Novel flat-panel cone-beam CT compared to multi-detector CT for assessment of acute ischemic stroke: A prospective study

Current State of Evidence for Neuroimaging Paradigms in Management of Acute Ischemic Stroke

Stroke is the chief differential diagnosis in patient presenting to the emergency room with abrupt onset focal neurological deficits. Neuroimaging, including non-contrast computed tomography (CT), magnetic resonance imaging (MRI), vascular and perfusion imaging, is a cornerstone in the diagnosis and treatment decision-making. This review examines the current state of evidence behind the different imaging paradigms for acute ischemic stroke diagnosis and treatment, including current recommendations from the guidelines. Non-contrast CT brain, or in some centers MRI, can help differentiate ischemic stroke and intracerebral hemorrhage (ICH), a pivotal juncture in stroke diagnosis and treatment algorithm, especially for early window thrombolytics. Advanced imaging such as MRI or perfusion imaging can also assist making a diagnosis of ischemic stroke versus mimics such as migraine, Todd's paresis, or functional disorders. Identification of medium-large vessel occlusions with CT or MR angiography triggers consideration of endovascular thrombectomy (EVT), with additional perfusion imaging help identify salvageable brain tissue in patients who are likely to benefit from reperfusion therapies, particularly in the ≥6 h window. We also review recent advances in neuroimaging and ongoing trials in key therapeutic areas and their imaging selection criteria to inform the readers on potential future transitions into use of neuroimaging for stroke diagnosis and treatment decision making. ANN NEUROL 2024;95:1017-1034.

Motion artifact correction for cone beam CT stroke imaging: a prospective series

BACKGROUND

Imaging assessment for acute ischemic stroke (AIS) patients in the angiosuite using cone beam CT (CBCT) has created increased interest since endovascular treatment became the first line therapy for proximal vessel occlusions. One of the main challenges of CBCT imaging in AIS patients is degraded image quality due to motion artifacts. This study aims to evaluate the prevalence of motion artifacts in CBCT stroke imaging and the effectiveness of a novel motion artifact correction algorithm for image quality improvement.

METHODS

Patients presenting with acute stroke symptoms and considered for endovascular treatment were included in the study. CBCT scans were performed using the angiosuite X-ray system. All CBCT scans were post-processed using a motion artifact correction algorithm. Motion artifacts were scored before and after processing using a 4-point scale.

RESULTS

We prospectively included 310 CBCT scans from acute stroke patients. 51% (n=159/310) of scans had motion artifacts, with 24% being moderate to severe. The post-processing algorithm improved motion artifacts in 91% of scans with motion (n=144/159), restoring clinical diagnostic capability in 34%. Overall, 76% of the scans were sufficient for clinical decision-making before correction, which improved to 93% (n=289/310) after post-processing with our algorithm.

CONCLUSIONS

Our results demonstrate that CBCT motion artifacts are significantly reduced using a novel post-processing algorithm, which improved brain CBCT image quality and diagnostic assessment for stroke. This is an important step on the road towards a direct-to-angio approach for endovascular thrombectomy (EVT) treatment.

An unsupervised dual contrastive learning framework for scatter correction in cone-beam CT image

PURPOSE

Cone-beam computed tomography (CBCT) is widely utilized in modern radiotherapy; however, CBCT images exhibit increased scatter artifacts compared to planning CT (pCT), compromising image quality and limiting further applications. Scatter correction is thus crucial for improving CBCT image quality.

METHODS

In this study, we proposed an unsupervised contrastive learning method for CBCT scatter correction. Initially, we transformed low-quality CBCT into high-quality synthetic pCT (spCT) and generated forward projections of CBCT and spCT. By computing the difference between these projections, we obtained a residual image containing image details and scatter artifacts. Image details primarily comprise high-frequency signals, while scatter artifacts consist mainly of low-frequency signals. We extracted the scatter projection signal by applying a low-pass filter to remove image details. The corrected CBCT (cCBCT) projection signal was obtained by subtracting the scatter artifacts projection signal from the original CBCT projection. Finally, we employed the FDK reconstruction algorithm to generate the cCBCT image.

