Variation in aorta attenuation in contrast-enhanced CT and its implications for calcification thresholds

Customization of Computed Tomography Radio-Opacity in 3D-Printed Contrast-Injectable Tumor Phantoms

Medical Imaging Phantoms (MIPs) calibrate imaging devices, train medical professionals, and can help procedural planning. Traditional MIPs are costly and limited in customization. Additive manufacturing allows for customizable, patient-specific phantoms. This study examines the CT attenuation characteristics of contrast-injectable, chambered 3D-printed phantoms to optimize tissue-mimicking capabilities. A MIP was constructed from a CT of a complex pelvic tumor near the iliac bifurcation. A 3D reconstruction of these structures composed of three chambers (aorta, inferior vena cava, tumor) with ports for contrast injection was 3D printed. Desired attenuations were 200 HU (arterial I), 150 HU (venous I), 40 HU (tumor I), 150 HU (arterial II), 90 HU (venous II), and 400 HU (tumor II). Solutions of Optiray 350 and water were injected, and the phantom was scanned on CT. Attenuations were measured using ROIs. Mean attenuation for the six phases was as follows: 37.49 HU for tumor I, 200.50 HU for venous I, 227.92 HU for arterial I, 326.20 HU for tumor II, 91.32 HU for venous II, and 132.08 HU for arterial II. Although the percent differences between observed and goal attenuation were high, the observed relative HU differences between phases were similar to goal HU differences. The observed attenuations reflected the relative concentrations of contrast solutions used, exhibiting a strong positive correlation with contrast concentration. The contrast-injectable tumor phantom exhibited a useful physiologic range of attenuation values, enabling the modification of tissue-mimicking 3D-printed phantoms even after the manufacturing process.

Independent Associations of Aortic Calcification with Cirrhosis and Liver Related Mortality in Veterans with Chronic Liver Disease

INTRODUCTION

Abdominal aortic calcifications (AAC) are incidentally found on medical imaging and useful cardiovascular burden approximations. The Morphomic Aortic Calcification Score (MAC) leverages automated deep learning methods to quantify and score AACs. While associations of AAC and non-alcoholic fatty liver disease (NAFLD) have been described, relationships of AAC with other liver diseases and clinical outcome are sparse. This study's purpose was to evaluate AAC and liver-related death in a cohort of Veterans with chronic liver disease (CLD).

METHODS

We utilized the VISN 10 CLD cohort, a regional cohort of Veterans with the three forms of CLD: NAFLD, hepatitis C (HCV), alcohol-associated (ETOH), seen between 2008 and 2014, with abdominal CT scans (n = 3604). Associations between MAC and cirrhosis development, liver decompensation, liver-related death, and overall death were evaluated with Cox proportional hazard models.

RESULTS

The full cohort demonstrated strong associations of MAC and cirrhosis after adjustment: HR 2.13 (95% CI 1.63, 2.78), decompensation HR 2.19 (95% CI 1.60, 3.02), liver-related death HR 2.13 (95% CI 1.46, 3.11), and overall death HR 1.47 (95% CI 1.27, 1.71). These associations seemed to be driven by the non-NAFLD groups for decompensation and liver-related death [HR 2.80 (95% CI 1.52, 5.17; HR 2.34 (95% CI 1.14, 4.83), respectively].

DISCUSSION

MAC was strongly and independently associated with cirrhosis, liver decompensation, liver-related death, and overall death. Surprisingly, stratification results demonstrated comparable or stronger associations among those with non-NAFLD etiology. These findings suggest abdominal aortic calcification may predict liver disease severity and clinical outcomes in patients with CLD.

Quantifying the calcification of abdominal aorta and major side branches with deep learning

AIM

To explore the possibility of a neural network-based method for quantifying calcifications of the abdominal aorta and its branches.

MATERIALS AND METHODS

In total, 58 computed tomography (CT) angiography volumes were selected from a dataset of 609 to represent different stages of sclerosis. The ground truth segmentations of the abdominal aorta, coeliac trunk, superior mesenteric artery, renal arteries, common iliac arteries, and their calcifications were delineated manually. Two V-Net ensemble models were trained, one for segmenting arteries of interest and another for calcifications. The branches of interest were shortened algorithmically. The volumes of calcification were then evaluated from the arteries of interest.

