Head movement during CT brain perfusion acquisition of patients with suspected acute ischemic stroke

Estimate and compensate head motion in non-contrast head CT scans using partial angle reconstruction and deep learning

BACKGROUND

Patient head motion is a common source of image artifacts in computed tomography (CT) of the head, leading to degraded image quality and potentially incorrect diagnoses. The partial angle reconstruction (PAR) means dividing the CT projection into several consecutive angular segments and reconstructing each segment individually. Although motion estimation and compensation using PAR has been developed and investigated in cardiac CT scans, its potential for reducing motion artifacts in head CT scans remains unexplored.

PURPOSE

To develop a deep learning (DL) model capable of directly estimating head motion from PAR images of head CT scans and to integrate the estimated motion into an iterative reconstruction process to compensate for the motion.

METHODS

Head motion is considered as a rigid transformation described by six time-variant variables, including the three variables for translation and three variables for rotation. Each motion variable is modeled using a B-spline defined by five control points (CP) along time. We split the full projections from 360° into 25 consecutive PARs and subsequently input them into a convolutional neural network (CNN) that outputs the estimated CPs for each motion variable. The estimated CPs are used to calculate the object motion in each projection, which are incorporated into the forward and backprojection of an iterative reconstruction algorithm to reconstruct the motion-compensated image. The performance of our DL model is evaluated through both simulation and phantom studies.

RESULTS

The DL model achieved high accuracy in estimating head motion, as demonstrated in both the simulation study (mean absolute error (MAE) ranging from 0.28 to 0.45 mm or degree across different motion variables) and the phantom study (MAE ranging from 0.40 to 0.48 mm or degree). The resulting motion-corrected image,I D L , P A R ${I}_{DL,\ PAR}$ , exhibited a significant reduction in motion artifacts when compared to the traditional filtered back-projection reconstructions, which is evidenced both in the simulation study (image MAE drops from 178 ± $ \pm $ 33HU to 37 ± $ \pm $ 9HU, structural similarity index (SSIM) increases from 0.60 ± $ \pm $ 0.06 to 0.98 ± $ \pm $ 0.01) and the phantom study (image MAE drops from 117 ± $ \pm $ 17HU to 42 ± $ \pm $ 19HU, SSIM increases from 0.83 ± $ \pm $ 0.04 to 0.98 ± $ \pm $ 0.02).

CONCLUSIONS

We demonstrate that using PAR and our proposed deep learning model enables accurate estimation of patient head motion and effectively reduces motion artifacts in the resulting head CT images.

Treatment of Acute Ischemic Stroke: The Last 30 Years of Trials and Tribulations

The landscape of acute ischemic stroke management has undergone a substantial transformation over the past 3 decades, mirroring our enhanced comprehension of the pathology and progress in diagnostic techniques, therapeutic interventions, and preventive measures. The 1990s marked a pivotal moment in stroke care with the integration of intravenous thrombolytics. However, the most significant paradigm shift in recent years has undoubtedly been the advent of endovascular thrombectomy. This article endeavors to deliver an exhaustive analysis of this revolutionary progression.

Mechanical Thrombectomy for Acute Ischemic Stroke

OBJECTIVE

Endovascular stroke therapy has greatly improved the ability to treat the deadliest and most disabling form of acute ischemic stroke. This article summarizes some of the recent innovations in this field and discusses likely future developments.

LATEST DEVELOPMENTS

At present, there is robust activity to improve all facets of care for patients with large vessel occlusion stroke, including better prehospital routing, more efficient in-hospital screening, expanding indications for thrombectomy eligibility, innovating novel thrombectomy devices, and improving the effects of recanalization on clinical outcomes. In addition, the integration of endovascular stroke therapy (EVT)-an emergent and frequently off-hours procedure that requires a specialized team of nurses, technologists, and physicians-into acute stroke care has transformed referral patterns, hospital accreditation pathways, and physician practices. The eligibility for the procedure will potentially continue to grow to include patients screened without advanced imaging, larger core infarcts, and more distal occlusions.