RESULTS

To evaluate cCBCT image quality, we aligned the CBCT and pCT of six patients. In comparison to CBCT, cCBCT maintains anatomical consistency and significantly enhances CT number, spatial homogeneity, and artifact suppression. The mean absolute error (MAE) of the test data decreased from 88.0623 ± 26.6700 HU to 17.5086 ± 3.1785 HU. The MAE of fat regions of interest (ROIs) declined from 370.2980 ± 64.9730 HU to 8.5149 ± 1.8265 HU, and the error between their maximum and minimum CT numbers decreased from 572.7528 HU to 132.4648 HU. The MAE of muscle ROIs reduced from 354.7689 ± 25.0139 HU to 16.4475 ± 3.6812 HU. We also compared our proposed method with several conventional unsupervised synthetic image generation techniques, demonstrating superior performance.

CONCLUSIONS

Our approach effectively enhances CBCT image quality and shows promising potential for future clinical adoption.

Image quality improvements for brain soft tissue in neuro-endovascular treatments: A novel dual-axis "butterfly" trajectory for optimized Cone-Beam CT

PURPOSE

Cone-beam computed tomography (CBCT) is useful in the diagnosis of complications after neuro-endovascular treatment. However, the image quality of conventional CBCT is inferior to that of conventional CT. To solve this problem, a dual-axis butterfly CBCT available with an angiography suite has been developed. This study aimed to evaluate the image quality of this dual-axis butterfly CBCT compared to the conventional CBCT in the same patient.

METHOD

We prospectively included patients who underwent scheduled neuro-endovascular treatment and performed conventional CBCT and novel dual-axis butterfly CBCT as a postoperative examination. We evaluated artifacts, brain contrast, and cortico-medullary junctions on a scoring system using a 5-point scale in which lower scores indicate better image quality. In addition, the white matter/gray matter ratio was calculated in selected brain lobe regions.

RESULTS

Forty-seven cases (94 paired images) were enrolled. The novel dual-axis butterfly CBCT had significantly fewer supratentorial and infratentorial artifacts in the artifact evaluation. Similarly, contrast and cortico-medullary junction discrimination in the cerebral hemispheres scored significantly better in the butterfly scan in all regions. The white matter/gray matter ROI ratio was significantly higher in the novel dual-axis butterfly CBCT in the frontal and occipital lobes but not in the temporal lobe.

CONCLUSIONS

Compared to conventional CBCT, the novel dual-axis butterfly CBCT showed supratentorial and infratentorial artifact reduction as well as improved contrast with the brain parenchyma and cerebrospinal fluid space and white matter/gray matter discrimination ability.

Can machine learning of post-procedural cone-beam CT images in acute ischemic stroke improve the detection of 24-h hemorrhagic transformation? A preliminary study

PURPOSE

Hemorrhagic transformation (HT) is an independent predictor of unfavorable outcome in acute ischemic stroke (AIS) patients undergoing endovascular thrombectomy (EVT). Its early identification could help tailor AIS management. We hypothesize that machine learning (ML) applied to cone-beam computed tomography (CB-CT), immediately after EVT, improves performance in 24-h HT prediction.

METHODS

We prospectively enrolled AIS patients undergoing EVT, post-procedural CB-CT, and 24-h non-contrast CT (NCCT). Three raters independently analyzed imaging at four anatomic levels qualitatively and quantitatively selecting a region of interest (ROI) < 5 mm². Each ROI was labeled as "hemorrhagic" or "non-hemorrhagic" depending on 24-h NCCT. For each level of CB-CT, Mean Hounsfield Unit (HU), minimum HU, maximum HU, and signal- and contrast-to-noise ratios were calculated, and the differential HU-ROI value was compared between both hemispheres. The number of anatomic levels affected was computed for lesion volume estimation. ML with the best validation performance for 24-h HT prediction was selected.

RESULTS

One hundred seventy-two ROIs from affected hemispheres of 43 patients were extracted. Ninety-two ROIs were classified as unremarkable, whereas 5 as parenchymal contrast staining, 29 as ischemia, 7 as subarachnoid hemorrhages, and 39 as HT. The Bernoulli Naïve Bayes was the best ML classifier with a good performance for 24-h HT prediction (sensitivity = 1.00; specificity = 0.75; accuracy = 0.82), though precision was 0.60.

CONCLUSION

ML demonstrates high-sensitivity but low-accuracy 24-h HT prediction in AIS. The automated CB-CT imaging evaluation resizes sensitivity, specificity, and accuracy rates of visual interpretation reported in the literature so far. A standardized quantitative interpretation of CB-CT may be warranted to overcome the inter-operator variability.

