT, including CT angiography (CTA) and CT perfusion (CTP), was increasingly used in stroke triage. This study was to determine the relationship between a new integrated parameter—both collateral circulation and relative permeability surface (PS)—and the hemorrhagic transformation (HT) in acute ischemic stroke (AIS) with middle cerebral artery occlusion (MCAO).MethodsWe retrospectively reviewed consecutive AIS patients with MCAO who underwent baseline CTA/CTP within 4 h of symptom onset and follow-up susceptibility-weighted imaging (SWI) within 3 weeks. Collateral circulation was assessed on the baseline CTA. Baseline CTP data were postprocessed to generate PS parameter. The patients with poor collateral circulation and at the same time with high relative PS were classified as the group of both poor collateral circulation and high relative PS. HT was defined according to European Cooperative Acute Stroke Study II criteria on follow-up SWI imaging. Multivariate logistic regression analysis was performed using HT as an outcome variable.ResultsThe group of patients with both poor collateral circulation and high relative PS was thirteen and thirty-three (52%) developed HT of the final cohort sixty-three AIS patients with MCAO. Multivariate logistic analysis revealed the new integrated parameter—both collateral circulation and relative PS (odds ratio, 16.59; 95% confidence interval, 13.09–19.10; P < 0.001) was independent predictor of HT. The area under the curve was 0.85 (95% confidence interval, 0.81–0.89). The sensitivity was 57%, specificity 97% and positive predictive value 92%, negative predictive value 58%.ConclusionsFor AIS patients with MCAO, these with poor collateral circulation on CTA and at the same time with high relative PS on CTP were at high risk for HT.

Abstract P319: Can Deep Learning Find the Ischemic Core on CT? Transfer Learning From Pre-Trained MRI-Based Networks

Objective: Ischemic core prediction from CT perfusion (CTP) remains inaccurate compared with gold standard diffusion-weighted imaging (DWI). We evaluated if a deep learning model to predict the DWI lesion from MR perfusion (MRP) could facilitate ischemic core prediction on CTP. Method: Using the multi-center CRISP cohort of acute ischemic stroke patient with CTP before thrombectomy, we included patients with major reperfusion (TICI score≥2b), adequate image quality, and follow-up MRI at 3-7 days. Perfusion parameters including Tmax, mean transient time, cerebral blood flow (CBF), and cerebral blood volume were reconstructed by RAPID software. Core lab experts outlined the stroke lesion on the follow-up MRI. A previously trained MRI model in a separate group of patients was used as a starting point, which used MRP parameters as input and RAPID ischemic core on DWI as ground truth. We fine-tuned this model, using CTP parameters as input, and follow-up MRI as ground truth. Another model was also trained from scratch with only CTP data. 5-fold cross validation was used. Performance of the models was compared with ischemic core (rCBF≤30%) from RAPID software to identify the presence of a large infarct (volume&gt;70 or &gt;100ml). Results: 94 patients in the CRISP trial met the inclusion criteria (mean age 67±15 years, 52% male, median baseline NIHSS 18, median 90-day mRS 2). Without fine-tuning, the MRI model had an agreement of 73% in infarct &gt;70ml, and 69% in &gt;100ml; the MRI model fine-tuned on CT improved the agreement to 77% and 73%; The CT model trained from scratch had agreements of 73% and 71%; All of the deep learning models outperformed the rCBF segmentation from RAPID, which had agreements of 51% and 64%. See Table and figure. Conclusions: It is feasible to apply MRP-based deep learning model to CT. Fine-tuning with CTP data further improves the predictions. All deep learning models predict the stroke lesion after major recanalization better than thresholding approaches based on rCBF.

Abstract P321: The Cerebral Collateral Cascade: Rethinking the Assessment of Vascular Pathways in Acute Ischemic Stroke Patients

