  Manual prescription of the field of view (FOV) by MRI technologists is
variable and prolongs the scanning process. Often, the FOV is too large or
crops critical anatomy. We propose a deep-learning framework, trained by
radiologists' supervision, for automating FOV prescription. An intra-stack
shared feature extraction network and an attention network are used to process
a stack of 2D image inputs to generate output scalars defining the location of
a rectangular region of interest (ROI). The attention mechanism is used to make
the model focus on the small number of informative slices in a stack. Then the
smallest FOV that makes the neural network predicted ROI free of aliasing is
calculated by an algebraic operation derived from MR sampling theory. We
retrospectively collected 595 cases between February 2018 and February 2022.
The framework's performance is examined quantitatively with intersection over
union (IoU) and pixel error on position, and qualitatively with a reader study.
We use the t-test for comparing quantitative results from all models and a
radiologist. The proposed model achieves an average IoU of 0.867 and average
ROI position error of 9.06 out of 512 pixels on 80 test cases, significantly
better (P<0.05) than two baseline models and not significantly different from a
radiologist (P>0.12). Finally, the FOV given by the proposed framework achieves
an acceptance rate of 92% from an experienced radiologist.