RESULTS

The results indicate that automatic detection is possible with a high correlation to the ground truth. The scores for the ensemble calcification model were dice score of 0.69 and volumetric similarity (VS) of 0.80 and for the arteries of interest segmentations: aorta: dice 0.96, VS 0.98; aortic branches: dice 0.74, VS 0.87; and common iliac arteries: dice 0.72, VS 0.91.

CONCLUSIONS

The presented neural network model is the first to be capable of automatically segmenting, in addition to calcification, both the aorta and its branches from contrast-enhanced CT angiography. This technology shows promise in addressing limitations inherent in earlier methods that relied solely on plain CT.

Reference distributions of aortic calcification and association with Framingham risk score

Evidence supporting aortic calcification as a leverageable cardiovascular risk factor is rapidly growing. Given aortic calcification's potential as a clinical correlate, we assessed granular vertebral-indexed calcification measurements of the abdominal aorta in a well curated reference population. We evaluated the relationship of aortic calcification measurements with Framingham risk scores. After exclusion, 4073 participants from the Reference Analytic Morphomic Population with varying vertebral levels were included. The percent of the aortic wall calcified was used to assess calcification burden at the L1-L4 levels. Descriptive statistics of participants, sex-specific vertebral indexed calcification measurements, relational plots, and relevant associations are reported. Mean aortic attenuation was higher in female than male participants. Overall, mean aortic calcium was higher with reference to inferior abdominal aortic measurements and demonstrated significant differences across all abdominal levels [L3 Area (mm[Formula: see text]): Females 6.34 (sd 16.60), Males 6.23 (sd 17.21); L3 Volume (mm[Formula: see text]): Females 178.90 (sd 474.19), Males 195.80 (sd 547.36); Wall Calcification (%): Females (L4) 6.97 (sd 16.03), Males (L3) 5.46 (13.80)]. Participants with elevated calcification had significantly higher Framingham risk scores compared to participants with normal calcification scores. Opportunistically measuring aortic calcification may inform further cardiovascular risk assessment and enhance cardiovascular event surveillance efforts.

Validation of a Length-Adjusted Abdominal Arterial Calcium Score Method for Contrast-Enhanced CT Scans

BACKGROUND

The Agatston score on noncontrast computed tomography (CT) scans is the gold standard for calcium load determination. However, contrast-enhanced CT is commonly used for patients with atherosclerotic cardiovascular diseases (ASCVDs), such as peripheral arterial occlusive disease (PAOD) and abdominal aortic aneurysm (AAA). Currently, there is no validated method to determine calcium load in the aorta and peripheral arteries with a contrast-enhanced CT. This study validated a length-adjusted calcium score (LACS) method for contrast-enhanced CT scans.

METHOD

The LACS (calcium volume in mm³/arterial length in cm) in the abdominal aorta was calculated using four-phase liver CT scans of 30 patients treated between 2017 and 2021 at the University Medical Center Groningen (UMCG) with no aortic disease. Noncontrast CT scans were segmented with a 130 Hounsfield units (HU) threshold, and a patient-specific threshold was used for contrast-enhanced CTs. The LACS was calculated and compared from both segmentations. Secondly, the interobserver variability and the influence of slice thickness (0.75 mm vs. 2.0 mm) was determined.

RESULTS

There was a high correlation between the LACS from contrast-enhanced CT scans and the LACS of noncontrast CTs (R² = 0.98). A correction factor of 1.9 was established to convert the LACS derived from contrast-enhanced CT to noncontrast CT scans. LACS interobserver agreement on contrast-enhanced CT was excellent (1.0, 95% confidence interval = 1.0-1.0). The 0.75 mm CT threshold was 541 (459-625) HU compared with 500 (419-568) HU on 2 mm CTs (p = 0.15). LACS calculated with both thresholds was not significantly different (p = 0.63).

CONCLUSION

The LACS seems to be a robust method for scoring calcium load on contrast-enhanced CT scans in arterial segments with various lengths.