ESSENTIAL POINTS

In this review, we discuss the current state of EVT and its implications for practice, and present three cases that highlight some of the directions in which the field is moving.

Pictorial Review on Imaging Findings in Cerebral CTP in Patients with Acute Stroke and Its Mimics: A Primer for General Radiologists

The imaging evaluation of computed tomography (CT), CT angiography (CTA), and CT perfusion (CTP) is of crucial importance in the setting of each emergency department for suspected cerebrovascular impairment. A fast and clear assignment of characteristic imaging findings of acute stroke and its differential diagnoses is essential for every radiologist. Different entities can mimic clinical signs of an acute stroke, thus the knowledge and fast identification of stroke mimics is important. A fast and clear assignment is necessary for a correct diagnosis and a rapid initiation of appropriate therapy. This pictorial review describes the most common imaging findings in CTP with clinical signs for acute stroke or other acute neurological disorders. The knowledge of these pictograms is therefore essential and should also be addressed in training and further education of radiologists.

Clinical validation of an AI-based motion correction reconstruction algorithm in cerebral CT

OBJECTIVES

To evaluate the clinical performance of an artificial intelligence (AI)-based motion correction (MC) reconstruction algorithm for cerebral CT.

METHODS

A total of 53 cases, where motion artifacts were found in the first scan so that an immediate rescan was taken, were retrospectively enrolled. While the rescanned images were reconstructed with a hybrid iterative reconstruction (IR) algorithm (reference group), images of the first scan were reconstructed with both the hybrid IR (motion group) and the MC algorithm (MC group). Image quality was compared in terms of standard deviation (SD), signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), the mean squared error (MSE), peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and mutual information (MI), as well as subjective scores. The diagnostic performance for each case was evaluated accordingly by lesion detectability or the Alberta Stroke Program Early CT Score (ASPECTS) assessment.

RESULTS

Compared with the motion group, the SNR and CNR of the MC group were significantly increased. The MSE, PSNR, SSIM, and MI with respect to the reference group were improved by 44.1%, 15.8%, 7.4%, and 18.3%, respectively (all p < 0.001). Subjective image quality indicators were scored higher for the MC than the motion group (p < 0.05). Improved lesion detectability and higher AUC (0.817 vs 0.614) in the ASPECTS assessment were found for the MC to the motion group.

CONCLUSIONS

The AI-based MC reconstruction algorithm has been clinically validated for reducing motion artifacts and improving diagnostic performance of cerebral CT.

KEY POINTS

• An artificial intelligence-based motion correction (MC) reconstruction algorithm has been clinically validated in both qualitative and quantitative manner. • The MC algorithm reduces motion artifacts in cerebral CT and increases the diagnostic confidence for brain lesions. • The MC algorithm can help avoiding rescans caused by motion and improving the efficiency of cerebral CT in the emergency department.

Inferring CT perfusion parameters and uncertainties using a Bayesian approach

BACKGROUND

Computed tomography perfusion imaging is commonly used for the rapid assessment of patients presenting with symptoms of acute stroke. Maps of perfusion parameters, such as cerebral blood volume (CBV), cerebral blood flow (CBF), and mean transit time (MTT) derived from the perfusion scan data, provide crucial information for stroke diagnosis and treatment decisions. Most CT scanners use singular value decomposition (SVD)-based methods to calculate these parameters. However, some known problems are associated with conventional methods.

METHODS

In this work, we propose a Bayesian inference algorithm, which can derive both the perfusion parameters and their uncertainties. We apply the variational technique to the inference, which then becomes an expectation-maximization problem. The probability distribution (with Gaussian mean and variance) of each estimated parameter can be obtained, and the coefficient of variation is used to indicate the uncertainty. We perform evaluations using both simulations and patient studies.

