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

Side-to-side differences in hip bone mineral density in patients with unilateral hip osteoarthritis

BACKGROUND

Accurately evaluating bone mineral density (BMD) in patients with unilateral hip osteoarthritis (OA) is crucial for diagnosing osteoporosis and selecting implants for hip arthroplasty. Our goal was to measure the BMD differences between sides, examine contributing factors, and identify the optimal side for BMD assessment in these patients.

METHODS

We analyzed 108 women with unilateral hip OA. Bilateral hip BMD was assessed automatically through quantitative CT (QCT) utilizing a validated, deep-learning-based approach. We evaluated BMD variations between the OA and healthy hips across total, neck, and distal regions. To determine their contributions, we analyzed factors, including patient demographics, Crowe classification, Bombelli classification, knee OA status, hip functional score, and gluteal muscle volume and density. Furthermore, we examined how side-to-side BMD differences influenced osteoporosis diagnosis using T-scores based on QCT.

RESULTS

The average BMD on the OA side was 6.9 % lower in the total region, 14.5 % higher in the neck region, and 9.4 % lower in the distal region than on the healthy side. Contributing factors to the reduced BMD in the OA hip included younger age, Bombelli classification (atrophic type), and significant gluteal muscle atrophy. Diagnoses from the OA side revealed lower sensitivity (61 %) than those from the healthy side (88 %).

CONCLUSIONS

Analysis on one side alone yields a more precise osteoporosis diagnosis from the healthy side. Nonetheless, bilateral BMD assessment remains crucial, particularly in younger individuals and those with atrophic OA types. Although based on QCT, our findings support bilateral analysis by dual-energy X-ray absorptiometry for these patients.

The effect of forearm rotation on the bone mineral density measurements of the distal radius

INTRODUCTION

Forearm dual-energy X-ray absorptiometry (DXA) is often performed in clinics where central DXA is unavailable. Accurate bone mineral density (BMD) measurement is crucial for clinical assessment. Forearm rotation can affect BMD measurements, but this effect remains uncertain. Thus, we aimed to conduct a simulation study using CT images to clarify the effect of forearm rotation on BMD measurements.

MATERIALS AND METHODS

Forearm CT images of 60 women were analyzed. BMD was measured at the total, ultra-distal (UD), mid-distal (MD), and distal 33% radius regions with the radius located at the neutral position using digitally reconstructed radiographs generated from CT images. Then, the rotation was altered from - 30° to 30° (supination set as positive) with a one-degree increment, and the percent BMD changes from the neutral position were quantified for all regions at each angle for each patient.

RESULTS

The maximum mean BMD changes were 5.8%, 7.0%, 6.2%, and 7.2% for the total, UD, MD, and distal 33% radius regions, respectively. The analysis of the absolute values of the percent BMD changes from the neutral position showed that BMD changes of all patients remained within 2% when the rotation was between - 5° and 7° for the total region, between - 3° and 2° for the UD region, between - 4° and 3° for the MD region, and between - 3° and 1° for the distal 33% radius region.

CONCLUSION

Subtle rotational changes affected the BMD measurement of each region. The results showed the importance of forearm positioning when measuring the distal radius BMD.

Bone mineral density estimation from a plain X-ray image by learning decomposition into projections of bone-segmented computed tomography

Osteoporosis is a prevalent bone disease that causes fractures in fragile bones, leading to a decline in daily living activities. Dual-energy X-ray absorptiometry (DXA) and quantitative computed tomography (QCT) are highly accurate for diagnosing osteoporosis; however, these modalities require special equipment and scan protocols. To frequently monitor bone health, low-cost, low-dose, and ubiquitously available diagnostic methods are highly anticipated. In this study, we aim to perform bone mineral density (BMD) estimation from a plain X-ray image for opportunistic screening, which is potentially useful for early diagnosis. Existing methods have used multi-stage approaches consisting of extraction of the region of interest and simple regression to estimate BMD, which require a large amount of training data. Therefore, we propose an efficient method that learns decomposition into projections of bone-segmented QCT for BMD estimation under limited datasets. The proposed method achieved high accuracy in BMD estimation, where Pearson correlation coefficients of 0.880 and 0.920 were observed for DXA-measured BMD and QCT-measured BMD estimation tasks, respectively, and the root mean square of the coefficient of variation values were 3.27 to 3.79% for four measurements with different poses. Furthermore, we conducted extensive validation experiments, including multi-pose, uncalibrated-CT, and compression experiments toward actual application in routine clinical practice.

