Applications of machine learning and deep learning in SPECT and PET imaging: General overview, challenges and future...

Area of Science:

• Nuclear Medicine
• Artificial Intelligence in Healthcare
• Medical Imaging Analysis

Background:

• Positron Emission Tomography (PET) and Single-Photon Emission Computed Tomography (SPECT) are crucial in nuclear medicine.
• Integrating advanced computational methods can significantly improve the utility of these imaging techniques.
• Machine Learning (ML) and Deep Learning (DL) offer powerful tools for analyzing complex medical data.

Purpose of the Study:

• To review the evolution and application of ML/DL algorithms in PET and SPECT imaging.
• To explore the potential of ML/DL in enhancing diagnostic and prognostic capabilities in nuclear imaging.
• To discuss challenges and future opportunities for ML/DL integration in clinical practice.

Main Methods:

• Comprehensive literature review focusing on ML and DL applications in PET and SPECT.
• Analysis of algorithm evolution and common applications within nuclear imaging.
• Discussion of implementation challenges (e.g., data standardization) and future directions (e.g., explainable AI).

Main Results:

• ML/DL algorithms are increasingly applied for optimizing image acquisition/reconstruction, biomarker identification, and multimodal fusion.
• These algorithms facilitate the development of systems for diagnosis, prognosis, and disease progression evaluation.
• ML/DL excel at analyzing complex patterns and extracting quantitative measures from imaging data.

Conclusions:

• The integration of ML/DL with PET and SPECT imaging significantly enhances precision and efficiency in diagnostics and treatment.
• Addressing challenges like data standardization and limited sample sizes is crucial for wider clinical adoption.
• Future advancements in areas like data augmentation and explainable AI will drive the development of more robust and transparent nuclear imaging systems.