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Ultrasound computed tomography (USCT) is an emerging imaging modality that holds great promise for breast imaging. Full-waveform inversion (FWI)-based image reconstruction methods incorporate accurate wave physics to produce high spatial resolution quantitative images of speed of sound or other acoustic properties of breast tissues from USCT measurement data. However, FWI is computationally burdensome and requires a good initial guess of the speed of sound distribution due to the nonconvex nature of the underlying optimization problem (cycle-skipping). Alternatively, the use of a simplified linear model, such as the Born approximation, allows the image reconstruction problem to be formulated as a convex optimization problem, but sacrifices accuracy. This work proposes utilizing a convolutional neural network (CNN) to correct pressure data and accurately reconstruct images using a simplified …