RESULTS

In a simulation, we show that the proposed method has much less bias than conventional methods. Then, in separate simulations, we apply the proposed method to evaluate the impacts of various scan conditions, i.e., with different frame intervals, truncated measurement, or motion, on the parameter estimate. In one patient study, the method produced CBF and MTT maps indicating an ischemic lesion consistent with the radiologist's report. In a second patient study affected by patient movement, we showed the feasibility of applying the proposed method to motion corrected data.

CONCLUSIONS

The proposed method can be used to evaluate confidence in parameter estimation and the scan protocol design. More clinical evaluation is required to fully test the proposed method.

Automated Brain Perfusion Imaging in Acute Ischemic Stroke: Interpretation Pearls and Pitfalls

Recent advancements in computed tomography technology, including improved brain coverage and automated processing of the perfusion data, have reinforced the use of perfusion computed tomography imaging in the routine evaluation of patients with acute ischemic stroke. The DAWN (Diffusion Weighted Imaging or Computerized Tomography Perfusion Assessment With Clinical Mismatch in the Triage of Wake Up and Late Presenting Strokes Undergoing Neurointervention) and DEFUSE 3 (Endovascular Therapy Following Imaging Evaluation for Ischemic Stroke 3) trials have established the benefit of endovascular thrombectomy in patients with acute ischemic stroke with anterior circulation large vessel occlusion up to 24 hours of last seen normal, using perfusion imaging-based patient selection. The compelling data has prompted stroke centers to increasingly introduce automated perfusion computed tomography imaging in the routine evaluation of patients with acute ischemic stroke. We present a comprehensive overview of the acquisition and interpretation of automated perfusion imaging in patients with acute ischemic stroke with a special emphasis on the interpretation pearls, pitfalls, and stroke mimicking conditions.

What is the impact of head movement on automated CT perfusion mismatch evaluation in acute ischemic stroke?

OBJECTIVES

Automated CT perfusion mismatch assessment is an established treatment decision tool in acute ischemic stroke. However, the reliability of this method in patients with head motion is unclear. We therefore sought to evaluate the influence of head movement on automated CT perfusion mismatch evaluation.

METHODS

Using a realistic CT brain-perfusion-phantom, 7 perfusion mismatch scenarios were simulated within the left middle cerebral artery territory. Real CT noise and artificial head movement were added. Thereafter, ischemic core, penumbra volumes and mismatch ratios were evaluated using an automated mismatch analysis software (RAPID, iSchemaView) and compared with ground truth simulated values.

RESULTS

While CT scanner noise alone had only a minor impact on mismatch evaluation, a tendency towards smaller infarct core estimates (mean difference of -5.3 (-14 to 3.5) mL for subtle head movement and -7.0 (-14.7 to 0.7) mL for strong head movement), larger penumbral estimates (+9.9 (-25 to 44) mL and +35 (-14 to 85) mL, respectively) and consequently larger mismatch ratios (+0.8 (-1.5 to 3.0) for subtle head movement and +1.9 (-1.3 to 5.1) for strong head movement) were noted in dependence of patient head movement.

CONCLUSIONS

Motion during CT perfusion acquisition influences automated mismatch evaluation. Potentially treatment-relevant changes in mismatch classifications in dependence of head movement were observed and occurred in favor of mechanical thrombectomy.

Motion estimation and correction in SPECT, PET and CT

Patient motion impacts single photon emission computed tomography (SPECT), positron emission tomography (PET) and X-ray computed tomography (CT) by giving rise to projection data inconsistencies that can manifest as reconstruction artifacts, thereby degrading image quality and compromising accurate image interpretation and quantification. Methods to estimate and correct for patient motion in SPECT, PET and CT have attracted considerable research effort over several decades. The aims of this effort have been two-fold: to estimate relevant motion fields characterizing the various forms of voluntary and involuntary motion; and to apply these motion fields within a modified reconstruction framework to obtain motion-corrected images. The aims of this review are to outline the motion problem in medical imaging and to critically review published methods for estimating and correcting for the relevant motion fields in clinical and preclinical SPECT, PET and CT. Despite many similarities in how motion is handled between these modalities, utility and applications vary based on differences in temporal and spatial resolution. Technical feasibility has been demonstrated in each modality for both rigid and non-rigid motion, but clinical feasibility remains an important target. There is considerable scope for further developments in motion estimation and correction, and particularly in data-driven methods that will aid clinical utility. State-of-the-art machine learning methods may have a unique role to play in this context.