Background and Purpose: Robust collateral blood flow in patients with acute ischemic stroke due to large vessel occlusion (AIS-LVO) has been correlated with favorable outcomes. Collaterals are commonly assessed by the number of arteries present on non-invasive CT angiography (CTA) overlying ischemic brain, but blood transit from these arteries through the brain tissue and the venous drainage is not assessed by this technique. We hypothesized that a comprehensive imaging analysis of the cerebral collateral cascade (CCC) would predict clinical and radiological outcomes in patients with AIS-LVO patients. Materials and Methods: Multicenter retrospective cohort study of AIS-LVO patients undergoing thrombectomy triage. CCC was determined on pre-treatment imaging by scoring for pial arterial collaterals, tissue-level collaterals (TLC), and venous outflow. Pial arterial collaterals were determined by CTA (Tan scale), TLC were assessed on CT perfusion data using the Hypoperfusion Intensity Ratio, and venous egress was assessed on CTA using the cortical vein opacification score system. 3 groups were defined: CCC+ (good pial collaterals, TLC, and venous perfusion), CCC- (poor pial collaterals, TLC, and venous perfusion) and CCCmixed (reminder of patients). Primary outcome was a good functional outcome (modified Rankin Scale [mRS] 0-2 at 90 days). Secondary outcome was final infarct volume. Results: 647 patients met inclusion criteria: 176 CCC+, 345 CCCmixed and 126 CCC-. Multivariate ordinal logistic regression showed that CCC+ predicted good functional outcomes (mRS 0-2: OR=20.8 [95% CI 9.3-46.8]; p&lt;0.001) compared to CCC- and CCCmixed. CCCmixed (β: 27.1, SE: 7.7; p&lt;0.001) and CCC- (β: 86.6, SE: 9.9; p&lt;0.001) profiles were associated with higher final infarct volumes after treatment compared to CCC+. Conclusion: Comprehensive assessment of the collateral blood flow cascade in AIS-LVO patients is a strong predictor of clinical and radiological outcomes in AIS-LVO patients.

Automated estimation of ischemic core prior to thrombectomy: comparison of two current algorithms.

Endovascular thrombectomy (EVT) improves clinical outcomes in ischemic stroke with large vessel occlusion. Clinical benefits are inversely proportional to size of the pre-treatment ischemic core. This study compared estimated ischemic core volumes by two different CT perfusion (CTP) automated algorithms to the gold standard follow-up infarct volume using diffusion-weighted imaging (DWI) to assess for congruence, and thus eligibility for EVT. Retrospective, single-center cohort study of 102 patients presenting to a comprehensive stroke center between 2012 and 2018. Inclusion criteria were CT perfusion prior to EVT, successful EVT with mTIBI 2b-3 reperfusion, and DWI post-EVT. CTP data were retrospectively processed by two algorithms: "delay and dispersion insensitive deconvolution" (DISD, RAPID software) versus "delay and dispersion corrected single value decomposition" (ddSVD, Mistar software), using commercially available software. Core volumes were compared to follow up DWI using independent software (MRIcron). Agreement between each algorithm and DWI was estimated using Lin's concordance coefficient and analyzed using reduced major axis regression. We included 102 patients. Both algorithms had excellent agreement with DWI (Lin's concordance coefficients: DISD 0.8 (95% CI: 0.73; 0.87), ddSVD 0.92 (95% CI: 0.89; 0.95). Compared to ddSVD (reduced major axis slope = 0.95), DISD exhibited a larger extent of proportional bias (slope = 1.12). The ddSVD algorithm better correlates with DWI follow-up infarct volume than DISD processing. The DISD algorithm overestimated larger ischemic cores which may lead to patient exclusion from thrombectomy based on selection by core volume.

Investigation of convolutional neural networks using multiple computed tomography perfusion maps to identify infarct core in acute ischemic stroke patients.

Purpose: To assess acute ischemic stroke (AIS) severity, infarct is segmented using computed tomography perfusion (CTP) software, such as RAPID, Sphere, and Vitrea, relying on contralateral hemisphere thresholds. Since this approach is potentially patient dependent, we investigated whether convolutional neural networks (CNNs) could achieve better performances without the need for contralateral hemisphere thresholds. Approach: CTP and diffusion-weighted imaging (DWI) data were retrospectively collected for 63 AIS patients. Cerebral blood flow (CBF), cerebral blood volume (CBV), time-to-peak, mean-transit-time (MTT), and delay time maps were generated using Vitrea CTP software. U-net shaped CNNs were developed, trained, and tested for 26 different input CTP parameter combinations. Infarct labels were segmented from DWI volumes registered with CTP volumes. Infarct volumes were reconstructed from two-dimensional CTP infarct segmentations. To remove erroneous segmentations, conditional random field (CRF) postprocessing was applied and compared with prior results. Spatial and volumetric infarct agreement was assessed between DWI and CTP (CNNs and commercial software) using median infarct difference, median absolute error, dice coefficient, positive predictive value. Results: The most accurate combination of parameters for CNN segmenting infarct using CRF postprocessing was CBF, CBV, and MTT (4.83mL, 10.14mL, 0.66, 0.73). Commercial software results are: RAPID = (2.25mL, 21.48mL, 0.63, 0.70), Sphere = (7.57mL, 17.74mL, 0.64, 0.70), Vitrea = (6.79mL, 15.28mL, 0.63, 0.72). Conclusions: Use of CNNs with multiple input perfusion parameters has shown to be accurate in segmenting infarcts and has the ability to improve clinical workflow by eliminating the need for contralateral hemisphere comparisons.

Assessment of Cerebral Autoregulation in the Perifocal Zone of a Chronic Subdural Hematoma.

