Dual-Energy Computed Tomography for Stone Type Assessment: A Pilot Study of Dual-Energy Computed Tomography with Five Indices

Non-Invasive characterisation of renal stones using dual energy CT: A method to differentiate calcium stones

BACKGROUND

Non-invasive DECT based characterization of renal stones using their effective atomic number (Z_eff) and the electron density (ρe) in patients.

AIM

This paper aims to develop a method for in-vivo characterization of renal stone. Differentiation of renal stones in-vivo especially sub types of calcium stones have very important advantage for better judgement of treatment modality.

MATERIALS AND METHODS

50 extracted renal stones were scanned ex-vivo using dual energy CT scanner. A method was developed to characterize these renal stones using effective atomic number and electron density obtained from dual energy CT data. The method and formulation developed in ex-vivo experiments was applied in in-vivo study of 50 randomly selected patients of renal stones who underwent dual energy CT scan.

RESULTS

The developed method was able to characterize Calcium Oxalate Monohydrate (COM) and the combination of COM and Calcium Oxalate Dihydrate (COD) stones non-invasively in patients with a sensitivity of 81% and 83%respectively. The method was also capable of differentiating Uric, Cystine and mixed stones with the sensitivity of 100, 100 and 85.71% respectively.

CONCLUSION

The developed dual energy CT based method was capable of differentiating sub types of calcium stones which is not differentiable on single energy or dual energy CT images.

Carotid Artery Plaque Calcifications: Lessons From Histopathology to Diagnostic Imaging

The role of calcium in atherosclerosis is controversial and the relationship between vascular calcification and plaque vulnerability is not fully understood. Although calcifications are present in ≈50% to 60% of carotid plaques, their association with cerebrovascular ischemic events remains unclear. In this review, we summarize current understanding of carotid plaque calcification. We outline the role of calcium in atherosclerotic carotid disease by analyzing laboratory studies and histopathologic studies, as well as imaging findings to understand clinical implications of carotid artery calcifications. Differences in mechanism of calcium deposition express themselves into a wide range of calcification phenotypes in carotid plaques. Some patterns, such as rim calcification, are suggestive of plaques with inflammatory activity with leakage of the vasa vasourm and intraplaque hemorrhage. Other patterns such as dense, nodular calcifications may confer greater mechanical stability to the plaque and reduce the risk of embolization for a given degree of plaque size and luminal stenosis. Various distributions and patterns of carotid plaque calcification, often influenced by the underlying systemic pathological condition, have a different role in affecting plaque stability. Modern imaging techniques afford multiple approaches to assess geometry, pattern of distribution, size, and composition of carotid artery calcifications. Future investigations with these novel technologies will further improve our understanding of carotid artery calcification and will play an important role in understanding and minimizing stroke risk in patients with carotid plaques.

Value of artificial intelligence model based on unenhanced computed tomography of urinary tract for preoperative prediction of calcium oxalate monohydrate stones in vivo

Background

Urolithiasis is a global disease with a high incidence and recurrence rate, and stone composition is closely related to the choice of treatment and preventive measures. Calcium oxalate monohydrate (COM) is the most common in clinical practice, which is hard and difficult to fragment. Preoperative identification of its components and selection of effective surgical methods can reduce the risk of patients having a second operation. Methods that can be used for stone composition analysis include infrared spectroscopy, X-ray diffraction, and polarized light microscopy, but they are all performed on stone specimens in vitro after surgery. This study aimed to design and develop an artificial intelligence (AI) model based on unenhanced computed tomography (CT) images of the urinary tract, and to investigate the predictive ability of the model for COM stones in vivo preoperatively, so as to provide surgeons with more accurate diagnostic information.

Methods

Preoperative unenhanced CT images of patients with urinary calculi whose components were determined by infrared spectroscopy in a single center were retrospectively analyzed, including 337 cases of COM stones and 170 of non-COM stones. All images were manually segmented and the image features were extracted, and randomly divided into the training and testing sets in a ratio of 7:3. The least absolute shrinkage and selection operation algorithm (LASSO) was used to construct the AI model, and classification of the training and testing sets was carried out.

Results

A total of 1,218 radiomics imaging features were extracted, and 8 features with non-zero coefficients were finally obtained. The sensitivity, specificity and accuracy of the AI model were 90.5%, 84.3% and 88.5% for the training set, and 90.1%, 84.3% and 88.3% for the testing set. The area under the curve was 0.935 for the training set and 0.933 for the testing set.

Conclusions

The AI model based on unenhanced CT images of the urinary tract can predict COM and non-COM stones in vivo preoperatively, and the model has high sensitivity, specificity and accuracy.

In Vivo Comparison of Radiation Exposure in Third Generation versus Second Generation Dual-Source Dual-Energy CT for Imaging Urinary Calculi

PURPOSE

To investigate the potential for decreasing radiation dose when utilizing a third generation versus second generation dual-source dual-energy CT scanner, while maintaining diagnostic image quality and acceptable image noise.

MATERIALS AND METHODS

Retrospective analysis of patients who underwent dual-source dual-energy CT (dsDECT) for clinical suspicion of urolithiasis from 10/2/2017 - 9/5/2018. Patient demographics, body mass index, abdominal diameter, scanning parameters, and CT dose index volume (CTDIvol) were recorded. Image quality was assessed by measuring the attenuation and standard deviation (SD) regions of interest in the aorta and in the bladder. Image noise was determined by averaging the SD at both levels. Patients were excluded if they had not undergone both 3rd and 2nd generation DECT, time between DECT was more than 2 years, or scan parameters were outside standard protocol.

RESULTS

117 patients met inclusion criteria. Examinations performed on a 3rd generation DECT had an average CTDIvol 12.3 mGy, while examinations performed on a 2nd generation DECT had an average CTDIvol 13.3 mGy (p<0.001). Average image noise was significantly lower for the 3rd generation DECT (SD=10.3) as compared to the 2nd generation DECT (SD=13.9) (p<0.001).

CONCLUSIONS

The third generation dsDECT scanners can simultaneously decrease patient radiation dose and decrease image noise as compared to second generation DECT. These reductions in radiation exposure can be particularly important in patients with urinary stone disease who often require repeated imaging to evaluate for stone development and recurrence as well as treatment assessment.