Assessment of computed tomography perfusion software in predicting spatial location and volume of infarct in acute ischemic stroke patients: a comparison of Sphere, Vitrea, and RAPID

BACKGROUND

CT perfusion (CTP) infarct and penumbra estimations determine the eligibility of patients with acute ischemic stroke (AIS) for endovascular intervention. This study aimed to determine volumetric and spatial agreement of predicted RAPID, Vitrea, and Sphere CTP infarct with follow-up fluid attenuation inversion recovery (FLAIR) MRI infarct.

METHODS

108 consecutive patients with AIS and large vessel occlusion were included in the study between April 2019 and January 2020 . Patients were divided into two groups: endovascular intervention (n=58) and conservative treatment (n=50). Intervention patients were treated with mechanical thrombectomy and achieved successful reperfusion (Thrombolysis in Cerebral Infarction 2b/2 c/3) while patients in the conservative treatment group did not receive mechanical thrombectomy or intravenous thrombolysis. Intervention and conservative treatment patients were included to assess infarct and penumbra estimations, respectively. It was assumed that in all patients treated conservatively, penumbra converted to infarct. CTP infarct and penumbra volumes were segmented from RAPID, Vitrea, and Sphere to assess volumetric and spatial agreement with follow-up FLAIR MRI.

RESULTS

Mean infarct differences (95% CIs) between each CTP software and FLAIR MRI for each cohort were: intervention cohort: RAPID=9.0±7.7 mL, Sphere=-0.2±8.7 mL, Vitrea=-7.9±8.9 mL; conservative treatment cohort: RAPID=-31.9±21.6 mL, Sphere=-26.8±17.4 mL, Vitrea=-15.3±13.7 mL. Overlap and Dice coefficients for predicted infarct were (overlap, Dice): intervention cohort: RAPID=(0.57, 0.44), Sphere=(0.68, 0.60), Vitrea=(0.70, 0.60); conservative treatment cohort: RAPID=(0.71, 0.56), Sphere=(0.73, 0.60), Vitrea=(0.72, 0.64).

CONCLUSIONS

Sphere proved the most accurate in patients who had intervention infarct assessment as Vitrea and RAPID overestimated and underestimated infarct, respectively. Vitrea proved the most accurate in penumbra assessment for patients treated conservatively although all software overestimated penumbra.

CT for Treatment Selection in Acute Ischemic Stroke: A Code Stroke Primer

CT is the primary imaging modality used for selecting appropriate treatment in patients with acute stroke. Awareness of the typical findings, pearls, and pitfalls of CT image interpretation is therefore critical for radiologists, stroke neurologists, and emergency department providers to make accurate and timely decisions regarding both (a) immediate treatment with intravenous tissue plasminogen activator up to 4.5 hours after a stroke at primary stroke centers and (b) transfer of patients with large-vessel occlusion (LVO) at CT angiography to comprehensive stroke centers for endovascular thrombectomy (EVT) up to 24 hours after a stroke. Since the DAWN and DEFUSE 3 trials demonstrated the efficacy of EVT up to 24 hours after last seen well, CT angiography has become the operational standard for rapid accurate identification of intracranial LVO. A systematic approach to CT angiographic image interpretation is necessary and useful for rapid triage, and understanding common stroke syndromes can help speed vessel evaluation. Moreover, when diffusion-weighted MRI is unavailable, multiphase CT angiography of collateral vessels and source-image assessment or perfusion CT can be used to help estimate core infarct volume. Both have the potential to allow distinction of patients likely to benefit from EVT from those unlikely to benefit. This article reviews CT-based workup of ischemic stroke for making tPA and EVT treatment decisions and focuses on practical skills, interpretation challenges, mimics, and pitfalls.^©RSNA, 2019.