The knowledge of conservative treatment modalities for a chronic subdural hematoma (CSDH) is still based on low-grade evidence. The purpose of this study was to evaluate the condition of the microcirculation and autoregulation in the perifocal CSDH zone for understanding of the mechanism of CSDH development. Cerebral microcirculation was evaluated in patients with the aid of brain perfusion computed tomography (PCT) within the first day. Perfusion parameters were assessed quantitatively in the cortex zone adjacent to the CSDH and in a similar zone of the contralateral hemisphere. The same PCT data were assessed quantitatively without and with use of a perfusion calculation mode excluding large-vessel voxels ("remote vessels" (RVs)) in the first and second methods, respectively. The first method of analysis of a similar zone in the contralateral hemisphere revealed significant increases in cerebral blood volume and cerebral blood flow (P<0.01) in comparison with normal values. Use of the second method with RVs showed no significant changes in perfusion parameters in microcirculatory blood flow in the cortex on the side contralateral to the hematoma. The persistence of microcirculatory blood flow perfusion reflects preservation of cerebral blood flow autoregulation in patients with a CSDH.

White Matter Hypoperfusion Associated with Leukoaraiosis Predicts Intracranial Hemorrhage after Intravenous Thrombolysis

ObjectivesWhite matter hyperintensity is common in patients receiving intravenous thrombolysis. Some studies have expressed concern about the increased risk of hemorrhagic transformation and poor prognosis for those patients with pre-existing leukoaraiosis. The purpose of this study was to evaluate hypoperfusion associated with leukoaraiosis before thrombolysis using CT perfusion and to explore whether chronic white matter hypoperfusion increases risks of intracranial hemorrhage and poor clinical prognosis. Materials and MethodsWe collected 175 patients underwent intravenous thrombolysis with complete CT perfusion data and follow-up MRI between June 2017 and January 2020. We measured cerebral blood flow, cerebral blood volume, mean transit time and transit time to the peak at both periventricular and subcortical layers in the cerebral hemisphere contralateral to the stroke. The differences of white matter perfusion were compared between groups with different leukoaraiosis severity. Univariate analysis was used to compare in incidence of hemorrhagic transformation and poor prognosis between the hypoperfusion and normal perfusion groups. Further, we examined association between white matter hypoperfusion and intracranial hemorrhage after thrombolysis using logistic regression. ResultsThe length of periventricular transit time to the peak was independently associated with a higher risk of intracranial hemorrhage after thrombolysis (OR=4.740, 95%CI=1.624–13.837, P=0.004). The best predictive value was 4.012. But there was no significant difference in poor prognosis at 3 months between hypoperfusion (periventricular transit time to the peak≥4.012 s) and normal perfusion (periventricular transit time to the peak<4.012 s) group. ConclusionsImage presentations of white matter hypoperfusion reflected the severity of leukoaraiosis. White matter hypoperfusion was independently associated with intracranial hemorrhage after intravenous thrombolysis. However, hypoperfusion would not increase the risk of poor prognosis.

Low Dose CT Perfusion With K-Space Weighted Image Average (KWIA).

CTP (Computed Tomography Perfusion) is widely used in clinical practice for the evaluation of cerebrovascular disorders. However, CTP involves high radiation dose (≥ ~200mGy) as the X-ray source remains continuously on during the passage of contrast media. The purpose of this study is to present a low dose CTP technique termed K-space Weighted Image Average (KWIA) using a novel projection view-shared averaging algorithm with reduced tube current. KWIA takes advantage of k-space signal property that the image contrast is primarily determined by the k-space center with low spatial frequencies and over-sampled projections. KWIA divides each 2D Fourier transform (FT) or k-space CTP data into multiple rings. The outer rings are averaged with neighboring time frames to achieve adequate signal-to-noiseratio (SNR), while the center region of k-space remains unchanged to preserve high temporal resolution. Reduced dose sinogram data were simulated by adding Poisson distributed noise with zero mean on digital phantom and clinical CTP scans. A physical CTP phantom study was also performed with different X-ray tube currents. The sinogram data with simulated and real low doses were then reconstructed with KWIA, and compared with those reconstructed by standard filtered back projection (FBP) and simultaneous algebraic reconstruction with regularization of total variation (SART-TV). Evaluation of image quality and perfusion metrics using parameters including SNR, CNR (contrast-to-noise ratio), AUC (area-under-the-curve), and CBF (cerebral blood flow) demonstrated that KWIA is able to preserve the image quality, spatial and temporal resolution, as well as the accuracy of perfusion quantification of CTP scans with considerable (50–75%) dose-savings.

The diagnostic performance of perfusion CT in the detection of local tumor recurrence in head and neck cancer.

