Third party funded individual grant
Start date : 01.01.2023
End date : 31.12.2025
The majority of diagnostic medical
imaging pipelines follow the same principles: raw measurement data is acquired
by scanner hardware, processed by image reconstruction algorithms, and then
evaluated for pathology by human radiology experts. Under this paradigm, every step
has traditionally been optimized to generate images that are visually pleasing
and easy to interpret for human experts. However, raw sensor information that
could maximize patient-specific diagnostic information may get lost in this
process. This problem is amplified by recent developments in machine
learning for medical imaging. Machine learning has been used successfully in
all steps of the diagnostic imaging pipeline: from the design of data
acquisition to image reconstruction, to computer-aided diagnosis. So far, these
developments have been disjointed from each other. In this project, we will
fuse machine learning for image reconstruction and for image-based disease
localization, thus providing an end-to-end learnable image reconstruction and
joint pathology detection approach that operates directly on raw measurement
data. Our hypothesis is that this combination can maximize diagnostic accuracy
while providing optimal images for both human experts and diagnostic machine
learning models.