Method and system for automated parametric mapping of brain metabolism

A system of using digital images for mapping metabolic activity within a selected anatomy of a subject, the system comprising:

• a computer having a processor connected to computer memory storing software that, when executed, performs computer instruction steps of a machine learning architecture comprising:
• collecting magnetic resonance image (MRI) data of the selected anatomy of the subject including a preferred anatomical portion of the selected anatomy:
• collecting dynamic positron emission tomography (dPET) data of the selected anatomy for the subject, in the time domain, with a tracer applied to the selected anatomy of the subject:
• co-registering MRI data frames with dPET data frames and storing a co-registered dPET volume of frames in the computer memory:
• applying the co-registered MRI data frames as inputs to a three-dimensional convolutional neural network (3D-CNN) that outputs localized data frames and a probability distribution for respective localized data frames, wherein the probability distribution corresponds to a preferred anatomical portion being visible in each of the respective localized data frames:
• saving, in the computer memory, visible data frames comprising the localized data frames having a selected probability of the preferred anatomical portion being visible therein:
• segmenting the preferred anatomical portions from the visible data frames and saving segmented data frames in the computer memory:
• calculating an image derived input function (IDIF) for blood flow into the preferred anatomical portions:
• calculating a model-corrected input function (MCIF) for blood flow using the IDIF:
• computing a Ki map for the preferred anatomical portions, wherein the Ki map illustrates influx of the tracer into the preferred anatomical portion.

A computer program product comprising:

• a non-transitory computer readable medium storing software that, when executed, performs computer instruction steps of a machine learning architecture comprising:
• collecting magnetic resonance image (MRI) data of the selected anatomy of the subject including a preferred anatomical portion of the selected anatomy:
• collecting dynamic positron emission tomography (dPET) data of the selected anatomy for the subject, in the time domain, with a tracer applied to the selected anatomy of the subject:
• co-registering MRI data frames with dPET data frames and storing a co-registered dPET volume of frames in the computer memory:
• applying the co-registered MRI data frames as inputs to a three-dimensional convolutional neural network (3D-CNN) that outputs localized data frames and a probability distribution for respective localized data frames, wherein the probability distribution corresponds to a preferred anatomical portion being visible in each of the respective localized data frames:
• saving, in the computer memory, visible data frames comprising the localized data frames having a selected probability of the preferred anatomical portion being visible therein;
• segmenting the preferred anatomical portions from the visible data frames and saving segmented data frames in the computer memory:
• calculating an image derived input function (IDIF) for blood flow into the preferred anatomical portions:
• calculating a model-corrected input function (MCIF) for blood flow using the IDIF:
• computing a Ki map for the preferred anatomical portions, wherein the Ki map illustrates influx of the tracer into the preferred anatomical portion.