Head Motion Correction Based on Filtered Backprojection in Helical CT Scanning

Head motion may unexpectedly occur during a CT scan. It thereby results in motion artifacts in a reconstructed image and may lead to a false diagnosis or a failure of diagnosis. To alleviate this motion problem, as a hardware approach, increasing the gantry rotation speed or using an immobilization device is usually considered. These approaches, however, cannot completely resolve the motion problem. Hence, motion estimation (ME) and compensation for it have been explored as a software approach instead. In this paper, adopting the latter approach, we propose a head motion correction algorithm in helical CT scanning, based on filtered backprojection (FBP). For the motion correction, we first introduce a new motion-compensated (MC) reconstruction scheme based on FBP, which is applicable to helical scanning. We then estimate the head motion parameters by using an iterative nonlinear optimization algorithm, or the L-BFGS. Note here that an objective function for the optimization is defined on reconstructed images in each iteration, which are obtained by using the proposed MC reconstruction scheme. Using the estimated motion parameters, we then obtain the final MC reconstructed image. Using numerical and physical phantom datasets along with simulated head motions, we demonstrate that the proposed algorithm can provide significantly improved quality to MC reconstructed images by alleviating motion artifacts.

Machine Learning-Enabled Automated Determination of Acute Ischemic Core From Computed Tomography Angiography

Background and Purpose- The availability of and expertise to interpret advanced neuroimaging recommended in the guideline-based endovascular stroke therapy (EST) evaluation are limited. Here, we develop and validate an automated machine learning-based method that evaluates for large vessel occlusion (LVO) and ischemic core volume in patients using a widely available modality, computed tomography angiogram (CTA). Methods- From our prospectively maintained stroke registry and electronic medical record, we identified patients with acute ischemic stroke and stroke mimics with contemporaneous CTA and computed tomography perfusion (CTP) with RAPID (IschemaView) post-processing as a part of the emergent stroke workup. A novel convolutional neural network named DeepSymNet was created and trained to identify LVO as well as infarct core from CTA source images, against CTP-RAPID definitions. Model performance was measured using 10-fold cross validation and receiver-operative curve area under the curve (AUC) statistics. Results- Among the 297 included patients, 224 (75%) had acute ischemic stroke of which 179 (60%) had LVO. Mean CTP-RAPID ischemic core volume was 23±42 mL. LVO locations included internal carotid artery (13%), M1 (44%), and M2 (21%). The DeepSymNet algorithm autonomously learned to identify the intracerebral vasculature on CTA and detected LVO with AUC 0.88. The method was also able to determine infarct core as defined by CTP-RAPID from the CTA source images with AUC 0.88 and 0.90 (ischemic core ≤30 mL and ≤50 mL). These findings were maintained in patients presenting in early (0-6 hours) and late (6-24 hours) time windows (AUCs 0.90 and 0.91, ischemic core ≤50 mL). DeepSymNet probabilities from CTA images corresponded with CTP-RAPID ischemic core volumes as a continuous variable with r=0.7 (Pearson correlation, P<0.001). Conclusions- These results demonstrate that the information needed to perform the neuroimaging evaluation for endovascular therapy with comparable accuracy to advanced imaging modalities may be present in CTA, and the ability of machine learning to automate the analysis.

Rigid motion artifact reduction in CT using extended difference function

BACKGROUND

In computed tomography (CT), a patient motion would result in degraded spatial resolution and image artifacts.

OBJECTIVE

To eliminate motion artifacts, this study presents a method to estimate the motion parameters from sinograms based on extended difference function.

METHODS

Based on our previous work, we first divide the projection data into two parts according to view angles and take Radon transform. Then, we calculate the extended difference functions and search for the minimum points. The relative displacements can be determined by the minimum points, and the motion can be estimated by the relationships between the relative displacements and motion parameters. Finally, we introduce the estimated parameters into the reconstruction process to compensate for the motion effects.

