Attenuation profile matching: An accurate and scan parameter‐robust measurement method for small airway dimensions in low‐dose <scp>CT</scp> scans

Yang, Zhou; Jin, Haiyan; Kim, Jong Hyo

doi:10.1002/mp.13074

Cited by 4 publications

(4 citation statements)

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“…Experimental methods for patient-specific QA in advanced radiotherapy are, however, time-consuming in both manpower and accelerator time and have been shown to be unable to detect some unacceptable plans. [6][7][8][9][10][11] Recent studies have demonstrated a sensitivity of only 5% to detect IMRT plan errors using IMRT pretreatment measurements. 12,13 Moreover, as treatment planning becomes more efficient and the number of patients treated with advanced radiotherapy techniques steadily increases, measurement-based verification may result in a continued increase in workload.…”

Section: Review Of the Problemmentioning

confidence: 99%

“…1,3 Despite accurate CT assessments of larger airways with thicker walls, the cross-sectional pathological features in small airways with internal diameters of less than 2 mm have shown to be more critical to airway function. 9,[11][12][13][14] Due to the spatial resolution limitations of CT techniques in the clinical setting, partial volume effects reduce measurement accuracy for both lumen diameters and wall thicknesses. 12 Hence, the assessment of small airway dimensions up to the 6th generation (~2.5 mm in inner diameter) or higher generations remains challenging.…”

Section: Introductionmentioning

confidence: 99%

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Quantification of airway dimensions using a high‐resolution CT scanner: A phantom study

et al. 2021

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Small airways with inner diameters less than 2 mm are sites of major airflow limitations in patients with chronic obstructive pulmonary disease (COPD) and asthma. The purpose of this study is to investigate the limitations for accurate assessment of small airway dimensions using both high-resolution CT (HRCT) and conventional normal-resolution CT at low dose levels. Methods:To model the normal human airways from the 3rd to 20th generations, a cylindrical polyurethane phantom with 14 airway tubes of inner diameters (ID) ranging from 0.3 to 3.4 mm and wall thicknesses (WT) ranging from 0.15 to 1.6 mm was placed within an Anthropomorphic QRM-Thorax phantom.The Aquilion Precision (Canon Medical Systems Corporation) HRCT scanner was used to acquire images at 80, 100, and 120 kV using high resolution mode (HR, 0.25 mm × 160 detector configuration) and normal-resolution (NR) mode (0.5 mm × 80 detector configuration). The HR data were reconstructed using a 1024 × 1024 matrix (0.22 × 0.22 × 0.25 mm voxel size) and the NR data were reconstructed using a 512 × 512 matrix (0.43 × 0.43 × 0.50 mm). Two reconstruction algorithms (filtered back projection; FBP and an adaptive iterative dose reduction 3D algorithm; AIDR 3D) and three reconstruction kernels (FC30, FC52, and FC56) were investigated. The CTDI vol dose values ranged from 0.2 to 6.2 mGy. A refined automated full-width half-maximum (FWHM) method was used for the measurement of airway dimensions, where the density profiles were computed by radial oversampling using a polar coordinate system. Both ID and WT were compared to the known dimensions using a regression model, and the root-mean-square error (RMSE) and average error were computed across all 14 airway tubes. Results:The results indicate that the ID can be measured within a 15% error down to approximately 0.8 and 2.0 mm using the HR and NR modes, respectively. The overall RMSE (and average error) of ID measurements for HR and NR were 0.10 mm (−0.70%) and 0.31 mm (−2.63%), respectively. The RMSE (and average error) of WT measurements using HR and NR were 0.10 mm (23.27%) and 0.27 mm (53.56%), respectively. The WT measurement using HR yielded a factor of two improvement in accuracy as compared to NR.

