Abstract
Recent advancements in deep learning for automated image processing and classification have accelerated many new applications for medical image analysis. However, most deep learning algorithms have been developed using reconstructed, human-interpretable medical images. While image reconstruction from raw sensor data is required for the creation of medical images, the reconstruction process only uses a partial representation of all the data acquired. Here, we report the development of a system to directly process raw computed tomography (CT) data in sinogram-space, bypassing the intermediary step of image reconstruction. Two classification tasks were evaluated for their feasibility of sinogram-space machine learning: body region identification and intracranial hemorrhage (ICH) detection. Our proposed SinoNet, a convolutional neural network optimized for interpreting sinograms, performed favorably compared to conventional reconstructed image-space-based systems for both tasks, regardless of scanning geometries in terms of projections or detectors. Further, SinoNet performed significantly better when using sparsely sampled sinograms than conventional networks operating in image-space. As a result, sinogram-space algorithms could be used in field settings for triage (presence of ICH), especially where low radiation dose is desired. These findings also demonstrate another strength of deep learning where it can analyze and interpret sinograms that are virtually impossible for human experts.
Highlights
Most prior work using deep learning algorithms has focused on image analysis of reconstructed images or as an alternative approach to image reconstruction
We evaluated twelve different classification models developed by training Inception-v318 on reconstructed computed tomography (CT) images and SinoNet with sinograms (Table 1, Methods)
Two-dimensional (2D) parallel-beam Radon transform was applied to the linear attenuation coefficients (LAC) slices (512 × 512 pixels) to generate a fully-sampled sinogram with 360 projections and 729 detector pixels (‘sino360x729’), which was uniformly subsampled in the horizontal direction and averaged in vertical direction by factors of 3 and 9 to obtain moderately sampled sinograms with 120 views by 240 pixels (‘sino120x240’) and sparsely sampled sinograms with 40 views by 80 pixels (‘sino40x80’)
Summary
Most prior work using deep learning algorithms has focused on image analysis of reconstructed images or as an alternative approach to image reconstruction. We. proposed a customized convolutional neural network (CNN) called SinoNet, optimized it for interpreting sinograms, and demonstrated its potential by comparing its performance to pre-existing system based on other CNN architectures using reconstructed CT images. Proposed a customized convolutional neural network (CNN) called SinoNet, optimized it for interpreting sinograms, and demonstrated its potential by comparing its performance to pre-existing system based on other CNN architectures using reconstructed CT images This approach accelerates edge computing by making it possible to identify critical findings rapidly from the raw data without time-consuming image reconstruction processes. This could enable us to develop simplified scanner hardware for the direct detection of critical findings through SinoNet alone
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