Deep learning approaches for CT image reconstruction: Advancements and clinical implications

Abstract

Background: CT is a trusted imaging modality for diagnosing disease. The quality of the CT image plays the most important role in disease prediction. Images will be degraded by noise, blur, and other artifacts. Therefore, the enhancement and reconstruction of CT images are required before processing for prediction. Enhancements are only cosmetic changes in images, but reconstruction provides well-defined models for reconstruction. In this paper, we have compared the performance of these four CT brain reconstruction and enhancement techniques: Filtered back projection (FBP), Iterative reconstruction, Generative Adversarial Network (GAN), and CNN autoencoder, by adding Speckle and Poisson noise.

Objectives: The possible objective statements of this study will cover a broad spectrum of developments in medical imaging technology. These are aimed at enhancing diagnostic test quality, patient safety, and overall care outcomes through innovative imaging methods and data analysis. The suggested objectives involve integrating an artificial intelligence algorithm to enhance them, thereby improving patient care and diagnostic accuracy across numerous medical fields. In this experiment, the CT brain stroke dataset was obtained from the Kaggle database and evaluated using performance metrics (PSNR, SSIM, and MSE) across the normal, ischemia, bleeding, and external test folders, with Poisson and Speckle noise added using the GAN, CNN autoencoder, iterative, and FBP techniques.

Materials and methods: The CT brain stroke dataset contains CT images organized in four folders: normal, ischemia, bleeding, and external test. These folders can be used to evaluate performance metrics. The methods applied include: Convolutional Neural Network (CNN) autoencoder, Generative Adversarial Network (GAN), iterative reconstruction, and FBP, each implemented with hyper-tuned parameters. Experimental results calculate PSNR, SSIM, and MSE after adding Poisson and Speckle noise to test the above deep learning algorithms.

Results: The lowest image quality was achieved for FBP for all datasets and both noise types. When the noise was Poisson, the performance of iterative reconstruction was the best, with PSNR exceeding 37 dB, SSIM exceeding 0.972, and MSE as low as 0.0002. On adding Poisson noise, the GAN achieved higher PSNR values and SSIM values up to 32.54 dB and 0.938, respectively, and with low errors in reconstruction, except for the external test folder. The CNN autoencoder (CNN-AE) also yielded significant improvements over FBP, achieving competitive PSNR and SSIM scores across all datasets.

Conclusion: The results indicate that the quality of CT images can be significantly improved using advanced deep learning and Iterative reconstruction methods compared with conventional FBP. It is observed that iterative reconstruction yields the best results for images corrupted by Poisson noise, whereas GANs perform best for images corrupted by Speckle noise. Therefore, reconstruction performance depends on the nature of the noise present in the CT images.