Three-dimensional quadratic modeling and quantitative evaluation of the diaphragm on a volumetric CT scan in patients with chronic obstructive pulmonary disease

• Published in: Medical physics (Lancaster) 2016-07, Vol.43 (7), p.4273-4282
• Main Authors: Chang, Yongjun, Bae, Jangpyo, Kim, Namkug, Park, Joo-Young, Lee, Sang Min, Seo, Joon Beom
• Format: Article
• Language: English
• Subjects: 3D quadratic function; 60 APPLIED LIFE SCIENCES; Algorithms; Biological material, e.g. blood, urine; Haemocytometers; CAT SCANNING; chronic obstructive pulmonary disease (COPD); Computed tomography; COMPUTER CODES; Computer modeling; Computerised tomographs; computerised tomography; Cone-Beam Computed Tomography - methods; Data analysis; DIAPHRAGM; Diaphragm - diagnostic imaging; diaphragm segmentation; Digital computing or data processing equipment or methods, specially adapted for specific applications; DISEASES; Humans; Image data processing or generation, in general; image segmentation; Imaging, Three-Dimensional - methods; LENGTH; Lungs; medical image processing; Medical imaging; Models, Biological; MORPHOLOGY; muscle; Muscles; PATIENTS; Pattern Recognition, Automated - methods; Probability theory, stochastic processes, and statistics; Pulmonary Disease, Chronic Obstructive - diagnostic imaging; RADIATION PROTECTION AND DOSIMETRY; Radiography, Thoracic - methods; Radiologists; Regression Analysis; robust regression; Segmentation; SIMULATION; Statistical analysis; SURFACE AREA; Surface measurements; Surface morphology; THREE-DIMENSIONAL CALCULATIONS
• ISSN: 0094-2405; 2473-4209
• DOI: 10.1118/1.4953451

Purpose: In patients with chronic obstructive pulmonary disease (COPD), diaphragm function may deteriorate due to reduced muscle fiber length. Quantitative analysis of the morphology of the diaphragm is therefore important. In the authors current study, they propose a diaphragm segmentation method for COPD patients that uses volumetric chest computed tomography (CT) data, and they provide a quantitative analysis of the diaphragmatic dimensions. Methods: Volumetric CT data were obtained from 30 COPD patients and 10 normal control patients using a 16-row multidetector CT scanner (Siemens Sensation 16) with 0.75-mm collimation. Diaphragm segmentation using 3D ray projections on the lower surface of the lungs was performed to identify the draft diaphragmatic lung surface, which was modeled using quadratic 3D surface fitting and robust regression in order to minimize the effects of segmentation error and parameterize diaphragm morphology. This result was visually evaluated by an expert thoracic radiologist. To take into consideration the shape features of the diaphragm, several quantification parameters—including the shape index on the apex (SIA) (which was computed using gradient set to 0), principal curvatures on the apex on the fitted diaphragm surface (CA), the height between the apex and the base plane (H), the diaphragm lengths along the x-, y-, and z-axes (XL, YL, ZL), quadratic-fitted diaphragm lengths on the z-axis (FZL), average curvature (C), and surface area (SA)—were measured using in-house software and compared with the pulmonary function test (PFT) results. Results: The overall accuracy of the combined segmentation method was 97.22% ± 4.44% while the visual accuracy of the models for the segmented diaphragms was 95.28% ± 2.52% (mean ± SD). The quantitative parameters, including SIA, CA, H, XL, YL, ZL, FZL, C, and SA were 0.85 ± 0.05 (mm−1), 0.01 ± 0.00 (mm−1), 17.93 ± 10.78 (mm), 129.80 ± 11.66 (mm), 163.19 ± 13.45 (mm), 71.27 ± 17.52 (mm), 61.59 ± 16.98 (mm), 0.01 ± 0.00 (mm−1), and 34 380.75 ± 6680.06 (mm2), respectively. Several parameters were correlated with the PFT parameters. Conclusions: The authors propose an automatic method for quantitatively evaluating the morphological parameters of the diaphragm on volumetric chest CT in COPD patients. By measuring not only the conventional length and surface area but also the shape features of the diaphragm using quadratic 3D surface modeling, the proposed method is especially useful for quantifyin