Dynamic OSEM Parameter Adjustment for PET Image Reconstruction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The long reconstruction time of medical images in nuclear medicine imaging apparatuses, such as PET-CT systems, using successive approximation methods like MLEM and OSEM, degrades examination efficiency due to high computational requirements, especially for whole-body scans.
Innovation Solution
A medical image diagnosis apparatus that dynamically adjusts parameters like iteration number and subset number in the OSEM algorithm based on scanning region information, allowing for optimized reconstruction time and image quality by prioritizing scanning regions during image reconstruction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If successive approximation method (MLEM/OSEM) is used for image reconstruction, then noise tolerance and image quality are improved, but reconstruction time increases significantly
Solution Approach 1:
The patent divides the body into multiple scanning regions (e.g., head, chest, abdomen, pelvis) and applies different reconstruction parameters to each region. This segmentation allows the system to process different body parts with appropriate parameter sets, reducing overall reconstruction time while maintaining image quality in critical regions.
Solution Approach 2:
The patent applies different reconstruction parameters (such as iteration number, subset number, or convergence criteria) to different scanning regions based on their diagnostic priorities. Critical regions receive higher processing quality while less critical regions use optimized-for-speed parameters, achieving local optimization of image quality versus time trade-off.
2Ease of operation
If fixed reconstruction parameters are used for all scanning regions, then processing simplicity is maintained, but examination efficiency decreases due to uniform long processing time
Solution Approach 1:
The patent implements dynamic parameter adjustment where reconstruction parameters are automatically modified based on the scanning region being processed. The system transitions from static fixed parameters to dynamic adaptive parameters that change according to region-specific requirements, thereby improving examination efficiency without significantly complicating operation.
Solution Approach 2:
The system performs self-configuration of reconstruction parameters based on pre-defined region characteristics or automatic detection of scanning regions. This self-service mechanism eliminates the need for manual parameter setting by the operator while maintaining optimization for each region, thus improving productivity without reducing ease of operation.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach reduces the overall reconstruction time for PET images while maintaining diagnostic image quality, thereby enhancing examination efficiency by tailoring processing parameters to specific body regions and their diagnostic priorities.
Implementation Method 1
the nuclear medicine imaging apparatus detects a gamma ray emitted from an isotope or a labeled compound selectively received in the body tissues using a detector
Data Source
AI summary
In a nuclear medicine imaging apparatus as a medical image diagnosis apparatus according to one embodiment, a PET detector is configured to detect a gamma ray emitted from a nuclide introduced into a body of a subject. A PET image reconstruction unit is configured to reconstruct a nuclear medicine image (PET image) as a medical image from the gamma ray projection data created based on the gamma ray detected by the PET detector using successive approximation. A controller is configured to control the PET image reconstruction unit to change the parameter used in the successive approximation depending on information regarding the scanning region in the body of the subject.