positron emission tomography (PET) is a noninvasive imaging technology able to assess the metabolic or functional state of healthy and/or pathological tissues. In clinical practice, PET data are usually acquired statically and normalized for the evaluation of the standardized uptake value (SUV). In contrast, dynamic PET acquisitions provide information about radiotracer delivery to tissue, its interaction with the target, and its physiological washout. The shape of the time activity curves (TACs) embeds tissue-specific biochemical properties. Conventionally, TACs are employed along with information about blood plasma activity concentration, i.e., the arterial input function, and tracer-specific compartmental models to obtain a full quantitative analysis of PET data. This method's primary disadvantage is the requirement for invasive arterial blood sample collection throughout the whole PET scan. In this study, we employ a variety of deep learning models to illustrate the diagnostic potential of dynamic PET acquisitions of varying lengths for discriminating breast cancer lesions in the absence of arterial blood sampling compared to static PET only. Our findings demonstrate that the use of TACs, even in the absence of arterial blood sampling and even when using only a share of all timeframes available, outperforms the discriminative ability of conventional SUV analysis.

Inglese, M., Ferrante, M., Duggento, A., Boccato, T., Toschi, N. (2023). Spatiotemporal Learning of Dynamic Positron Emission Tomography Data Improves Diagnostic Accuracy in Breast Cancer. IEEE TRANSACTIONS ON RADIATION AND PLASMA MEDICAL SCIENCES, 7(6), 630-637 [10.1109/TRPMS.2023.3268361].

Spatiotemporal Learning of Dynamic Positron Emission Tomography Data Improves Diagnostic Accuracy in Breast Cancer

Inglese M.;Ferrante M.;Duggento A.;Toschi N.
2023-01-01

Abstract

positron emission tomography (PET) is a noninvasive imaging technology able to assess the metabolic or functional state of healthy and/or pathological tissues. In clinical practice, PET data are usually acquired statically and normalized for the evaluation of the standardized uptake value (SUV). In contrast, dynamic PET acquisitions provide information about radiotracer delivery to tissue, its interaction with the target, and its physiological washout. The shape of the time activity curves (TACs) embeds tissue-specific biochemical properties. Conventionally, TACs are employed along with information about blood plasma activity concentration, i.e., the arterial input function, and tracer-specific compartmental models to obtain a full quantitative analysis of PET data. This method's primary disadvantage is the requirement for invasive arterial blood sample collection throughout the whole PET scan. In this study, we employ a variety of deep learning models to illustrate the diagnostic potential of dynamic PET acquisitions of varying lengths for discriminating breast cancer lesions in the absence of arterial blood sampling compared to static PET only. Our findings demonstrate that the use of TACs, even in the absence of arterial blood sampling and even when using only a share of all timeframes available, outperforms the discriminative ability of conventional SUV analysis.
2023
Pubblicato
Rilevanza internazionale
Articolo
Esperti anonimi
Settore FIS/07
Settore ING-INF/06
English
4-D convolutions
arterial input function
deep learning
dynamic positron emission tomography (PET)
long short-term memory (LSTM)
Inglese, M., Ferrante, M., Duggento, A., Boccato, T., Toschi, N. (2023). Spatiotemporal Learning of Dynamic Positron Emission Tomography Data Improves Diagnostic Accuracy in Breast Cancer. IEEE TRANSACTIONS ON RADIATION AND PLASMA MEDICAL SCIENCES, 7(6), 630-637 [10.1109/TRPMS.2023.3268361].
Inglese, M; Ferrante, M; Duggento, A; Boccato, T; Toschi, N
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2108/345397
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