Convolutional neural networks are an emerging tool in medical imaging. Conventional CNNs accept an image as input and return a task-specific output (e.g., a filtered image, a disease probability). Conventional CNNs struggle to generalize or perform poorly when image data alone is insufficient to solve the problem. We propose three ways to incorporate relevant scan information into a CNN. The value of this method is demonstrated on rSOS image denoising, a previously unstable problem.
How to access this content:
For one year after publication, abstracts and videos are only open to registrants of this annual meeting. Registrants should use their existing login information. Non-registrant access can be purchased via the ISMRM E-Library.
After one year, current ISMRM & ISMRT members get free access to both the abstracts and videos. Non-members and non-registrants must purchase access via the ISMRM E-Library.
After two years, the meeting proceedings (abstracts) are opened to the public and require no login information. Videos remain behind password for access by members, registrants and E-Library customers.
Keywords