Intravascular functional maps of common neurovascular lesions derived from volumetric 4D CT data

Computational methods for visualizing and measuring verapamil efficacy for cerebral vasospasm

Cerebral vasospasm is a dreaded sequelae of aneurysmal subarachnoid hemorrhage (aSAH), requiring timely intervention with therapeutic goals of improving brain perfusion. There are currently no standardized real-time, objective assessments of the interventional procedures performed to treat vasospasm. Here we describe real-time techniques to quantify cerebral perfusion during interventional cerebral angiography. We retrospectively analyzed 39 consecutive cases performed to treat clinical vasospasm and quantified the changes in perfusion metrics between pre- and post- verapamil administrations. With Digital Subtraction Angiography (DSA) perfusion analysis, we are able to identify hypoperfused territories and quantify the exact changes in cerebral perfusion for each individual case and vascular territory. We demonstrate that perfusion analysis for DSA can be performed in real time. This provides clinicians with a colorized map which directly visualizes hypoperfused tissue, combined with associated perfusion statistics. Quantitative thresholds and analysis based on DSA perfusion may assist with real-time dosage estimation and help predict response to treatment, however future prospective analysis is required for validation.

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

BACKGROUND AND PURPOSE

4D CT angiography is increasingly used in clinical practice for the assessment of different neurovascular disorders. Optimized processing of 4D-CTA is crucial for diagnostic interpretation because of the large amount of data that is generated. A color-mapping method for 4D-CTA is presented for improved and enhanced visualization of the cerebral vasculature hemodynamics. This method was applied to detect cranial AVFs.

MATERIALS AND METHODS

All patients who underwent both 4D-CTA and DSA in our hospital from 2011 to 2018 for the clinical suspicion of a cranial AVF or carotid cavernous fistula were retrospectively collected. Temporal information in the cerebral vasculature was visualized using a patient-specific color scale. All color-maps were evaluated by 3 observers for the presence or absence of an AVF or carotid cavernous fistula. The presence or absence of cortical venous reflux was evaluated as a secondary outcome measure.

RESULTS

In total, 31 patients were included, 21 patients with and 10 without an AVF. Arterialization of venous structures in AVFs was accurately visualized using color-mapping. There was high sensitivity (86%-100%) and moderate-to-high specificity (70%-100%) for the detection of AVFs on color-mapping 4D-CTA, even without the availability of dynamic subtraction rendering. The diagnostic performance of the 3 observers in the detection of cortical venous reflux was variable (sensitivity, 43%-88%; specificity, 60%-80%).

CONCLUSIONS

Arterialization of venous structures can be visualized using color-mapping of 4D-CTA and proves to be accurate for the detection of cranial AVFs. This finding makes color-mapping a promising visualization technique for assessing temporal hemodynamics in 4D-CTA.

The analysis of the cerebral venous blood volume in cavernous sinus using 320 row multi-detector CT

OBJECTIVES

Functional venous anatomy in the brain has been mostly understood from the morphological and embryological points of view and no published study has directly evaluated the blood flow volume of cerebral small veins. We developed a method to directly evaluate the relative blood volume in small venous channels using multi-detector computed tomography (CT) and applied it to evaluate the blood volume in each tributary of the cavernous sinus, which plays an important role in cerebral venous drainage.

PATIENTS AND METHODS

Ten patients with small brain tumors who had normal venous anatomy were included in the present study. All of them underwent preoperative 320-row multi-detector CT. After injecting the contrast bolus, we measured the Hounsfield units (HUs) at 10 time point over 60 s in each tributary of the cavernous sinus. The gamma distribution fitting to each HU enabled us to obtain a time-density curve and determine the relative venous volume in each venous channel.

RESULTS

In terms of blood volume, the superficial middle cerebral vein and inferior petrosal sinus were the largest inflow and outflow channels of the cavernous sinus and accounted for 36.1% and 24.7% of its inflow and outflow on average, respectively. The superior orbital vein did not contribute to the blood volume passing through the cavernous sinus in the current study.

CONCLUSIONS

The present study allowed us to determine the relative blood volume in each tributary of the cavernous sinus, which was very useful to understand the physiological actual venous drainage pattern concerning the cavernous sinus in normal anatomy.

Classification of arterial and venous cerebral vasculature based on wavelet postprocessing of CT perfusion data

PURPOSE

The purpose of this study was to propose and evaluate a new wavelet-based technique for classification of arterial and venous vessels using time-resolved cerebral CT perfusion data sets.