Development and validation of an open-source tool for opportunistic screening of osteoporosis from hip CT images

Aims

This study aimed to develop and validate a fully automated system that quantifies proximal femoral bone mineral density (BMD) from CT images.

Methods

The study analyzed 978 pairs of hip CT and dual-energy X-ray absorptiometry (DXA) measurements of the proximal femur (DXA-BMD) collected from three institutions. From the CT images, the femur and a calibration phantom were automatically segmented using previously trained deep-learning models. The Hounsfield units of each voxel were converted into density (mg/cm3). Then, a deep-learning model trained by manual landmark selection of 315 cases was developed to select the landmarks at the proximal femur to rotate the CT volume to the neutral position. Finally, the CT volume of the femur was projected onto the coronal plane, and the areal BMD of the proximal femur (CT-aBMD) was quantified. CT-aBMD correlated to DXA-BMD, and a receiver operating characteristic (ROC) analysis quantified the accuracy in diagnosing osteoporosis.

Results

CT-aBMD was successfully measured in 976/978 hips (99.8%). A significant correlation was found between CT-aBMD and DXA-BMD (r = 0.941; p < 0.001). In the ROC analysis, the area under the curve to diagnose osteoporosis was 0.976. The diagnostic sensitivity and specificity were 88.9% and 96%, respectively, with the cutoff set at 0.625 g/cm2.

Conclusion

Accurate DXA-BMD measurements and diagnosis of osteoporosis were performed from CT images using the system developed herein. As the models are open-source, clinicians can use the proposed system to screen osteoporosis and determine the surgical strategy for hip surgery.

The effect of patient positioning on measurements of bone mineral density of the proximal femur: a simulation study using computed tomographic images

The patient's position may affect the bone mineral density (BMD) measurements; however, the extent of this effect is undefined. This CT image-based simulation study quantified changes in BMD induced by hip flexion, adduction, and rotations to recommend appropriate patient positioning when acquiring dual-energy x-ray absorptiometry images.

PURPOSE

Several studies have analyzed the effect of hip rotation on the measurement of bone mineral density (BMD) of the proximal femur by dual-energy x-ray absorptiometry (DXA). However, as the effects of hip flexion and abduction on BMD measurements remain uncertain, a computational simulation study using CT images was performed in this study.

METHODS

Hip CT images of 120 patients (33 men and 87 women; mean age, 82.1 ± 9.4 years) were used for analysis. Digitally reconstructed radiographs of the proximal femur region were generated from CT images to calculate the BMD of the proximal femur region. BMD at the neutral position was quantified, and the percent changes in BMD when hip internal rotation was altered from -30° to 15°, when hip flexion was altered from 0° to 30°, and when hip abduction was altered from -15° to 30° were quantified. Analyses were automatically performed with a 1° increment in each direction using computer programming.

RESULTS

The alteration of hip angles in each direction affected BMD measurements, with the largest changes found for hip flexion (maximum change of 17.7% at 30° flexion) and the smallest changes found for hip rotation (maximum change of 2.2% at 15° internal rotation). The BMD measurements increased by 0.34% for each 1° of hip abduction, and the maximum change was 12.3% at 30° abduction.

CONCLUSION

This simulation study quantified the amount of BMD change induced by altering the hip position. Based on these results, we recommend that patients be positioned carefully when acquiring DXA images.

Development of a system to assess the two- and three-dimensional bone mineral density of the lumbar vertebrae from clinical quantitative CT images

This study developed a system to quantify the lumbar spine's bone mineral density (BMD) in two and three dimensions for osteoporosis screening using quantitative CT images. Measuring the two-dimensional BMD could reproduce the BMD measurement performed in dual-energy X-ray absorptiometry, and an accurate diagnosis of osteoporosis was possible.

PURPOSE

To date, the assessment of bone mineral density (BMD) using CT images has been made in three dimensions, leading to errors in detecting osteoporosis based on the two-dimensional assessments of BMD using dual-energy X-ray absorptiometry (DXA-BMD). Herein, we aimed to develop a system that measures two- and three-dimensional lumbar BMD from quantitative CT images and validated the accuracy of the system in diagnosing osteoporosis with regard to the DXA classification.

METHODS

Fifty-nine pairs of spinal CT and DXA images were analyzed. First, the three-dimensional BMD was measured at the axial slice of the L1 vertebra on CT images (L1-vBMD). Then, the L1-L4 vertebrae were segmented from the CT images to measure the three-dimensional BMD at the trabecular region of the L1-L4 vertebral bodies (CT-vBMD). Lastly, the segmented vertebrae were projected onto the coronal plane to measure the two-dimensional BMD (CT-aBMD). Each parameter was correlated with DXA-BMD, and the receiver operating characteristic (ROC) curve to diagnose osteoporosis was assessed.

