Quantitative Computed Tomography (QCT) of the Distal Forearm in Men Using a Spiral Whole-Body CT Scanner - Description of a Method and Reliability Assessment of the QCT Pro Software

Performance of iCare quantitative computed tomography in bone mineral density assessment of the hip and vertebral bodies in European spine phantom

BACKGROUND

Osteoporosis is a systemic bone disease which can increase the risk of osteoporotic fractures. Dual-energy X-ray absorptiometry (DXA) is considered as the clinical standard for diagnosing osteoporosis by detecting the bone mineral density (BMD) in patients, but it has flaws in distinguishing between calcification and other degenerative diseases, thus leading to inaccurate BMD levels in subjects. Mindways quantitative computed tomography (Mindways QCT) is a classical QCT system. Similar to DXA, Mindways QCT can directly present the density of trabecular bone, vascular or tissue calcification; therefore, it is more accurate and sensitive than DXA and has been widely applied in clinic to evaluate osteoporosis. iCare QCT osteodensitometry was a new phantom-based QCT system, recently developed by iCare Inc. (China). It has been gradually applied in clinic by its superiority of taking 3-dimensional BMD of bone and converting BMD values to T value automatically. This study aimed at evaluating the osteoporosis detection rate of iCare QCT, compared with synchronous Mindways QCT (USA).

METHODS

In this study, 131 patients who underwent hip phantom-based CT scan were included. Bone mineral density (BMD) of the unified region of interests (ROI) defined at the European spine phantom (ESP, German QRM) including L1 (low), L2 (medium), and L3 (high) vertebral bodies was detected for QCT quality control and horizontal calibration. Every ESP scan were taken for 10 times, and the mean BMD values measured by iCare QCT and Mindways QCT were compared. Hip CT scan was conducted with ESP as calibration individually. T-scores gained from iCare QCT and Mindways QCT were analyzed with Pearson correlation test. The detection rates of osteoporosis were compared between iCare QCT and Mindways QCT. The unified region of interests (ROI) was delineated in the QCT software.

RESULTS

The results showed that there was no significant difference between iCare QCT and Mindways QCT in the evaluation of L1, L2, and L3 vertebrae bodies in ESP. A strong correlation between iCare QCT and Mindways QCT in the assessment of hip T-score was found. It was illustrated that iCare QCT had a higher detection rate of osteoporosis with the assessment of hip T-score than Mindways QCT did. In patients < 50 years subgroup, the detection rate of osteoporosis with iCare QCT and Mindways QCT was equal. In patients ≥ 50 years subgroup, the detection rate of osteoporosis with iCare QCT (35/92, 38.0%) was higher than that with Mindways QCT. In female subgroup, the detection rate of osteoporosis with iCare QCT was significantly higher than Mindways QCT. In male subgroup, the detection rate of osteoporosis with iCare QCT was also markedly higher than Mindways QCT. The detection rate of osteoporosis by iCare QCT was higher than Mindways QCT with hip bone assessment. Of course, the results of the present study remain to be further verified by multicenter studies in the future.

An application of the density standard and scaled-pixel-counting protocol to assess the radiodensity of equine incisor teeth affected by resorption and hypercementosis: preliminary advancement in dental radiography

BACKGROUND

Equine Odontoclastic Tooth Resorption and Hypercementosis (EOTRH) syndrome is a dental disease where the radiographic signs may be quantified using radiographic texture features. This study aimed to implement the scaled-pixel-counting protocol to quantify and compare the image structure of teeth and the density standard in order to improve the identification of the radiographic signs of tooth resorption and hypercementosis using the EOTRH syndrome model.

METHODS AND RESULTS

A detailed examination of the oral cavity was performed in 80 horses and maxillary incisor teeth were evaluated radiographically, including an assessment of the density standard. On each of the radiographs, pixel brightness (PB) was extracted for each of the ten steps of the density standard (S1-S10). Then, each evaluated incisor tooth was assigned to one of 0-3 EOTRH grade-related groups and annotated using region of interest (ROI). For each ROI, the number of pixels (NP) from each range was calculated. The linear relation between an original X-ray beam attenuation and PB was confirmed for the density standard. The NP values increased with the number of steps of the density standard as well as with EOTRH degrees. Similar accuracy of the EOTRH grade differentiation was noted for data pairs EOTRH 0-3 and EOTRH 0-1, allowing for the differentiation of both late and early radiographic signs of EOTRH.

