No abstract

Bone fatigue accumulation is a factor leading to bone fracture, which is a progressive process of microdamage deteriorating under long-term and repeated stress. Since the microdamage of the early stage in bone is difficult to be investigated by linear ultrasound, the second harmonic generation method in nonlinear ultrasound technique is employed in this paper, which is proved to be more sensitive to microdamage. To solve the deficiency that the second harmonic component is easily submerged by noise in traditional nonlinear measurement, a weighted chirp coded sinusoidal signal was applied as the ultrasonic excitation, while pulse inversion is implemented at the receiving side. The effectiveness of this combination to improve the signal-to-noise ratio has been demonstrated by in vitro experiment. Progressive fatigue loading experiments were conducted on the cortical bone plate in vitro for microdamage generation. There was a significant increase in the slope of the acoustic nonlinearity parameter with the propagation distance (increased by 8% and 24% respectively) when the bone specimen was at a progressive level of microdamage. These results indicate that the coded nonlinear ultrasonic method might have the potential in diagnosing bone fatigue.

No abstract

BackgroundThe accurate definition of gross tumor volume (GTV) of esophageal squamous cell carcinoma (ESCC) can promote precise irradiation field determination, and further achieve the radiotherapy curative effect. This retrospective study is intended to assess the applicability of leveraging deep learning-based method to automatically define the GTV from 3D 18F-FDG PET/CT images of patients diagnosed with ESCC.MethodsWe perform experiments on a clinical cohort with 164 18F-FDG PET/CT scans. The state-of-the-art esophageal GTV segmentation deep neural net is first employed to delineate the lesion area on PET/CT images. Afterwards, we propose a novel equivalent truncated elliptical cone integral method (ETECIM) to estimate the GTV value. Indexes of Dice similarity coefficient (DSC), Hausdorff distance (HD), and mean surface distance (MSD) are used to evaluate the segmentation performance. Conformity index (CI), degree of inclusion (DI), and motion vector (MV) are used to assess the differences between predicted and ground truth tumors. Statistical differences in the GTV, DI, and position are also determined.ResultsWe perform 4-fold cross-validation for evaluation, reporting the values of DSC, HD, and MSD as 0.72 ± 0.02, 11.87 ± 4.20 mm, and 2.43 ± 0.60 mm (mean ± standard deviation), respectively. Pearson correlations (R2) achieve 0.8434, 0.8004, 0.9239, and 0.7119 for each fold cross-validation, and there is no significant difference (t = 1.193, p = 0.235) between the predicted and ground truth GTVs. For DI, a significant difference is found (t = −2.263, p = 0.009). For position assessment, there is no significant difference (left-right in x direction: t = 0.102, p = 0.919, anterior–posterior in y direction: t = 0.221, p = 0.826, and cranial–caudal in z direction: t = 0.569, p = 0.570) between the predicted and ground truth GTVs. The median of CI is 0.63, and the gotten MV is small.ConclusionsThe predicted tumors correspond well with the manual ground truth. The proposed GTV estimation approach ETECIM is more precise than the most commonly used voxel volume summation method. The ground truth GTVs can be solved out due to the good linear correlation with the predicted results. Deep learning-based method shows its promising in GTV definition and clinical radiotherapy application.

No abstract