Patient motion is frequently a problem in mammography, especially when the x-ray exposure is long, resulting in image
quality degradation. At present, patient motion can only be identified by inspecting the image subjectively after image
acquisition. As digital breast tomosynthesis (DBT) takes longer time to complete the data acquisition than conventional
mammography, there is more chance for patient motion to happen in DBT. Therefore it is important to understand the
potential motion problem in DBT and incorporate a design to minimize it. In this paper we present an automatic method
to detect patient motions in DBT. The method is developed based on an understanding that, features of breast should
move along predictable trajectory in a time-series of projection measurements; deviations from it are linked to patient
motion. Motion distance is estimated by analyzing skin lines and large calcifications (if exist) in all projection images
and then a motion score is derived for a DBT scan. Effectiveness and robustness of this method will be demonstrated
with clinical data, together with discussions on different motion patterns observed clinically. The impacts of this work
could be far-reaching. It allows real-time detection and objective evaluation of patient motions, applicable to all breasts.
Patient with severe motion can be re-scanned immediately before leaving the room. Data with moderate motions can go
through additional targeted image processing to minimize motion artifacts. It also enables a powerful tool to evaluate
and optimize different DBT designs to minimize the patient motion problem. Besides, this method can be extended to
other imaging modalities, e.g. breast CT, to study patient motions.