Artificial intelligence (AI)-based standardized uptake value (SUV) correction and variation assessment for positron emission tomography (PET)

A non-transitory computer-readable medium storing instructions readable and executable by at least one electronic processor to perform an imaging method, the method comprising:

• reconstructing emission imaging data to generate an emission image of a lesion;
• converting intensity values of voxels of the emission image to standardized uptake values SUV values) for the lesion, wherein the converting of intensity values of the emission image to at least one SUV value for the lesion includes converting an intensity value I to a corresponding SUV value according to:
• SUV
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• where i is the index of a voxel of the PET image, vi is the value of the voxel i expressed as a radiotracer activity concentration in the tissue at voxel i in the image being converted to SUV values, D is a radiopharmaceutical dose, M is a body mass or weight of the patient, t is a wait time between administration of the radiopharmaceutical and the PET imaging data acquisition, and t1/2 is a half-life of the radiopharmaceutical;
• processing input data using a regression neural network to output an SUV correction factor for the lesion, wherein the input data includes at least two of (i) image data comprising the emission image or a feature vector representing the emission image, (ii) the at least one SUV value, (iii) a size of the lesion, and (iv) reconstruction parameters used in the reconstructing;
• and
• controlling a display device to display at least one of (I) the SUV correction factor and (II) a corrected SUV value generated by applying the SUV correction factor to the at least one SUV value.

An imaging system, comprising:

• an image acquisition device configured to obtain emission imaging data of a patient;
• and
• at least one electronic processor programmed to:
• reconstruct emission imaging data to generate an emission image of a lesion;
• convert intensity values of voxels of the emission image to standardized uptake values (SUV values) for the lesion, the SUV values being computed as a function of a ratio D/M of a radiopharmaceutical dose D, to a body mass or weight M of the patient and a ratio t/t1/2 of a wait time t between administration of the radiopharmaceutical and the PET imaging data acquisition, and a half-life t1/2 of the radiopharmaceutical;
• process input data using a regression neural network to output an SUV correction factor for the lesion, wherein the input data includes at least three of (i) image data comprising the emission image or a feature vector representing the emission image, (ii) the at least one SUV value, (iii) a size of the lesion, and (iv) reconstruction parameters used in the reconstructing;
• generate a confidence value for the SUV correction factor by repeating the processing with one or more values of the input data varied to perform a sensitivity analysis of the SUV correction factor on the one or more values;
• and
• control a display device to display at least one of (I) the SUV correction factor;
• (II) a corrected SUV value generated by applying the SUV correction factor to the at least one SUV value;
• and (III) the generated confidence value.