Shi Gengxin, Quevedo Gonzalez Fernando J, Breighner Ryan E, Moseley Kendall F, Witham Timothy, Carrino John A, Siewerdsen Jeffrey H, Zbijewski Wojciech
Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland, USA.
Department of Biomechanics, Hospital for Special Surgery, New York, New York, USA.
Med Phys. 2025 Jul;52(7):e17943. doi: 10.1002/mp.17943.
CT image texture features (TFs) of trabecular bone are being investigated as predictive markers in osteoporosis (OP) and osteoarthritis (OA). To support the development of such radiomic approaches, the reproducibility of bone TFs to CT system factors needs to be characterized. We hypothesize that, in addition to the well-established inter- and intra-scanner effects (e.g., changes in reconstruction algorithm), the non-stationarity of CT spatial resolution may introduce inconsistencies in TFs of morphologically similar bone regions placed in different locations within the scanner field-of-view (FOV).
To characterize the impact of spatially-variant CT blur on the reproducibility of trabecular bone TFs to radial shift within the FOV for a conventional normal resolution (NR) protocol with ∼0.5 mm slice thickness, and an ultra-high resolution (UHR) protocol representative of the new generation of high-resolution CT (0.25 mm slice thickness).
Canon Aquilion Precision CT (Canon Medical Systems, Japan) was used to scan four human femora placed at 0, 9, and 18 cm radial shifts from the isocenter. NR (∼0.26 mm in-plane voxel size) and UHR (∼0.13 mm in-plane voxel) images were obtained at 1.5 s rotation. At each CT resolution, a total of 377 spherical regions of interest (ROIs) of 2.5 mm radius were seeded within the trabecular bone of the femora at 0 cm shift and transported via rigid registration to the images at 9 cm and 18 cm shift. TFs of the Gray Level Co-Occurrence and Run Length matrices (GLCM and GLRLM) were obtained for all ROIs. Reproducibility across radial shifts was evaluated (separately in NR and UHR) in terms of the concordance correlation coefficient (CCC) between the registered ROIs. Support vector machine (SVM) classifiers were applied to evaluate whether the radial shift of an ROI can be predicted from its TFs.
In NR, the median CCCs for 0 cm vs. 9 cm radial shifts were ∼0.9 for both GLCM and GLRLM TFs, dropping to ∼0.6 for 0 cm vs. 18 cm shifts. In UHR CT, the median CCCs of GLCM TFs were 0.92 for 0 cm vs. 9 cm and 0.52 for 0 cm vs. 18 cm; for GLRLM, the median CCCs were 0.88 for 0 cm vs. 9 cm and 0.53 for 0 cm vs. 18 cm. In a separate analysis on only high contrast ROIs (upper quartiles of voxel value variance at 0 cm), we found an additional 20% (for 0 cm vs. 9 cm) to 40% (for 0 cm vs. 18 cm) reduction in CCC in both UHR and NR. The somewhat worse reproducibility in UHR CT is attributed to more pronounced effects of gantry motion blur. The classification models were able to discriminate ROIs at 0 cm from the other radial shifts with median accuracy of 0.54 (0.67 in high contrast ROIs) in NR and 0.46 (0.67 in high contrast ROIs) in UHR. The ROIs at 9 cm were identified with 0.31 (0.5) accuracy in NR and 0.46 (0.58) accuracy in UHR; the ROIs at 18 cm were identified with 0.60 (0.83) accuracy in NR and 0.77 (0.83) accuracy in UHR.
Nonstationary CT spatial resolution leads to a loss of reproducibility of trabecular bone TFs between different regions of the scan FOV, potentially confounding radiomic predictors in clinical data where the lateral shift of the skeletal site of interest changes with patient size.
小梁骨的CT图像纹理特征（TFs）正被研究作为骨质疏松症（OP）和骨关节炎（OA）的预测标志物。为支持此类放射组学方法的发展，需要确定骨TFs对CT系统因素的可重复性。我们假设，除了已确定的扫描仪间和扫描仪内效应（例如，重建算法的变化）外，CT空间分辨率的非平稳性可能会导致放置在扫描仪视野（FOV）内不同位置的形态相似骨区域的TFs出现不一致。
对于具有约0.5mm层厚的传统正常分辨率（NR）协议以及代表新一代高分辨率CT（0.25mm层厚）的超高分辨率（UHR）协议，表征空间变化的CT模糊对小梁骨TFs在FOV内径向移位的可重复性的影响。
使用佳能Aquilion Precision CT（佳能医疗系统公司，日本）扫描四个置于距等中心径向移位0、9和18cm处的人股骨。在1.5秒旋转时获得NR（平面体素大小约为0.26mm）和UHR（平面体素约为0.13mm）图像。在每个CT分辨率下，在0cm移位处的股骨小梁骨内植入总共377个半径为2.5mm的球形感兴趣区域（ROIs），并通过刚性配准将其传输到9cm和18cm移位处的图像。获取所有ROIs的灰度共生矩阵和游程长度矩阵（GLCM和GLRLM）的TFs。根据配准ROIs之间的一致性相关系数（CCC）评估径向移位的可重复性（在NR和UHR中分别进行）。应用支持向量机（SVM）分类器评估是否可以从ROIs的TFs预测其径向移位。
在NR中，GLCM和GLRLM TFs在0cm与9cm径向移位之间的中位数CCC约为0.9，在0cm与18cm移位之间降至约0.6。在UHR CT中，GLCM TFs在0cm与9cm之间的中位数CCC为0.92，在0cm与18cm之间为0.52；对于GLRLM，在0cm与9cm之间的中位数CCC为0.88，在0cm与18cm之间为0.53。在仅对高对比度ROIs（0cm处体素值方差的上四分位数）进行的单独分析中，我们发现UHR和NR中CCC在0cm与9cm之间额外降低了20%，在0cm与18cm之间额外降低了40%。UHR CT中稍差的可重复性归因于龙门运动模糊的更明显影响。分类模型能够将0cm处的ROIs与其他径向移位区分开，在NR中的中位数准确率为0.54（高对比度ROIs中为0.67），在UHR中为0.46（高对比度ROIs中为0.67）。在NR中，9cm处的ROIs识别准确率为0.31（0.5），在UHR中为0.46（0.58）；在NR中，18cm处的ROIs识别准确率为0.60（0.83），在UHR中为0.77（0.83）。
非平稳CT空间分辨率导致扫描FOV不同区域之间小梁骨TFs的可重复性丧失，这可能会混淆临床数据中的放射组学预测指标，因为感兴趣骨骼部位的横向移位会随患者体型而变化。
