Quantitative validation of the severity of emphysema by multi-detector CT

Lung CT harmonization of paired reconstruction kernel images using generative adversarial networks

BACKGROUND

The kernel used in CT image reconstruction is an important factor that determines the texture of the CT image. Consistency of reconstruction kernel choice is important for quantitative CT-based assessment as kernel differences can lead to substantial shifts in measurements unrelated to underlying anatomical structures.

PURPOSE

In this study, we investigate kernel harmonization in a multi-vendor low-dose CT lung cancer screening cohort and evaluate our approach's validity in quantitative CT-based assessments.

METHODS

Using the National Lung Screening Trial, we identified CT scan pairs of the same sessions with one reconstructed from a soft tissue kernel and one from a hard kernel. In total, 1000 pairs of five different paired kernel types (200 each) were identified. We adopt the pix2pix architecture to train models for kernel conversion. Each model was trained on 100 pairs and evaluated on 100 withheld pairs. A total of 10 models were implemented. We evaluated the efficacy of kernel conversion based on image similarity metrics including root mean squared error (RMSE), peak signal-to-noise ratio (PSNR), and structural similarity index measure (SSIM) as well as the capability of the models to reduce measurement shifts in quantitative emphysema and body composition measurements. Additionally, we study the reproducibility of standard radiomic features for all kernel pairs before and after harmonization.

RESULTS

Our approach effectively converts CT images from one kernel to another in all paired kernel types, as indicated by the reduction in RMSE (p < 0.05) and an increase in the PSNR (p < 0.05) and SSIM (p < 0.05) for both directions of conversion for all pair types. In addition, there is an increase in the agreement for percent emphysema, skeletal muscle area, and subcutaneous adipose tissue (SAT) area for both directions of conversion. Furthermore, radiomic features were reproducible when compared with the ground truth features.

CONCLUSIONS

Kernel conversion using deep learning reduces measurement variation in percent emphysema, muscle area, and SAT area.

Ultra-low-dose CT combined with noise reduction techniques for quantification of emphysema in COPD patients: An intra-individual comparison study with standard-dose CT

PURPOSE

Phantom studies in CT emphysema quantification show that iterative reconstruction and deep learning-based noise reduction (DLNR) allow lower radiation dose. We compared emphysema quantification on ultra-low-dose CT (ULDCT) with and without noise reduction, to standard-dose CT (SDCT) in chronic obstructive pulmonary disease (COPD).

METHOD

Forty-nine COPD patients underwent ULDCT (third generation dual-source CT; 70ref-mAs, Sn-filter 100kVp; median CTDIvol 0.38 mGy) and SDCT (64-multidetector CT; 40mAs, 120kVp; CTDIvol 3.04 mGy). Scans were reconstructed with filtered backprojection (FBP) and soft kernel. For ULDCT, we also applied advanced modelled iterative reconstruction (ADMIRE), levels 1/3/5, and DLNR, levels 1/3/5/9. Emphysema was quantified as Low Attenuation Value percentage (LAV%, ≤-950HU). ULDCT measures were compared to SDCT as reference standard.

RESULTS

For ULDCT, the median radiation dose was 84 % lower than for SDCT. Median extent of emphysema was 18.6 % for ULD-FBP and 15.4 % for SDCT (inter-quartile range: 11.8-28.4 % and 9.2 %-28.7 %, p = 0.002). Compared to SDCT, the range in limits of agreement of emphysema quantification as measure of variability was 14.4 for ULD-FBP, 11.0-13.1 for ULD-ADMIRE levels and 10.1-13.9 for ULD-DLNR levels. Optimal settings were ADMIRE 3 and DLNR 3, reducing variability of emphysema quantification by 24 % and 27 %, at slight underestimation of emphysema extent (-1.5 % and -2.9 %, respectively).

CONCLUSIONS

Ultra-low-dose CT in COPD patients allows dose reduction by 84 %. State-of-the-art noise reduction methods in ULDCT resulted in slight underestimation of emphysema compared to SDCT. Noise reduction methods (especially ADMIRE 3 and DLNR 3) reduced variability of emphysema quantification in ULDCT by up to 27 % compared to FBP.

Correlation between quantitative multi-detector computed tomography lung analysis and pulmonary function tests in chronic obstructive pulmonary disease patients

Background

Chronic obstructive pulmonary disease [COPD] is a very common disease in developing as well as in developed countries. Using CT has a growing interest to give a phenotypic classification helping the clinical characterization of COPD patients. So, the aim of the present study was to evaluate whether there was a significant correlation between quantitative computed tomography lung analysis and pulmonary function tests in chronic obstructive pulmonary disease patients.

Results

The study included 50 male patients with a mean age of 62.82 years ± 8.65 years standard deviation [SD]. Significant correlation was found between the pulmonary function tests [FEV1 and FEV1/FVC ratio], and all parameters of quantitative assessment with – 950 HU [the percentage of low-attenuation areas (% LAA)]. Pulmonary function tests according to GOLD [Global Initiative for Chronic Obstructive Lung Disease] guidelines revealed that 4% had normal pulmonary function, 8% had mild obstructive defect, 32% had moderate obstructive defect, 26% had severe obstructive defect, and 30% had very severe obstructive defect.

Conclusion

Automated CT densitometry defining the emphysema severity was significantly correlated with the parameters of pulmonary function tests and providing an alternative, quick, simple, non-invasive study for evaluation of emphysema severity. Its main importance was the determination of the extent and distribution of affected emphysematous parts of the lungs especially for selecting the patients suitable for the lung volume reduction surgery.