Pseudo Computed Tomography estimation from brain MRI using anatomic signature and joint dictionary learning

Abstract

Most research on using pseudo-computed tomography (pCT) on brain-imaging techniques relies on in-house methods. As performance as a whole increase, they pay particular attention when using MRI imaging. Methodologies for predicting CT values from MRI data are needed in radiation treatment (RT). Although the employment of dictionary-learning-based approaches for defining picture patches has not been considered, it has been found that Deep Learning (DL) offers increased opportunities in the medical domains. The stages of this paper CT estimation from MRI using Anatomic Signature and Joint Dictionary Learning (ASJDL) are as follows: a) data gathering from the RIRE image data and b) image pre-processing to remove anomalies. c) Using Gabor Filters for feature extraction to extract significant features d) The choice of anatomic signature traits is used to identify strong, illuminating characteristics that classify and identify objects. e) Development of the pCT through shared dictionary learning. Using cutting-edge techniques like Accelerated Simplified Swarm Optimization (ASSO), Particle Swarm Optimization (PSO), Simplified Swarm Optimization (SSO), SLA12, Intensity-based, Fast-patch based, and Coupled dictionary over measures like peak S/N ratio, MAE, normalized cross-correlation, SSIM, Accuracy, and Computation time, the evaluation of both feature selection and classification methods is compared. With values of 23.825.08 (PSNR), 83.327.05 (MAE(HU)), 0.920.03 (NCC), and 0.860.03 (SSIM), the suggested framework (ASJDL) performs better than all other methods.