Method for rigid motion correction of PET and MR images on PET/MR scanners
Inventors
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Assignees
Leland Stanford Junior University
MemberStanford UniversityStanford UniversityTodd Coleman is an associate professor of bioengineering at Stanford University. His research combines applied probability, physiology, and bioelectronics to develop methods and devices for monitoring and modulating nervous-system and other physiological activity, including brain and visceral-organ signals.
Todd Coleman is an associate professor of bioengineering at Stanford University. His research combines applied probability, physiology, and bioelectronics to develop methods and devices for monitoring and modulating nervous-system and other physiological activity, including brain and visceral-organ signals.
Publication Number
US-12733893-B2
Publication Date
2026-09-15
Expiration Date
Abstract
A method for combined PET/MR imaging performs a PET/MR scan using a PET/MR scanner. During the scan a frame is reconstructed and co-registered with a reference frame. The reference frame is dynamically updated throughout the PET/MR scan by incorporating most recently co-registered images from previously reconstructed frames.
Core Innovation
The invention describes combined PET/MR imaging that corrects for evolving tracer distribution during a scan. A plurality of PET frames is iteratively reconstructed, and each frame is co-registered with a reference frame. After each co-registration, the reference frame is adaptively regenerated and dynamically updated by incorporating multiple of the most recently co-registered images from previously reconstructed frames.
Tracer distribution changes during a scan, whereas a fixed reference frame does not represent those changes for frame co-registration. The adaptive reference utilizes recent co-registered images to address this issue and improve registration accuracy. The document also describes estimating patient motion from short PET frames and applying a motion transfer function to update MRI gradient waveforms.
Claims Coverage
The independent claim is claim 1. The claim set describes four inventive features: adaptive reference-frame updating, an ordered co-registration sequence, independent co-registration of sagittal sections, and motion-transfer-function-based MRI gradient updates.
Adaptive reference-frame updating
During a dynamic PET/MR scan, frames are iteratively reconstructed and each frame is co-registered with a reference frame. After each co-registration, the reference frame is adaptively regenerated and dynamically updated by incorporating multiple most recently co-registered images from previously reconstructed frames.
Ordered co-registration estimates
The co-registration estimates translation along the Z- and X-axes first, followed by translation along the Y-axis and rotation around the X-, Y-, and Z-axes.
Independent co-registration of sagittal sections
The reference frame is divided into two equal sections along a sagittal plane, and each section is independently co-registered.
Motion-transfer-function-based gradient updates
A motion transfer function is estimated from short PET frames and used in an MRI sequence to update gradient waveforms and correct patient motion.
The claims center on updating a PET/MR reference frame with recent co-registered images, with dependent features specifying co-registration, sagittal-section handling, and MRI gradient updates for patient-motion correction.
Stated Advantages
The adaptive reference improves registration as tracer distribution changes during the scan.
Updating MRI gradient waveforms using a motion transfer function corrects patient motion.
Documented Applications
Combined PET/MR imaging, including the described brain and knee studies.
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