The butterfly effect: improving brain cone-beam CT image artifacts for stroke assessment using a novel dual-axis trajectory

BACKGROUND

Cone-beam computed tomography (CBCT) imaging of the brain can be performed in the angiography suite to support various neurovascular procedures. Relying on CBCT brain imaging solely, however, still lacks full diagnostic confidence due to the inferior image quality compared with CT and various imaging artifacts that persist even with modern CBCT.

OBJECTIVE

To perform a detailed evaluation of image artifact improvement using a new CBCT protocol which implements a novel dual-axis 'butterfly' trajectory.

METHODS

Our study included 94 scans from 47 patients who received CBCT imaging for assessment of either ischemia or hemorrhage during a neurovascular procedure. Both a traditional uni-axis 'circular' and novel dual-axis 'butterfly' protocol were performed on each patient (same-patient control). Each brain scan was divided into six regions and scored out of 3 based on six artifacts originating from various physics-based and patient-based sources.

RESULTS

The dual-axis trajectory produces CBCT images with significantly fewer image artifacts than the traditional circular scan (whole brain average artifact score, AS: 0.20 vs 0.33), with the greatest improvement in bone beam hardening (AS: 0.13 vs 0.78) and cone-beam artifacts (AS: 0.04 vs 0.55).

CONCLUSIONS

Recent developments in CBCT imaging protocols have significantly improved image artifacts, which has improved diagnostic confidence for stroke and supports a direct-to-angiography suite transfer approach for patients with acute ischemic stroke.

XperCT facilitates sharp recanalization for the treatment of chronic thoracic venous occlusive disease in hemodialysis patients

OBJECTIVE

Our objective was to evaluate the feasibility of XperCT combined fluoroscopy to guide sharp recanalization for the treatment of chronic thoracic venous occlusive disease in hemodialysis patients.

METHODS

The records of hemodialysis patients with chronic thoracic venous occlusive disease who received endovascular sharp recanalization after conventional techniques failed were retrospectively reviewed. The sharp devices used for recanalization included the stiff end of a guidewire, Chiba biopsy needle, RUPS-100 set, and transseptal needle. The needle was advanced toward a target placed at the opposite end of the occlusion and was guided by fluoroscopy and/or XperCT. While the guidewire crossed the occlusion, endovascular procedures such as percutaneous angioplasty were performed for the treatment of the occlusion.

RESULTS

The analysis included 32 sharp thoracic vein recanalization procedures in 29 patients. Two attempts in one patient failed, and in one patient the first attempt failed but the second attempt was successful. In one patient, two separate successful procedures were performed, and the other 26 procedures in 26 patients were successful. The overall technical success rate of sharp recanalization was 90%. The mean number of puncture attempts in the combined group was less than that of the fluoroscopy-guided alone group (2 vs 5, p < 0.05). The success rate of sharp recanalization in the combined group was higher (100% vs 86%), and the recanalization time (28.5 min vs 36 min, p > 0.05) was no different. There was no statistical difference in procedure-related complications between the groups.

CONCLUSION

XperCT can facilitate sharp recanalization for the treatment of chronic thoracic venous occlusive disease in hemodialysis patients.

Advances in cerebral perfusion imaging techniques in acute ischemic stroke

The application of cerebral perfusion imaging has demonstrated significant assessment benefits and an ability to establish an appropriate triage of patients with acute ischemic stroke (AIS) and large artery occlusion (LAO) in the extended time window. Computed tomography perfusion (CTP) and magnetic resonance imaging (MRI) are routinely used to determine the ischemic core, as well as the tissue at risk, to aid in therapeutic decision-making. However, the time required to transport patients to imaging extends the door-to-reperfusion time. C-arm cone-beam CT (CBCT) is a novel tomography technology that combines 2D radiography and 3D CT imaging based on the digital subtraction angiography platform. In comparison with CT or MRI perfusion techniques, CBCT combined with catheterized angiogram or therapy can serve as a "one-stop-shop" for the diagnosis and treatment of AIS, and greatly reduce the door to reperfusion time. Here, we review the current evidence on the efficacy and theoretical basis of CBCT, as well as other perfusion techniques, with the purpose to assist clinicians to establish an effective and repaid workflow for patients with AIS and LAO in clinical practice.

Diagnostic performance of intraoperative cone beam computed tomography compared with postoperative magnetic resonance imaging for detecting hemorrhagic transformation after endovascular treatment following large vessel occlusion

OBJECTIVES

Early detection of hemorrhagic transformation (HT) in patients with large vessel occlusion (LVO) after endovascular treatment is important for postoperative patient management. We investigated the diagnostic performance of intraoperative cone beam computed tomography (CBCT) with reference standard magnetic resonance imaging (MRI) for detecting HT.