Detecting local tumor recurrence from post-treatment changes in head and neck cancer (HNC) remains a challenge. Based on the hypothesis that post-therapeutically altered tissue is bradytroph, lower perfusion values are expected in perfusion CT (PCT) while higher perfusion values are expected in recurrent malignant tissue. This prospective study investigates PCT for post-treatment recurrent HNC detection with a maximum slope algorithm. A total of 80 patients who received PCT of the head and neck for post-therapy follow-up, of which 63 had no tumor recurrence and 17 presented a histopathologically confirmed recurrence were examined. Regions of interest were placed in the location of the initial tumor, in reference ipsilateral nuchal muscle tissue and the corresponding internal carotid artery. Perfusion was calculated using a single-input maximum slope algorithm. With PCT, recurrent HNC can be differentiated from post-treatment tissue (p < 0.05). It further allows delineating recurrent tumor tissue from benign nuchal tissue of reference (p < 0.05). PCT data of patients with and without recurrent HNC are comparable as perfusion values of reference tissues in patients with and without HNC do not differ (p > 0.05). PCT in combination with a commercially available maximum slope algorithm offers radiologists a reliable imaging tool to detect recurrent head and neck cancer within post-therapeutically altered tissue.

Early detection of small volume stroke and thromboembolic sources with computed tomography: Rationale and design of the ENCLOSE study.

BackgroundComputed tomography is the most frequently used imaging modality in acute stroke imaging protocols. Detection of small volume infarcts in the brain and cardioembolic sources of stroke is difficult with current computed tomography protocols. Furthermore, the role of computed tomography findings to predict recurrent ischemic stroke is unclear. With ENCLOSE, we aim to improve (1) the detection of small volume infarcts with thin slice computed tomography perfusion (CTP) images and thromboembolic source with cardiac computed tomography techniques in the acute stage of ischemic stroke and (2) prediction of recurrent ischemic stroke with computed tomography-derived predictors.Methods/design: ENCLOSE is a prospective multicenter observational cohort study, which will be conducted in three Dutch stroke centers (ClinicalTrials.gov Identifier: NCT04019483). Patients (≥18 years) with suspected acute ischemic stroke who undergo computed tomography imaging within 9 h after symptom onset are eligible. Computed tomography imaging includes non-contrast CT, CTP, and computed tomography angiography (CTA) from base of the heart to the top of the brain. Dual-energy CT data will be acquired when possible, and thin-slice CTP reconstructions will be obtained in addition to standard 5 mm CTP data. CTP data will be processed with commercially available software and locally developed model-based methods. The post-processed thin-slice CTP images will be compared to the standard CTP images and to magnetic resonance diffusion-weighted imaging performed within 48 h after admission. Detection of cardioembolic sources of stroke will be evaluated on the CTA images. Recurrence will be evaluated 90 days and two years after the index event. The added value of imaging findings to prognostic models for recurrent ischemic stroke will be evaluated.ConclusionThe aim of ENCLOSE is to improve early detection of small volume stroke and thromboembolic sources and to improve prediction of recurrence in patients with acute ischemic stroke.

Prognostic value of CT perfusion and permeability imaging in traumatic brain injury.

Currently established prognostic models in traumatic brain injury (TBI) include noncontrast computed tomography (CT) which is insensitive to early perfusion alterations associated with secondary brain injury. Perfusion CT (PCT) on the other hand offers insight into early perfusion abnormalities. We hypothesized that adding CT perfusion and permeability data to the established outcome predictors improves the performance of the prognostic model. A prospective cohort study of consecutive 50 adult patients with head injury and Glasgow Coma Scale score of 12 or less was performed at a single Level 1 Trauma Centre. Perfusion CT was added to routine control CT 12 hours to 24 hours after admission. Region of interest analysis was performed in six major vascular territories on perfusion and permeability parametric maps. Glasgow Outcome Scale (GOS) was used 6 months later to categorize patients' functional outcomes to favorable (GOS score > 3) or unfavorable (GOS score ≤ 3). We defined core prognostic model, consisting of age, motor Glasgow Coma Scale score, pupillary reactivity, and CT Rotterdam Score. Next, we added perfusion and permeability data as predictors and compared updated models to the core model using cross-validated areas under the receiver operator curves (cv-AUC). Significant advantage over core model was shown by the model, containing both mean cerebral extravascular-extracellular volume per unit of tissue volume and cerebral blood volume of the least perfused arterial territory in addition to core predictors (cv-AUC, 0.75; 95% confidence interval, 0.51-0.84 vs. 0.6; 95% confidence interval, 0.37-0.74). The development of cerebral ischemia and traumatic cerebral edema constitutes the secondary brain injury and represents the target for therapeutic interventions. Our results suggest that adding CT perfusion and permeability data to the established outcome predictors improves the performance of the prognostic model in the setting of moderate and severe TBI. Prognostic study, level III.

Optimal Imaging at the Primary Stroke Center.

Optimal Imaging at the Primary Stroke Center.