Use of the Spectral Iodine Map in the Assessment of Chronic Pulmonary Thromboembolism
Lung scintigraphy is considered the technique of choice in the evaluation of chronic pulmonary thromboembolism. Technical advances in computed tomography such as dual energy (spectral) have allowed high quality images to be obtained. As an example, a CT scan is shown as a dual-energy patient case, who underwent both scintigraphy and deep spectral learning, not only was a lower radiation dose observed on scintigraphy, but also provided anatomical data on perfusion defect, essential for surgical planning and also highlighted other areas of perfusion alteration that were not clearly identified on V/Q scintigraphy. Therefore, we bring this initial experience with the use of Spectral Deep Learning as a possibility to combine anatomical and perfusion assessment in the same acquisition, reducing dose and possible costs.
Use of the Spectral Iodine Map in the Assessment of Chronic Pulmonary Thromboembolism
-
DOI: https://doi.org/10.22533/at.ed.15946624040710
-
Palavras-chave: tomography; thromboembolism; dual energy; scintigraphy
-
Keywords: tomography; thromboembolism; dual energy; scintigraphy
-
Abstract:
Lung scintigraphy is considered the technique of choice in the evaluation of chronic pulmonary thromboembolism. Technical advances in computed tomography such as dual energy (spectral) have allowed high quality images to be obtained. As an example, a CT scan is shown as a dual-energy patient case, who underwent both scintigraphy and deep spectral learning, not only was a lower radiation dose observed on scintigraphy, but also provided anatomical data on perfusion defect, essential for surgical planning and also highlighted other areas of perfusion alteration that were not clearly identified on V/Q scintigraphy. Therefore, we bring this initial experience with the use of Spectral Deep Learning as a possibility to combine anatomical and perfusion assessment in the same acquisition, reducing dose and possible costs.
- Henrique Junior Cirino
- Jacqueline Kioko Nishimura Matsumoto
- Angela dos Santos Marin
- Eduardo Kaiser Ururahy Nunes Fonseca
- César Higa Nomura