METHODS

Fourteen consecutive patients (mean age 73 yr, range 17-97) with suspected stroke but no pathology in follow-up MRI were included. A CT perfusion scan with 32 dynamic phases was performed during intravenous bolus contrast-agent application. After rigid-body motion correction, a Paul wavelet (order 1) was used to calculate voxelwise the wavelet power spectrum (WPS) of each attenuation-time course. The angiographic intensity A was defined as the maximum of the WPS, located at the coordinates T (time axis) and W (scale/width axis) within the WPS. Using these three parameters (A, T, W) separately as well as combined by (1) Fisher's linear discriminant analysis (FLDA), (2) logistic regression (LogR) analysis, or (3) support vector machine (SVM) analysis, their potential to classify 18 different arterial and venous vessel segments per subject was evaluated.

RESULTS

The best vessel classification was obtained using all three parameters A and T and W [area under the curve (AUC): 0.953 with FLDA and 0.957 with LogR or SVM]. In direct comparison, the wavelet-derived parameters provided performance at least equal to conventional attenuation-time-course parameters. The maximum AUC obtained from the proposed wavelet parameters was slightly (although not statistically significantly) higher than the maximum AUC (0.945) obtained from the conventional parameters.

CONCLUSIONS

A new method to classify arterial and venous cerebral vessels with high statistical accuracy was introduced based on the time-domain wavelet transform of dynamic CT perfusion data in combination with linear or nonlinear multidimensional classification techniques.

Optimizing analysis, visualization, and navigation of large image data sets: one 5000-section CT scan can ruin your whole day

The technology revolution in image acquisition, instrumentation, and methods has resulted in vast data sets that far outstrip the human observers' ability to view, digest, and interpret modern medical images by using traditional methods. This may require a paradigm shift in the radiologic interpretation process. As human observers, radiologists must search for, detect, and interpret targets. Potential interventions should be based on an understanding of human perceptual and attentional abilities and limitations. New technologies and tools already in use in other fields can be adapted to the health care environment to improve medical image analysis, visualization, and navigation through large data sets. This historical psychophysical and technical review touches on a broad range of disciplines but focuses mainly on the analysis, visualization, and navigation of image data performed during the interpretive process. Advanced postprocessing, including three-dimensional image display, multimodality image fusion, quantitative measures, and incorporation of innovative human-machine interfaces, will likely be the future. Successful new paradigms will integrate image and nonimage data, incorporate workflow considerations, and be informed by evidence-based practices. This overview is meant to heighten the awareness of the complexities and limitations of how radiologists interact with images, particularly the large image sets generated today. Also addressed is how human-machine interface and informatics technologies could combine to transform the interpretation process in the future to achieve safer and better quality care for patients and a more efficient and effective work environment for radiologists.

SUPPLEMENTAL MATERIAL

http://radiology.rsna.org/lookup/suppl/doi:10.1148/radiol.11091276/-/DC1.

Noise reduction and image quality improvement of low dose and ultra low dose brain perfusion CT by HYPR-LR processing

PURPOSE

To evaluate image quality and signal characteristics of brain perfusion CT (BPCT) obtained by low-dose (LD) and ultra-low-dose (ULD) protocols with and without post-processing by highly constrained back-projection (HYPR)-local reconstruction (LR) technique.

METHODS AND MATERIALS

Simultaneous BPCTs were acquired in 8 patients on a dual-source-CT by applying LD (80 kV, 200 mAs, 14×1.2 mm) on tube A and ULD (80 kV, 30 mAs, 14×1.2 mm) on tube B. Image data from both tubes was reconstructed with identical parameters and post-processed using the HYPR-LR. Correlation coefficients between mean and maximum (MAX) attenuation values within corresponding ROIs, area under attenuation curve (AUC), and signal to noise ratio (SNR) of brain parenchyma were assessed. Subjective image quality was assessed on a 5-point scale by two blinded observers (1: excellent, 5: non-diagnostic).

RESULTS

Radiation dose of ULD was more than six times lower compared to LD. SNR was improved by HYPR: ULD vs. ULD+HYPR: 1.9±0.3 vs. 8.4±1.7, LD vs. LD+HYPR: 5.0±0.7 vs. 13.4±2.4 (both p<0.0001). There was a good correlation between the original datasets and the HYPR-LR post-processed datasets: r = 0.848 for ULD and ULD+HYPR and r = 0.933 for LD and LD+HYPR (p<0.0001 for both). The mean values of the HYPR-LR post-processed ULD dataset correlated better with the standard LD dataset (r = 0.672) than unprocessed ULD (r = 0.542), but both correlations were significant (p<0.0001). There was no significant difference in AUC or MAX. Image quality was rated excellent (1.3) in LD+HYPR and non-diagnostic (5.0) in ULD. LD and ULD+HYPR images had moderate image quality (3.3 and 2.7).

CONCLUSION

SNR and image quality of ULD-BPCT can be improved to a level similar to LD-BPCT when using HYPR-LR without distorting attenuation measurements. This can be used to substantially reduce radiation dose. Alternatively, LD images can be improved by HYPR-LR to higher diagnostic quality.