RESULTS

The simulation results show that the running times can reduce by about 30% than our previous work. In phantom experiments, the relative mean rotation excursion (RMRE) and relative mean translation excursion (RMTE) of the new method are lower than the conventional Helgason-Ludwig consistency condition (HLCC) based method and comparable to our previous work. Compare with the HLCC method, the root mean square error (RMSE) of the new method also reduces, while the Pearson correlation coefficient (CC) and mean structural similarity index (MSSIM) increase.

CONCLUSIONS

The proposed new method yields the improved performance on accuracy of motion estimation with higher computational efficiency, and thus it can produce high-quality images.

CT-based Techniques for Brain Perfusion

Recent rapid advances in endovascular treatment for acute ischemic stroke highlight the crucial role of neuroimaging especially multimodal computed tomography (CT) including CT perfusion in stroke triage and management decisions. With an increasing focus on changes in cerebral physiology along with time-based matrices in clinical decisions for acute ischemic stroke, CT perfusion provides a rapid and practical modality for assessment and identification of salvageable tissue at risk and infarct core and provides a better understanding of the changes in cerebral physiology. Although there are challenges with the lack of standardization and accuracy of quantitative assessment, CT perfusion is evolving as a cornerstone for imaging-based strategies in the rapid management of acute ischemic stroke.

CT perfusion analysis by nonlinear regression for predicting hemorrhagic transformation in ischemic stroke

PURPOSE

Intravenous thrombolysis can improve clinical outcome in acute ischemic stroke patients but increases the risk of hemorrhagic transformation (HT). Blood-brain barrier damage, which can be quantified by the vascular permeability for contrast agents, is a potential predictor for HT. This study aimed to assess whether this prediction can be improved by measuring vascular permeability using a novel fast nonlinear regression (NLR) method instead of Patlak analysis.

METHODS

From a prospective ischemic stroke multicenter cohort study, 20 patients with HT on follow-up imaging and 40 patients without HT were selected. The permeability transfer constant K(trans) was measured in three ways; using standard Patlak analysis, Patlak analysis with a fixed offset, and the NLR method. In addition, the permeability-surface (PS) area product and the conventional perfusion parameters (blood volume, flow, and mean transit time) were measured using the NLR method. Relative values were calculated in two ways, i.e., by dividing the average in the infarct core by the average in the contralateral hemisphere, and by dividing the average in the ipsilateral hemisphere by the average in the contralateral hemisphere. Mann-Whitney U tests and receiver operating characteristic (ROC) analyses were performed to assess the discriminative power of each of the relative parameters.

RESULTS

Both the infarct-core and whole-hemisphere averaged relative K(trans) (rK(trans)) values, measured with the NLR method, were significantly higher in the patients who developed HT as compared with those who did not. The rK(trans) measured with standard Patlak analysis was not significantly different. The relative PS (rPS), measured with NLR, had the highest discriminative power (P = 0.002). ROC analysis of rPS showed an area under the curve (AUC) of 0.75 (95% confidence interval: 0.62-0.89) and a sensitivity of 0.75 at a specificity of 0.75. The AUCs of the Patlak rK(trans), the Patlak rK(trans) with fixed offset, and the NLR rK(trans) were 0.58, 0.66, and 0.67, respectively.

CONCLUSIONS

CT perfusion analysis may aid in predicting HT, but standard Patlak analysis did not provide estimates for rK(trans) that were significantly higher in the HT group. The rPS, measured in the infarct core with NLR, had superior discriminative power compared with K(trans) measured with either Patlak analysis with a fixed offset or NLR, and conventional perfusion parameters.