MATERIALS AND METHODS

Consecutive patients with LVO treated by endovascular treatment who underwent intraoperative CBCT and postoperative MRI were included. Two observers evaluated all images for the presence of HT. Sensitivity and specificity for detecting HT were calculated with MRI as reference standard. The observers classified HT according to the European Cooperative Acute Stroke Study (ECASS). Inter-method and inter-rater agreement for the detection of HT and for the ECASS classification were assessed using kappa or weighted Brennan-Prediger (wBP) statistics.

RESULTS

Images of 106 procedures (94 for anterior circulation) were analyzed. The sensitivity and specificity for detecting HT on CBCT were 0.77 and 0.83, respectively, for all procedures and 0.83 and 0.8, respectively, for anterior circulation. The inter-method agreement for HT detection (κ = 0.63 overall, κ = 0.69 anterior circulation) and ECASS classification (wBP = 0.67 overall, wBP = 0.77 anterior circulation) were substantial. The inter-rater agreement for HT detection (κ = 0.87 overall, κ = 0.85 anterior circulation) and for ECASS classification (wBP = 0.95 overall, wBP = 0.92 anterior circulation) were almost perfect.

CONCLUSIONS

The diagnostic performance of CBCT for the detection of HT in stroke patients treated for LVO was acceptable with excellent inter-rater agreement. Intraoperative CBCT may be useful to trigger early interventions if HT is detected, although detailed classifications of HT may be difficult.

Flat Detector CT with Cerebral Pooled Blood Volume Perfusion in the Angiography Suite: From Diagnostics to Treatment Monitoring

C-arm flat-panel detector computed tomographic (CT) imaging in the angiography suite increasingly plays an important part during interventional neuroradiological procedures. In addition to conventional angiographic imaging of blood vessels, flat detector CT (FD CT) imaging allows simultaneous 3D visualization of parenchymal and vascular structures of the brain. Next to imaging of anatomical structures, it is also possible to perform FD CT perfusion imaging of the brain by means of cerebral blood volume (CBV) or pooled blood volume (PBV) mapping during steady state contrast administration. This enables more adequate decision making during interventional neuroradiological procedures, based on real-time insights into brain perfusion on the spot, obviating time consuming and often difficult transportation of the (anesthetized) patient to conventional cross-sectional imaging modalities. In this paper we review the literature about the nature of FD CT PBV mapping in patients and demonstrate its current use for diagnosis and treatment monitoring in interventional neuroradiology.

Principles, techniques and applications of high resolution cone beam CT angiography in the neuroangio suite

The aim of this review is to describe the acquisition and reformatting of state of the art high resolution cone beam CT (HR-CBCT) and demonstrate its role in multiple neurovascular conditions as a tool to improve the understanding of disease and guide therapeutic decisions. First, we will review the basic principle of CBCT acquisition, followed by the injection protocols and the reformatting paradigms. Next, multiple applications in different pathological conditions such as aneurysms, arteriovenous malformations, dural arteriovenous fistulas, and stroke will be described. HR-CBCT angiography, widely available, is uniquely useful in certain clinical scenarios to improve the understanding of disease and guide therapeutic decisions. It rapidly is becoming an essential tool for the contemporary neurointerventionalist.AChoAho.

Reference-free learning-based similarity metric for motion compensation in cone-beam CT

