Color-Mapping of 4D-CTA for the Detection of Cranial Arteriovenous Shunts

The novel imaging methods in diagnosis and assessment of cerebrovascular diseases: an overview

Cerebrovascular diseases, including ischemic strokes, hemorrhagic strokes, and vascular malformations, are major causes of morbidity and mortality worldwide. The advancements in neuroimaging techniques have revolutionized the field of cerebrovascular disease diagnosis and assessment. This comprehensive review aims to provide a detailed analysis of the novel imaging methods used in the diagnosis and assessment of cerebrovascular diseases. We discuss the applications of various imaging modalities, such as computed tomography (CT), magnetic resonance imaging (MRI), positron emission tomography (PET), and angiography, highlighting their strengths and limitations. Furthermore, we delve into the emerging imaging techniques, including perfusion imaging, diffusion tensor imaging (DTI), and molecular imaging, exploring their potential contributions to the field. Understanding these novel imaging methods is necessary for accurate diagnosis, effective treatment planning, and monitoring the progression of cerebrovascular diseases.

Diagnostic performance of dynamic 3D magnetic resonance angiography in daily practice for the detection of intracranial arteriovenous shunts in patients with non-traumatic intracranial hemorrhage

Introduction

Identification of treatable causes of intracranial hemorrhage (ICH) such as intracranial arteriovenous shunt is crucial to prevent recurrence. However, diagnostic approaches vary considerably across centers, partly because of limited knowledge of the diagnostic performance of first-line vascular imaging techniques. We assessed the diagnostic performance of dynamic three-dimensional magnetic resonance angiography (dynamic 3D MRA) in daily practice to detect intracranial arteriovenous shunts in ICH patients against subsequent digital subtraction angiography (DSA) as reference standard.

Methods

We reviewed all adult patients who underwent first-line dynamic 3D MRA and subsequent DSA for non-traumatic ICH between January 2016 and September 2021 in a tertiary center. Sensitivity, specificity, accuracy, positive and negative predictive values of dynamic 3D MRA for the detection of intracranial arteriovenous shunt were calculated with DSA as reference standard.

Results

Among 104 included patients, 29 (27.9%) had a DSA-confirmed arteriovenous shunt [19 pial arteriovenous malformations, 10 dural arteriovenous fistulae; median onset-to-DSA: 17 (IQR: 3-88) days]. The sensitivity and specificity of dynamic 3D MRA [median onset-to-dynamic 3D MRA: 14 (3-101) h] for the detection of intracranial arteriovenous shunt were 66% (95% CI: 48-83) and 91% (95% CI: 84-97), respectively. The corresponding accuracy, positive and negative predictive values were 84% (95% CI: 77-91), 73% (95% CI: 56-90), and 87% (95% CI: 80-95), respectively.

Conclusion

This study suggests that although first-line evaluation with dynamic 3D MRA may be helpful for the detection of intracranial arteriovenous shunts in patients with ICH, additional vascular imaging work-up should not be withheld if dynamic 3D MRA is negative. Comparative prospective studies are needed to determine the best imaging strategy to diagnose arteriovenous shunts after non-traumatic ICH.

Validation of SART 3.5D algorithm for cerebrovascular dynamics and artery versus vein classification in presurgical 3D digital subtraction angiographies

Classification of arteries and veins in cerebral angiograms can increase the safety of neurosurgical procedures, such as StereoElectroEncephaloGraphy, and aid the diagnosis of vascular pathologies, as arterovenous malformations. We propose a new method for vessel classification using the contrast medium dynamics in rotational digital subtraction angiography (DSA). After 3D DSA and angiogram segmentation, contrast enhanced projections are processed to suppress soft tissue and bone structures attenuation effect and further enhance the CM flow. For each voxel labelled as vessel, a time intensity curve (TIC) is obtained as a linear combination of temporal basis functions whose weights are addressed by simultaneous algebraic reconstruction technique (SART 3.5D), expanded to include dynamics. Each TIC is classified by comparing the areas under the curve in the arterial and venous phases. Clustering is applied to optimize the classification thresholds. On a dataset of 60 patients, a median value of sensitivity (90%), specificity (91%), and accuracy (92%) were obtained with respect to annotated arterial and venous voxels up to branching order 4–5. Qualitative results are also presented about CM arrival time mapping and its distribution in arteries and veins respectively. In conclusion, this study shows a valuable impact, at no protocol extra-cost or invasiveness, concerning surgical planning related to the enhancement of arteries as major organs at risk. Also, it opens a new scope on the pathophysiology of cerebrovascular dynamics and its anatomical relationships.

Interrater Reliability in the Measurement of Flow Characteristics on Color-Coded Quantitative DSA of Brain AVMs

BACKGROUND AND PURPOSE

Hemodynamic features of brain AVMs may portend increased hemorrhage risk. Previous studies have suggested that MTT is shorter in ruptured AVMs as assessed on quantitative color-coded parametric DSA. This study assesses the interrater reliability of MTT measurements obtained using quantitative color-coded DSA.

MATERIALS AND METHODS

Thirty-five color-coded parametric DSA images of 34 brain AVMs were analyzed by 4 neuroradiologists with experience in interventional neuroradiology. Hemodynamic features assessed included MTT of the AVM and TTP of the dominant feeding artery and draining vein. Agreement among the 4 raters was assessed using the intraclass correlation coefficient.

RESULTS

The interrater reliability among the 4 raters was poor (intraclass correlation coefficient = 0.218; 95% CI, 0.062-0.414; P value = .002) as it related to MTT assessment. When the analysis was limited to cases in which the raters selected the same image to analyze and selected the same primary feeding artery and the same primary draining vein, interrater reliability improved to fair (intraclass correlation coefficient  = 0.564; 95% CI, 0.367-0.717; P < .001).

CONCLUSIONS

Interrater reliability in deriving color-coded parametric DSA measurements such as MTT is poor so minor differences among raters may result in a large variance in MTT and TTP results, partly due to the sensitivity and 2D nature of the technique. Reliability can be improved by defining a standard projection, feeding artery, and draining vein for analysis.