An Optimal Estimate for the Anisotropic Logarithmic Potential

This paper introduces the new annulus body to establish the optimal lower bound for the anisotropic logarithmic potential as the complement to the theory of its upper bound estimate which has already been investigated. The connections with convex geometry analysis and some metric properties are also established. For the application, a polynomial dual log-mixed volume difference law is deduced from the optimal estimate.

Modelling of snowmelt runoff across the Himalayan Region

The Snowmelt-Runoff Model (SRM) was used to evaluate the impact of climate change on hydrological aspects of Lidder River Catchment of the Himalayan Region. It was observed that the river has an average discharge of 1082.49 cusecs. The coefficient of determination (R2) was varies in the range 0.90-0.95 during model validation period (2013-2018).The average coefficient of determination 0.926 and average seasonal volume difference (Dv) was obtained (-) 0.83%. The snow melt runoff harvested water can be used to bring 10 per cent more area under irrigation and water use efficiency which can be increased to an extent of 12-15 per cent for sustainable agriculture production in the Himalayan Region.

Predictive role of lymphoscintigraphy undergoing lymphovenous anastomosis in patients with lower extremity lymphedema: a preliminary study

Background We investigated whether preoperative lymphoscintigraphy could predict the treatment response of unilateral lymphovenous anastomosis (LVA) in patients with lower extremity lymphedema. Materials and methods A total of 17 patients undergoing lymphoscintigraphy subsequent to LVA was included. As qualitative lymphoscintigraphic indicators, ilioinguinal lymph node uptake, main lymphatic vessel, collateral vessel, and four types of dermal backflow patterns (absent; distal only; proximal only; whole lower limb) were evaluated. Lymph node uptake ratio, extremity uptake ratio, and injection site clearance ratio were obtained as quantitative lymphoscintigraphic indicators at 1 and 2-h after injection. To evaluate therapy response, the volume difference ratio of the whole lower limb at 3 months (early response) and 1 year (late response) was measured. Volume difference ratios (continuous variable and binary variable with a cut-off value of zero) were compared according to the lymphoscintigraphic variables. Results The group with whole lower limb dermal backflow had a greater volume change than the other groups (p = 0.047). The group with dermal backflow in the whole lower limb OR only in the distal part had a higher rate of volume reduction than the group with dermal backflow only in the proximal part OR absent (p = 0.050). The 2-h extremity uptake ratio was the only indicator that positively correlated with early and late volume difference ratio (p = 0.016, p = 0.001). The rate of volume decrease at 1 year was high in patients with high 2-h extremity uptake ratio (p = 0.027). As the amount of dermal backflow increases, the postoperative therapeutic effect increases (p = 0.040). Conclusions Preoperative lymphoscintigraphy is useful to predict both early and late therapy response in patients with lower extremity lymphedema undergoing LVA. Both dermal backflow pattern and extremity uptake ratio may be predictive lymphoscintigraphic indicators.

Predictive role of lymphoscintigraphy undergoing lymphovenous anastomosis in patients with lower extremity lymphedema

Background We investigated whether preoperative lymphoscintigraphy could predict the treatment response of unilateral lymphovenous anastomosis (LVA) in patients with lower extremity lymphedema. Materials and methods A total of 17 patients undergoing lymphoscintigraphy subsequent to LVA was included. As qualitative lymphoscintigraphic indicators, ilioinguinal lymph node uptake, main lymphatic vessel, collateral vessel, and dermal backflow pattern were evaluated. Lymph node uptake ratio, extremity uptake ratio, and injection site clearance ratio were obtained as quantitative lymphoscintigraphic indicators at 1 and 2-h after injection. To evaluate therapy response, the volume difference ratio of the whole leg at 3 months (early response) and 1 year (late response) was measured. Results The group with whole leg dermal backflow had a greater volume change than the other groups (p=0.047). The group with dermal backflow in the whole leg OR only in the distal part had a higher rate of volume reduction than the group with dermal backflow only in the proximal part OR absent (p=0.050). The 2-h extremity uptake ratio was the only indicator that positively correlated with early and late volume difference ratio (p=0.016, p=0.001). The rate of volume decrease at 1 year was high in patients with high 2-h extremity uptake ratio (p=0.027). As the amount of dermal backflow increases, the postoperative therapeutic effect increases (p=0.040). Conclusions Preoperative lymphoscintigraphy is useful to predict both early and late therapy response in patients with lower extremity lymphedema undergoing LVA. Both dermal backflow pattern and extremity uptake ratio may be predictive lymphoscintigraphic indicators.

Defining growth in small pulmonary nodules using volumetry: results from a “coffee-break” CT study and implications for current nodule management guidelines