RESULTS

The correlation coefficients of DXA-BMD with L1-vBMD, CT-vBMD, and CT-aBMD were 0.364, 0.456, and 0.911, respectively (all p < 0.01). In the ROC curve analysis to diagnose osteoporosis, the area under the curve for CT-aBMD (0.941) was significantly higher than those for L1-vBMD (0.582) and CT-vBMD (0.657) (both p < 0.01).

CONCLUSION

Compared with L1-vBMD and CT-vBMD, CT-aBMD could accurately predict DXA-BMD and detect patients with osteoporosis. Given that our method can quantify BMD in both two and three dimensions, it could be used to screen for osteoporosis from quantitative CT images.

The Effect of Region of Interest on Measurement of Bone Mineral Density of the Proximal Femur: Simulation Analysis Using CT Images

While accurate measurement of bone mineral density (BMD) is essential in the diagnosis of osteoporosis and in evaluating the treatment of osteoporosis, it is unclear how region of interest (ROI) settings affect measurement of BMD at the total proximal femur region. In this study, we performed a simulation analysis to clarify the effect on BMD measurement of changing the ROI using hip computed tomography (CT) images of 75 females (mean age, 62.4 years). Digitally reconstructed radiographs of the proximal femur region were generated from CT images to calculate the change in BMD when the proximal boundary of the ROI was altered by 0-10 mm, and when the distal boundary of the ROI was altered by 0-30 mm. Further, changes in BMD were compared across BMD classification groups. A mean BMD increase of 0.62% was found for each 1-mm extension of the distal boundary. A mean BMD decrease of 0.18% was found for each 1-mm alteration of the proximal boundary. Comparing BMD classification groups, patients with osteoporosis and osteopenia demonstrated greater BMD changes than patients with normal BMD for the distal boundary (0.68%, 0.64%, and 0.54%, respectively) and patients with osteoporosis demonstrated greater BMD changes than patients with osteoporosis and normal BMD for the proximal boundary (0.37%, 0.13%, and 0.03%, respectively). In conclusion, our study found that a consistent ROI setting, especially on the distal boundary, is necessary for the accurate measurement of total proximal femur BMD. Based on the findings, we recommend confirming that the ROI setting shown on the BMD result form is consistent with changes in serial BMD.

Development of an open-source measurement system to assess the areal bone mineral density of the proximal femur from clinical CT images

Commercial software is generally needed to measure the areal bone mineral density (aBMD) of the proximal femur from clinical computed tomography (CT) images. This study developed and verified an open-source reproducible system to quantify CT-aBMD to screen osteoporosis using clinical CT images.

PURPOSE

For existing CT images acquired for various reasons other than osteoporosis, it might be beneficial to estimate areal BMD as assessed by dual-energy X-ray absorptiometry (DXA-based BMD) to ascertain the bone status based on DXA. In this study, we aimed to (1) develop an open-source reproducible measurement system to quantify DXA-based BMD from CT images and (2) validate its accuracy.

METHODS

This study analyzed 75 pairs of hip CT and DXA images of women that were acquired for the preoperative assessment of total hip arthroplasty. From the CT images, the femur and a calibration phantom were automatically segmented using pre-trained codes/models available at https://github.com/keisuke-uemura . The proximal femoral region was isolated by manually selected landmarks and was projected onto the coronal plane to measure the areal density (CT-aHU). The calibration phantom was employed to convert the CT-aHU into CT-aBMD. Each parameter was correlated with DXA-based BMD, and the residual errors of CT images to estimate the T-scores in DXA were calculated using the standard error of estimate (SEE).

RESULTS

The correlation coefficients of DXA-based BMD with CT-aHU and CT-aBMD were 0.947 and 0.950, respectively (both p < 0.001). The SEE for quantifying the T-scores in DXA were 0.51 and 0.50 for CT-aHU and CT-aBMD, respectively.

CONCLUSION

With the method developed herein, CT permits estimation of the DXA-based BMD of the proximal femur within the standard DXA total hip region of interest with an SEE of 0.5 in T-scores. The radiation dose for CT acquisition needs consideration; therefore, our data do not provide a rationale for performing CT for screening osteoporosis. However, on CT images already acquired for clinical indications other than osteoporosis, researchers may use this open-source system to investigate osteoporosis status through the estimated DXA-based BMD of the proximal femur.