Machine-learning methods are being actively developed for computed imaging systems like MRI. However, these methods occasionally introduce false, unexplainable structures in images, known as hallucinations, that can lead to incorrect diagnoses.

Researchers at the Beckman Institute for Advanced Science and Technology and the Computational Imaging Science Laboratory have defined a mathematical framework for identifying hallucinations, a first step toward reducing their frequency.

This work, “On hallucinations in tomographic image reconstruction,” is published in IEEE Transactions on Medical Imaging in a special issue on machine learning methods for image reconstruction.
Most modern medical imaging devices — such as MRI, computed tomography, and PET — do not record images directly. Instead, they employ a computational procedure known as image reconstruction. Researchers worldwide are seeking to develop improved image reconstruction methods that use deep learning to circumvent the limitations of traditional methods. Deep learning, like all machine-learning methods, allows algorithms to self-improve by learning from characterized datasets. Advanced DL-based methods can reduce imaging scan times and radiation doses. Although DL-based image reconstruction methods hold great potential for such purposes, they sometimes produce images that appear plausible, but contain false structures that may confound a medical diagnosis.

“This work is an excellent example of how our research group is making foundational contributions to the rapidly evolving field of imaging science,” said Mark Anastasio, the principal investigator of this study and head of the Department of Bioengineering at the University of Illinois Urbana-Champaign.

“Because our framework for analyzing hallucinations in biomedical images will enable researchers to quantitatively assess their image reconstruction methods in new and meaningful ways, we expect the impact of our study to be large."

Lead authors Varun Kelkar and Sayantan Bhadra, both graduate students in Anastasio’s group, anticipate that hallucination maps will help researchers and radiologists identify hallucinations and assess how detrimental they might be. Ultimately, this leads to a deeper understanding of how DL reconstruction methods should be trained to prevent patient misdiagnosis.

“An effective reconstruction method is based on understanding the physics of the imaging system, but also uses realistic assumptions about how an object should appear,” Bhadra said. “DL-based image reconstruction methods are about designing the interplay of these elements by … training deep neural networks on large databases of previously existing clinical images.”

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