Image reconstruction under multiple light scattering is crucial in a number of applications such as diffraction tomography. The reconstruction problem is often formulated as a nonconvex optimization, where a nonlinear measurement model is used to account for multiple scattering and regularization is used to enforce prior constraints on the object. In this paper, we propose a powerful alternative to this optimization-based view of image reconstruction by designing and training a deep convolutional neural network that can invert multiple scattered measurements to produce a high-quality image of the refractive index. Our results on both simulated and experimental datasets show that the proposed approach is substantially faster and achieves higher imaging quality compared to the state-of-the-art methods based on optimization.

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Primary tumor resection (PTR) is recommended for patients with unresectable stage IV colorectal cancer (CRC) who present with symptoms related to their primary tumor. However, the survival benefit of PTR for asymptomatic patients is controversial. We investigated the change in PTR rates and the contribution of PTR to survival in patients with unresectable stage IV CRC over the past two decades in the United States. Clinicopathological factors and long-term survival were compared for 44 514 patients diagnosed with unresectable stage IV CRC from January 1, 1988, through December 31, 2010, who had or had not undergone PTR. Multivariable Cox regression and the instrumental variable method were used to identify independent factors for survival. Of the 44 514 patients with unresectable stage IV CRC, 27 931 (62.7%) had undergone PTR. The annual rate of PTR decreased from 74.4% to 50.2% diagnosed in 1988 and 2010, and the median overall survival increased for both PTR and non-PTR patients. Instrumental variable analyses revealed that PTR was associated with better overall, cancer-specific, and other-cause survival of patients with unresectable stage IV CRC.