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Section: Review Of the Problemmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Quantification of airway dimensions using a high‐resolution CT scanner: A phantom study

et al. 2021

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“…The segmentation of small and peripheral airways is not a trivial task. 5,48,80 In this paper, we analyse the reproducibility of all computerized components of the tapering algorithm. The paper does not address reproducibility of manual labelling of the airways.…”

Section: Limitationsmentioning

confidence: 99%

Reproducibility of an airway tapering measurement in computed tomography with application to bronchiectasis

et al. 2019

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Purpose: This paper proposes a pipeline to acquire a scalar tapering measurement from the carina to the most distal point of an individual airway visible on CT. We show the applicability of using tapering measurements on clinically acquired data by quantifying the reproducibility of the tapering measure. Methods: We generate a spline from the centreline of an airway to measure the area and arclength at contiguous intervals. The tapering measurement is the gradient of the linear regression between area in log space and arclength. The reproducibility of the measure was assessed by analysing different radiation doses, voxel sizes and reconstruction kernel on single timepoint and longitudinal CT scans and by evaluating the effct of airway bifurcations. Results: Using 74 airways from 10 CT scans, we show a statistical difference, p = 3.4 × 10 −4 in tapering between healthy airways (n = 35) and those affected by bronchiectasis (n = 39). The difference between the mean of the two populations was 0.011mm −1 and the difference between the medians of the two populations was 0.006mm −1 . The tapering measurement retained a 95% confidence interval of ±0.005mm −1 in a simulated 25 mAs scan and retained a 95% confidence of ±0.005mm −1 on simulated CTs up to 1.5 times the original voxel size. Conclusion: We have established an estimate of the precision of the tapering measurement and estimated the effect on precision of simulated voxel size and CT scan dose. We recommend that the scanner calibration be undertaken with the phantoms as described, on the specific CT scanner, radiation dose and reconstruction algorithm that is to be used in any quantitative studies.Our code is available at https://github.com/quan14/AirwayTaperingInCT

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“…Computed tomography (CT) is widely used for diagnosis, patient evaluation, and monitoring of body composition changes. Volumetric techniques are essential for diagnostic purposes, and recent efforts focus on deriving non-invasive imaging biomarkers for various diseases [ 1 , 2 ]. Hounsfield unit values enable assessment of the muscle, adipose tissue, and bone structures’ volume and status.…”

Section: Introductionmentioning

confidence: 99%

Deep learning-based pectoralis muscle volume segmentation method from chest computed tomography image using sagittal range detection and axial slice-based segmentation

Yang,

Choi,

Choi

et al. 2023

PLoS ONE

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The pectoralis muscle is an important indicator of respiratory muscle function and has been linked to various parenchymal biomarkers, such as airflow limitation severity and diffusing capacity for carbon monoxide, which are widely used in diagnosing parenchymal diseases, including asthma and chronic obstructive pulmonary disease. Pectoralis muscle segmentation is a method for measuring muscle volume and mass for various applications. The segmentation method is based on deep-learning techniques that combine a muscle area detection model and a segmentation model. The training dataset for the detection model comprised multichannel images of patients, whereas the segmentation model was trained on 7,796 cases of the computed tomography (CT) image dataset of 1,841 patients. The dataset was expanded incrementally through an active learning process. The performance of the model was evaluated by comparing the segmentation results with manual annotations by radiologists and the volumetric differences between the CT image datasets of the same patients. The results indicated that the machine learning model is promising in segmenting the pectoralis major muscle, with good agreement between the automatic segmentation and manual annotations by radiologists. The training accuracy and loss values of the validation set were 0.9954 and 0.0725, respectively, and for segmentation, the loss value was 0.0579. This study shows the potential clinical usefulness of the machine learning model for pectoralis major muscle segmentation as a quantitative biomarker for various parenchymal and muscular diseases.

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Attenuation profile matching: An accurate and scan parameter‐robust measurement method for small airway dimensions in low‐dose CT scans

Abstract: Our proposed APM method has the potential to overcome the resolution limitations of current CT systems and accurately determine the small airway dimensions in COPD patients.

Cited by 4 publications

References 35 publications

Quantification of airway dimensions using a high‐resolution CT scanner: A phantom study

Quantification of airway dimensions using a high‐resolution CT scanner: A phantom study

Reproducibility of an airway tapering measurement in computed tomography with application to bronchiectasis

Deep learning-based pectoralis muscle volume segmentation method from chest computed tomography image using sagittal range detection and axial slice-based segmentation

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