CONCLUSION

The scaled-pixel-counting protocol based on the use of density standard has been successfully implemented for the differentiation of radiographic signs of EOTRH degrees.

Reduced trabecular bone mineral density and thinner cortices in men with distal forearm fractures

Men with distal forearm fractures have reduced bone density, an increased risk of osteoporosis and of further fractures. The aim of the study was to investigate the structural determinants of these observations using quantitative CT (qCT). Ninety six men with low-trauma distal forearm fracture and 101 age-matched healthy control subjects were recruited. All subjects underwent a quantitative CT on a standard 64-slice whole body CT scanner. These were analysed on Mindways QCT PRO™ Software to generate volumetric and geometric data at the lumbar spine, femoral neck and total hip, ultra-distal and distal 33 % radius. Biochemical investigations, health questionnaires and measurements of bone turnover were made. Men with fracture had significantly lower total and trabecular vBMD at all sites. The greatest percentage reduction was at the ultra-distal radius (13.5 % total and 11.7 % trabecular vBMD). In the fracture group cortical vBMD was significantly higher in the femoral neck (p < 0.001) and maintained at the ultra-distal radius compared with control subjects. However, cortical cross-sectional area (CSA) and thickness were significantly reduced at the femoral neck (p < 0.001 and p = 0.002 respectively) and forearm sites (CSA ultradistal radius p = 0.001, cortical thickness p = 0.002, CSA distal one third radius p = 0.045 and cortical thickness p = 0.005). Cross sectional moment of inertia (CSMI) and section moduli were significantly reduced at the femoral neck (CSMI1 p = 0.002, CSMI2 p = 0.012 and section moduli Z1 p < 0.001, Z2 p = 0.004) and the ultra-distal radius (CSMI1 p = 0.008 and section moduli Z1 p = 0.018, Z2 p = 0.007). In stepwise logistic regression analysis distal forearm fracture showed the strongest association with a model comprising ultra-distal forearm trabecular vBMD (negative), procollagen type I N-terminal propeptide (PINP, positive) and sex hormone binding globulin (SHBG, negative). In conclusion, these observations explain the structural reasons for the increased fracture risk in men with distal forearm fractures.

Automated segmentation of an intensity calibration phantom in clinical CT images using a convolutional neural network

PURPOSE

In quantitative computed tomography (CT), manual selection of the intensity calibration phantom's region of interest is necessary for calculating density (mg/cm³) from the radiodensity values (Hounsfield units: HU). However, as this manual process requires effort and time, the purposes of this study were to develop a system that applies a convolutional neural network (CNN) to automatically segment intensity calibration phantom regions in CT images and to test the system in a large cohort to evaluate its robustness.

METHODS

This cross-sectional, retrospective study included 1040 cases (520 each from two institutions) in which an intensity calibration phantom (B-MAS200, Kyoto Kagaku, Kyoto, Japan) was used. A training dataset was created by manually segmenting the phantom regions for 40 cases (20 cases for each institution). The CNN model's segmentation accuracy was assessed with the Dice coefficient, and the average symmetric surface distance was assessed through fourfold cross-validation. Further, absolute difference of HU was compared between manually and automatically segmented regions. The system was tested on the remaining 1000 cases. For each institution, linear regression was applied to calculate the correlation coefficients between HU and phantom density.

RESULTS

The source code and the model used for phantom segmentation can be accessed at https://github.com/keisuke-uemura/CT-Intensity-Calibration-Phantom-Segmentation . The median Dice coefficient was 0.977, and the median average symmetric surface distance was 0.116 mm. The median absolute difference of the segmented regions between manual and automated segmentation was 0.114 HU. For the test cases, the median correlation coefficients were 0.9998 and 0.999 for the two institutions, with a minimum value of 0.9863.

CONCLUSION

The proposed CNN model successfully segmented the calibration phantom regions in CT images with excellent accuracy.