Purpose. Patient motion artifacts present a prevalent challenge to image quality in interventional cone-beam CT (CBCT). We propose a novel reference-free similarity metric (DL-VIF) that leverages the capability of deep convolutional neural networks (CNN) to learn features associated with motion artifacts within realistic anatomical features. DL-VIF aims to address shortcomings of conventional metrics of motion-induced image quality degradation that favor characteristics associated with motion-free images, such as sharpness or piecewise constancy, but lack any awareness of the underlying anatomy, potentially promoting images depicting unrealistic image content. DL-VIF was integrated in an autofocus motion compensation framework to test its performance for motion estimation in interventional CBCT.Methods. DL-VIF is a reference-free surrogate for the previously reported visual image fidelity (VIF) metric, computed against a motion-free reference, generated using a CNN trained using simulated motion-corrupted and motion-free CBCT data. Relatively shallow (2-ResBlock) and deep (3-Resblock) CNN architectures were trained and tested to assess sensitivity to motion artifacts and generalizability to unseen anatomy and motion patterns. DL-VIF was integrated into an autofocus framework for rigid motion compensation in head/brain CBCT and assessed in simulation and cadaver studies in comparison to a conventional gradient entropy metric.Results. The 2-ResBlock architecture better reflected motion severity and extrapolated to unseen data, whereas 3-ResBlock was found more susceptible to overfitting, limiting its generalizability to unseen scenarios. DL-VIF outperformed gradient entropy in simulation studies yielding average multi-resolution structural similarity index (SSIM) improvement over uncompensated image of 0.068 and 0.034, respectively, referenced to motion-free images. DL-VIF was also more robust in motion compensation, evidenced by reduced variance in SSIM for various motion patterns (σ_DL-VIF = 0.008 versusσ_{gradient entropy} = 0.019). Similarly, in cadaver studies, DL-VIF demonstrated superior motion compensation compared to gradient entropy (an average SSIM improvement of 0.043 (5%) versus little improvement and even degradation in SSIM, respectively) and visually improved image quality even in severely motion-corrupted images.Conclusion: The studies demonstrated the feasibility of building reference-free similarity metrics for quantification of motion-induced image quality degradation and distortion of anatomical structures in CBCT. DL-VIF provides a reliable surrogate for motion severity, penalizes unrealistic distortions, and presents a valuable new objective function for autofocus motion compensation in CBCT.

Performance characterization of a prototype dual-layer cone-beam computed tomography system

PURPOSE

Conventional cone-beam computed tomography CT (CBCT) provides limited discrimination between low-contrast tissues. Furthermore, it is limited to full-spectrum energy integration. A dual-energy CBCT system could be used to separate photon energy spectra with the potential to increase the visibility of clinically relevant features and acquire additional information relevant in a multitude of clinical imaging applications. In this work, the performance of a novel dual-layer dual-energy CBCT (DL-DE-CBCT) C-arm system is characterized for the first time.

METHODS

A prototype dual-layer detector was fitted into a commercial interventional C-arm CBCT system to enable DL-DE-CBCT acquisitions. DL-DE reconstructions were derived from material-decomposed Compton scatter and photoelectric base functions. The modulation transfer function (MTF) of the prototype DL-DE-CBCT was compared to that of a commercial CBCT. Noise and uniformity characteristics were evaluated using a cylindrical water phantom. Effective atomic numbers and electron densities were estimated in clinically relevant tissue substitutes. Iodine quantification was performed (for 0.5-15 mg/ml concentrations) and virtual noncontrast (VNC) images were evaluated. Finally, contrast-to-noise ratios (CNR) and CT number accuracies were estimated.

RESULTS

The prototype and commercial CBCT showed similar spatial resolution, with a mean 10% MTF of 5.98 cycles/cm and 6.28 cycles/cm, respectively, using a commercial standard reconstruction. The lowest noise was seen in the 80 keV virtual monoenergetic images (VMI) (7.40 HU) and the most uniform images were seen at VMI 60 keV (4.74 HU) or VMI 80 keV (1.98 HU), depending on the uniformity measure used. For all the tissue substitutes measured, the mean accuracy in effective atomic number was 98.2% (SD 1.2%) and the mean accuracy in electron density was 100.3% (SD 0.9%). Iodine quantification images showed a mean difference of -0.1 (SD 0.5) mg/ml compared to the true iodine concentration for all blood and iodine-containing objects. For VNC images, all blood substitutes containing iodine averaged a CT number of 43.2 HU, whereas a blood-only substitute measured 44.8 HU. All water-containing iodine substitutes measured a mean CT number of 2.6 in the VNC images. A noise-suppressed dataset showed a CNR peak at VMI 40 keV and low at VMI 120 keV. In the same dataset without noise suppression applied, a peak in CNR was obtained at VMI 70 keV and a low at VMI 120 keV. The estimated CT numbers of various clinically relevant objects were generally very close to the calculated CT number.

CONCLUSIONS

The performance of a prototype dual-layer dual-energy C-arm CBCT system was characterized. Spatial resolution and noise were comparable with a commercially available C-arm CBCT system, while offering dual-energy capability. Iodine quantifications, effective atomic numbers, and electron densities were in good agreement with expected values, indicating that the system can be used to reliably evaluate the material composition of clinically relevant tissues. The VNC and monoenergetic images indicate a consistent ability to separate clinically relevant tissues. The results presented indicate that the system could find utility in diagnostic, interventional, and radiotherapy planning settings.