Learned Hotelling observers for use with multi-modal data

Jason L. Granstedt; Fu Li; Umberto Villa; Mark A. Anastasio

doi:10.1117/12.2613138

4 April 2022 Learned Hotelling observers for use with multi-modal data

Jason L. Granstedt, Fu Li, Umberto Villa, Mark A. Anastasio

Author Affiliations +

Proceedings Volume 12035, Medical Imaging 2022: Image Perception, Observer Performance, and Technology Assessment; 1203512 (2022) https://doi.org/10.1117/12.2613138
Event: SPIE Medical Imaging, 2022, San Diego, California, United States

Conference Poster

Abstract

In the medical imaging field, task-based metrics of image quality have been advocated as a mean to evaluate the performance of imaging systems and/or reconstruction algorithms. One such way of obtaining these metrics is through a numerical observer. Although the Bayesian ideal observer is optimal by definition, it is frequently intractable and nonlinear. Therefore, linear approximations to the IO are sometimes used to obtain task-based statistics. The optimal linear observer for maximizing the signal-to-noise ratio (SNR) of the test statistic is the Hotelling Observer (HO). However, the computational cost for obtaining the HO increases with image size and becomes intractable for large scale images. In multimodal data, this further becomes an issue because each additional modality dramatically increases the size of the composite image. An alternative to obtaining the HO is approximating the test statistic using a feed-forward neural network (FFNN). However, these methods of learning the HO have not been evaluated on multi-modal data. In this work, a tractable learned multi-modal observer is implemented. The considered task is a signal-known-statistically/background known statistically binary signal detection task. A stylized operator representing an ultrasound computed tomography imaging system and numerical breast phantoms with speed of sound and attenuation modalities are considered. The considered signal is a microcalcification cluster with a random amplitude. It is demonstrated that the learned HO can closely approximate the HO for the considered task.

Conference Presentation

Citation Download Citation

Jason L. Granstedt, Fu Li, Umberto Villa, and Mark A. Anastasio "Learned Hotelling observers for use with multi-modal data", Proc. SPIE 12035, Medical Imaging 2022: Image Perception, Observer Performance, and Technology Assessment, 1203512 (4 April 2022); https://doi.org/10.1117/12.2613138

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

;

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE