Tag: pseudo-CT

Make Accurate Treatment Planning in Radiotherapy using Deep Learning

Last year, a published deepMRAC study in Radiology evaluated the feasibility of deep learning-based pseudo-CT generation in PET/MR attenuation correction, in which our AI team demonstrated the pseudo-CT generated by learning MR information could significantly improve PET reconstruction in PET/MR, leading to less than 1% uncertainty in brain FDG PET quantification. We investigated the feasibility, applicability, and robustness of deep learning-based pseudo-CT generation in MR-guided radiation therapy to seek the clinical value of deep learning-based pseudo-CT generation. With the new method published as deepMTP framework, we demonstrated the high clinical value of deep learning pseudo-CT in radiation therapy for saving radiation dose and providing high-quality treatment planning equivalent to the standard clinical method.

An example of a patient with a right frontal brain tumor adjacent to chiasm and right optic nerves. deepMTP provided a treatment plan with similar PTV and isodose lines around the tumor region compared with CT-based treatment plan (CTTP) in the fused MR and CT images (a, b). The plan was designed to avoid adjacent chiasm and optic nerves. The DVH (c) showed highly similar dose curves for the PTV, chiasm, and right optic nerve between CTTP (solid line) and deepMTP (dashed line).

We have shown that deep learning approaches applied to MR-based treatment planning in radiation therapy can produce comparable plans to CT-based methods. The further development and clinical evaluation of such approaches for MR-based treatment planning have potential value for providing accurate dose coverage and reducing treatment unrelated doses in radiation therapy, improving workflow for MR-only treatment planning, combined with the improved soft tissue contrast and resolution of MR. Our study demonstrates that deep learning approaches such as deepMTP will substantially impact future work in treatment planning in the brain and elsewhere in the body.

Accurate and Dose-Saving Positron Emission Tomography Imaging using Deep Learning

In a recent paper published in EJNMMI Physics, our AI team at the University of Wisconsin proposed a deep learning algorithm to address the challenge for simultaneous accurate and dose-saving positron emission tomography (PET) imaging.

PET is a non-invasive imaging modality that directly provides biomarkers for physiology. Accurate PET activity is calculated by reconstructing the photon signal emitted from the PET tracer after correcting scattering and signal decay when photons travel through tissue. The conventional method to perform such a correction requires additional transmission images, such as computed tomography (CT) images, thus leading to additional radiation to patients. In this work, our AI team invented a data-driven method to correct PET signals using a pseudo-CT image generated from the PET image itself using deep learning. This novel technique avoids acquiring additional transmission images, reduces the radiation dose, and increases imaging robustness against the subject motion. Meanwhile, our experiment in brain imaging demonstrated almost no significant difference between our deep learning method and clinical standard methods, with an average deviation of less than 1% in most brain regions.

Raw PET imageDeep learning pseudo-CT image for PET correctionReal CT image