Objectives An increase in lung nodule volume on serial CT may represent true growth or measurement variation. In nodule guidelines, a 25% increase in nodule volume is frequently used to determine that growth has occurred; this is based on previous same-day, test–retest (coffee-break) studies examining metastatic nodules. Whether results from prior studies apply to small non-metastatic nodules is unknown. This study aimed to establish the interscan variability in the volumetric measurements of small-sized non-metastatic nodules. Methods Institutional review board approval was obtained for this study. Between March 2019 and January 2021, 45 adults (25 males; mean age 65 years, range 37–84 years) with previously identified pulmonary nodules (30–150 mm3) requiring surveillance, without a known primary tumour, underwent two same-day CT scans. Non-calcified solid nodules were measured using commercial volumetry software, and interscan variability of volume measurements was assessed using a Bland–Altman method and limits of agreement. Results One hundred nodules (range 28–170 mm3; mean 81.1 mm3) were analysed. The lower and upper limits of agreement for the absolute volume difference between the two scans were − 14.2 mm3 and 12.0 mm3 respectively (mean difference 1.09 mm3, range − 33–12 mm3). The lower and upper limits of agreement for relative volume difference were − 16.4% and 14.6% respectively (mean difference 0.90%, range − 24.1–32.8%). Conclusions The interscan volume variability in this cohort of small non-metastatic nodules was smaller than that in previous studies involving lung metastases of varying sizes. An increase of 15% in nodule volume on sequential CT may represent true growth, and closer surveillance of these nodules may be warranted. Key Points • In current pulmonary nodule management guidelines, a threshold of 25% increase in volume is required to determine that true growth of a pulmonary nodule has occurred. • This test–retest (coffee break) study has demonstrated that a smaller threshold of 15% increase in volume may represent true growth in small non-metastatic nodules. • Closer surveillance of some small nodules growing 15–25% over a short interval may be appropriate.

Quantitative Comparisons of Deep-learning-based and Atlas-based Auto-segmentation of the Intermediate Risk Clinical Target Volume for Nasopharyngeal Carcinoma

Background: Manual segment target volumes were time-consuming and inter-observer variability couldn’t be avoided. With the development of computer science, auto-segmentation had the potential to solve this problem. Objective: To evaluate the accuracy and stability of Atlas-based and deep-learning-based auto-segmentation of the intermediate risk clinical target volume, composed of CTV2 and CTVnd, for nasopharyngeal carcinoma quantitatively. Methods and Materials: A cascade-deep-residual neural network was constructed to automatically segment CTV2 and CTVnd by deep learning method. Meanwhile, a commercially available software was used to automatically segment the same regions by Atlas-based method. The datasets included contrast computed tomography scans from 102 patients. For each patient, the two regions were manually delineated by one experienced physician. The similarity between the two auto-segmentation methods was quantitatively evaluated by Dice similarity coefficient, the 95th Hausdorff distance, volume overlap error and relative volume difference, respectively. Statistical analyses were performed using the ranked Wilcoxon test. Results: The average Dice similarity coefficient (±standard deviation) given by the deep-learning-based and Atlas-based auto-segmentation were 0.84(±0.03) and 0.74(±0.04) for CTV2, 0.79(±0.02) and 0.68(±0.03) for CTVnd, respectively. For the 95th Hausdorff distance, the corresponding values were 6.30±3.55mm and 9.34±3.39mm for CTV2, 7.09±2.27mm and 14.33±3.98mm for CTVnd. Besides, volume overlap error and relative volume difference could also predict the same situations. Statistical analyses showed significant difference between the two auto-segmentation methods (p<0.01). Conclusions: Compared with the Atlas-based segmentation approach, the deep-learning-based segmentation method performed better both in accuracy and stability for meaningful anatomical areas other than organs at risk.

Precise Segmentation of COVID-19 Infected Lung from CT Images Based on Adaptive First-Order Appearance Model with Morphological/Anatomical Constraints

A new segmentation technique is introduced for delineating the lung region in 3D computed tomography (CT) images. To accurately model the distribution of Hounsfield scale values within both chest and lung regions, a new probabilistic model is developed that depends on a linear combination of Gaussian (LCG). Moreover, we modified the conventional expectation-maximization (EM) algorithm to be run in a sequential way to estimate both the dominant Gaussian components (one for the lung region and one for the chest region) and the subdominant Gaussian components, which are used to refine the final estimated joint density. To estimate the marginal density from the mixed density, a modified k-means clustering approach is employed to classify the Gaussian subdominant components to determine which components belong properly to a lung and which components belong to a chest. The initial segmentation, based on the LCG-model, is then refined by the imposition of 3D morphological constraints based on a 3D Markov–Gibbs random field (MGRF) with analytically estimated potentials. The proposed approach was tested on CT data from 32 coronavirus disease 2019 (COVID-19) patients. Segmentation quality was quantitatively evaluated using four metrics: Dice similarity coefficient (DSC), overlap coefficient, 95th-percentile bidirectional Hausdorff distance (BHD), and absolute lung volume difference (ALVD), and it achieved 95.67±1.83%, 91.76±3.29%, 4.86±5.01, and 2.93±2.39, respectively. The reported results showed the capability of the proposed approach to accurately segment healthy lung tissues in addition to pathological lung tissues caused by COVID-19, outperforming four current, state-of-the-art deep learning-based lung segmentation approaches.

Implants versus Lipograft – Analysis of Long-Term Results after Congenital Breast Asymmetry Procedure

Congenital breast asymmetry represents a particular challenge to the classical techniques of plastic surgery due to a young group of patients. This study compares traditional breast augmentation using silicone implants to the more innovative lipograft technique regarding long-term results. To achieve this, we not only captured subjective parameters like satisfaction with outcome and symmetry, but also objective parameters such as breast volume and anthropometric measurements. Objective examination was performed manually and by using the Vectra&reg; H2 photogrammetry scanning system. Patients who underwent implant augmentation and lipograft both showed no significant differences in patient&acute;s satisfaction with surgical outcome (p = 0.55) and symmetry (p = 0.69). Furthermore, a breast symmetry of 93 % in both groups was reported. Likewise, no statistically significant volume difference between left and right breast was observed in both groups (p&lt;0.41). However, on average, lipograft patients needed 1.3 procedures more until the desired result was achieved. In contrast, patients treated with implant-based breast augmentation usually need several implant changes during their life. In conclusion, both methods should be considered for patients with congenital breast asymmetry.