  Recent advancements in deep learning for automated image processing and
classification have accelerated many new applications for medical image
analysis. However, most deep learning applications 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 for sinogram-space machine learning: body region identification and
intracranial hemorrhage (ICH) detection. Our proposed SinoNet 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 binary
diagnosis testing, triage, and in clinical settings 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.