Correction for human head motion in helical x-ray CT

Correction for rigid object motion in helical CT can be achieved by reconstructing from a modified source-detector orbit, determined by the object motion during the scan. This ensures that all projections are consistent, but it does not guarantee that the projections are complete in the sense of being sufficient for exact reconstruction. We have previously shown with phantom measurements that motion-corrected helical CT scans can suffer from data-insufficiency, in particular for severe motions and at high pitch. To study whether such data-insufficiency artefacts could also affect the motion-corrected CT images of patients undergoing head CT scans, we used an optical motion tracking system to record the head movements of 10 healthy volunteers while they executed each of the 4 different types of motion ('no', slight, moderate and severe) for 60 s. From these data we simulated 354 motion-affected CT scans of a voxelized human head phantom and reconstructed them with and without motion correction. For each simulation, motion-corrected (MC) images were compared with the motion-free reference, by visual inspection and with quantitative similarity metrics. Motion correction improved similarity metrics in all simulations. Of the 270 simulations performed with moderate or less motion, only 2 resulted in visible residual artefacts in the MC images. The maximum range of motion in these simulations would encompass that encountered in the vast majority of clinical scans. With severe motion, residual artefacts were observed in about 60% of the simulations. We also evaluated a new method of mapping local data sufficiency based on the degree to which Tuy's condition is locally satisfied, and observed that areas with high Tuy values corresponded to the locations of residual artefacts in the MC images. We conclude that our method can provide accurate and artefact-free MC images with most types of head motion likely to be encountered in CT imaging, provided that the motion can be accurately determined.

Characteristics of Misclassified CT Perfusion Ischemic Core in Patients with Acute Ischemic Stroke

BACKGROUND

CT perfusion (CTP) is used to estimate the extent of ischemic core and penumbra in patients with acute ischemic stroke. CTP reliability, however, is limited. This study aims to identify regions misclassified as ischemic core on CTP, using infarct on follow-up noncontrast CT. We aim to assess differences in volumetric and perfusion characteristics in these regions compared to areas that ended up as infarct on follow-up.

MATERIALS AND METHODS

This study included 35 patients with >100 mm brain coverage CTP. CTP processing was performed using Philips software (IntelliSpace 7.0). Final infarct was automatically segmented on follow-up noncontrast CT and used as reference. CTP and follow-up noncontrast CT image data were registered. This allowed classification of ischemic lesion agreement (core on CTP: rMTT≥145%, aCBV<2.0 ml/100g and infarct on follow-up noncontrast CT) and misclassified ischemic core (core on CTP, not identified on follow-up noncontrast CT) regions. False discovery ratio (FDR), defined as misclassified ischemic core volume divided by total CTP ischemic core volume, was calculated. Absolute and relative CTP parameters (CBV, CBF, and MTT) were calculated for both misclassified CTP ischemic core and ischemic lesion agreement regions and compared using paired rank-sum tests.

RESULTS

Median total CTP ischemic core volume was 49.7ml (IQR:29.9ml-132ml); median misclassified ischemic core volume was 30.4ml (IQR:20.9ml-77.0ml). Median FDR between patients was 62% (IQR:49%-80%). Median relative mean transit time was 243% (IQR:198%-289%) and 342% (IQR:249%-432%) for misclassified and ischemic lesion agreement regions, respectively. Median absolute cerebral blood volume was 1.59 (IQR:1.43-1.79) ml/100g (P<0.01) and 1.38 (IQR:1.15-1.49) ml/100g (P<0.01) for misclassified ischemic core and ischemic lesion agreement, respectively. All CTP parameter values differed significantly.

CONCLUSION

For all patients a considerable region of the CTP ischemic core is misclassified. CTP parameters significantly differed between ischemic lesion agreement and misclassified CTP ischemic core, suggesting that CTP analysis may benefit from revisions.

Effect of extended CT perfusion acquisition time on ischemic core and penumbra volume estimation in patients with acute ischemic stroke due to a large vessel occlusion

BACKGROUND AND PURPOSE

It has been suggested that CT Perfusion acquisition times <60 seconds are too short to capture the complete in and out-wash of contrast in the tissue, resulting in incomplete time attenuation curves. Yet, these short acquisitions times are not uncommon in clinical practice. The purpose of this study was to investigate the occurrence of time attenuation curve truncation in 48 seconds CT Perfusion acquisition and to quantify its effect on ischemic core and penumbra estimation in patients with acute ischemic stroke due to a proximal intracranial arterial occlusion of the anterior circulation.

