A novel strategy to develop deep learning for image super-resolution using original ultra-high-resolution computed tomography images of lung as training dataset

Abstract

Purpose: To improve the image quality of inflated fixed cadaveric human lungs by utilizing ultra-high-resolution computed tomography (U-HRCT) as a training dataset for super-resolution processing using deep learning (SR-DL).

Materials and methods: Image data of nine cadaveric human lungs were acquired using U-HRCT. Three different matrix images of U-HRCT images were obtained with two acquisition modes: normal mode (512-matrix image) and super-high-resolution mode (1024- and 2048-matrix image). SR-DL used 512- and 1024-matrix images as training data for deep learning. The virtual 2048-matrix images were acquired by applying SR-DL to the 1024-matrix images. Three independent observers scored normal anatomical structures and abnormal computed tomography (CT) findings of both types of 2048-matrix images on a 3-point scale compared to 1024-matrix images. The image noise values were quantitatively calculated. Moreover, the edge rise distance (ERD) and edge rise slope (ERS) were also calculated using the CT attenuation profile to evaluate margin sharpness.

Results: The virtual 2048-matrix images significantly improved visualization of normal anatomical structures and abnormal CT findings, except for consolidation and nodules, compared with the conventional 2048-matrix images (p < 0.01). Quantitative noise values were significantly lower in the virtual 2048-matrix images than in the conventional 2048-matrix images (p < 0.001). ERD was significantly shorter in the virtual 2048-matrix images than in the conventional 2048-matrix images (p < 0.01). ERS was significantly higher in the virtual 2048-matrix images than in the conventional 2048-matrix images (p < 0.01).

Conclusion: SR-DL using original U-HRCT images as a training dataset might be a promising tool for image enhancement in terms of margin sharpness and image noise reduction. By applying trained SR-DL to U-HRCT SHR mode images, virtual ultra-high-resolution images were obtained which surpassed the image quality of unmodified SHR mode images.

Keywords: Artificial intelligence; Cadaveric lung; Convolutional neural networks; Deep learning; Ultra-high-resolution computed tomography.

Fig. 1

The raw data were acquired in the U-HRCT NR mode (detector 0.5 × 0.5 mm), and the 512 × 512 matrix images were then reconstructed. Raw data were also acquired in U-HRCT SHR mode (detector 0.25 × 0.25 mm), and the 1024 × 1024 matrix images and 2048 × 2048 matrix images (Conventional 2048) were then reconstructed. The SR-DL was trained using the 512 × 512 matrix images as low-resolution data and the 1024 × 1024 matrix images as high-resolution data. Then, the 1024 × 1024 matrix images were applied to this trained SR-DL to construct the 2048 × 2048 matrix images (Virtual 2048). U-HRCT ultra-high-resolution computed tomography, NR normal resolution, SHR super-high resolution, SR-DL super-resolution processing using deep learning

Fig. 2

a A straight line that traverses the bronchiolar wall almost vertically. b The CT attenuation profile along the straight line shown in (a). CT computed tomography

Fig. 3

a The conventional 2048-matrix image and b the virtual 2048-matrix image. The interlobular septal thickening (white arrow) is more clearly seen in (b) than in (a)

Fig. 4

a The conventional 2048-matrix image and b the virtual 2048-matrix image. The fine reticular opacity in the ground-glass opacity is more conspicuous in (b) than in (a). There is also a subpleural nodule (white arrow)

Fig. 5

a The conventional 2048-matrix image and b the virtual 2048-matrix image. The consolidation is drawn to the same extent in (a) and (b), but the edge of consolidation and air bronchogram are slightly more conspicuous in (b) than in (a)

Fig. 6

a Conventional 2048-matrix image of GGO. b FFT power spectrum of (a). c Virtual 2048-matrix image of GGO. d FFT power spectrum of (c). e Subtraction image of (d–b). f Conventional 2048-matrix image of consolidation. g FFT power spectrum of (f). h Virtual 2048-matrix image of consolidation. i FFT power spectrum of (h). j Subtraction image of (i–g). GGO ground-glass opacity, FFT fast Fourier transform