MATERIALS AND METHODS

We analyzed CT Perfusion data with 48 seconds and extended acquisition times, assuring full time attenuation curves, of 36 patients. Time attenuation curves were classified as complete or truncated. Ischemic core and penumbra volumes resulting from both data sets were compared by median paired differences and interquartile ranges. Controlled experiments were performed using a digital CT Perfusion phantom to investigate the effect of time attenuation curve truncation on ischemic core and penumbra estimation.

RESULTS

In 48 seconds acquisition data, truncation was observed in 24 (67%) cases for the time attenuation curves in the ischemic core, in 2 cases for the arterial input function and in 5 cases for the venous output function. Analysis of extended data resulted in smaller ischemic cores and larger penumbras with a median difference of 13.2 (IQR: 4.3-26.0) ml (P<0.001) and; 12.4 (IQR: 4.1-25.7) ml (P<0.001), respectively. The phantom data showed increasing ischemic core overestimation with increasing tissue time attenuation curve truncation.

CONCLUSIONS

Truncation is common in patients with large vessel occlusion and results in repartitioning of the area of hypoperfusion into larger ischemic core and smaller penumbra estimations. Phantom experiments confirmed that truncation results in overestimation of the ischemic core.

3D movement correction of CT brain perfusion image data of patients with acute ischemic stroke

INTRODUCTION

Head movement during CT brain perfusion (CTP) acquisition can deteriorate the accuracy of CTP analysis. Most CTP software packages can only correct in-plane movement and are limited to small ranges. The purpose of this study is to validate a novel 3D correction method for head movement during CTP acquisition.

METHODS

Thirty-five CTP datasets that were classified as defective due to head movement were included in this study. All CTP time frames were registered with non-contrast CT data using a 3D rigid registration method. Location and appearance of ischemic area in summary maps derived from original and registered CTP datasets were qualitative compared with follow-up non-contrast CT. A quality score (QS) of 0 to 3 was used to express the degree of agreement. Furthermore, experts compared the quality of both summary maps and assigned the improvement score (IS) of the CTP analysis, ranging from -2 (much worse) to 2 (much better).

RESULTS

Summary maps generated from corrected CTP significantly agreed better with appearance of infarct on follow-up CT with mean QS 2.3 versus mean QS 1.8 for summary maps from original CTP (P = 0.024). In comparison to original CTP data, correction resulted in a quality improvement with average IS 0.8: 17 % worsened (IS = -2, -1), 20 % remained unchanged (IS = 0), and 63 % improved (IS = +1, +2).

CONCLUSION

The proposed 3D movement correction improves the summary map quality for CTP datasets with severe head movement.

The effect of head movement on CT perfusion summary maps: simulations with CT hybrid phantom data

Head movement is common during CT brain perfusion (CTP) acquisition of patients with acute ischemic stroke. The effects of this movement on the accuracy of CTP analysis has not been studied previously. The purpose of this study was to quantify the effects of head movement on CTP analysis summary maps using simulated phantom data. A dynamic digital CTP phantom dataset of 25 time frames with a simulated infarct volume was generated. Head movement was simulated by specific translations and rotations of the phantom data. Summary maps from this transformed phantom data were compared to the original data using the volumetric dice similarity coefficient (DSC). DSC for both penumbra and core strongly decreased for rotation angles larger than approximately 1°, 2°, and 7° for, respectively, pitch, roll, and yaw. The accuracy is also sensitive for small translations in the z-direction only. Sudden movements introduced larger errors than gradual movement. These results indicate that CTP summary maps are sensitive to head movement, even for small rotations and translations. CTP scans with head movement larger than the presented values should be interpreted with extra care.