Dynamic Frame Reconstruction Using Group-Specific Scatter Estimation

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Solution Overview

Problem

Clinical imaging processes, particularly in nuclear medicine, face significant computational challenges due to the need for scatter estimates and random estimates during dynamic frame reconstruction, leading to high computational costs and reduced image quality.

Innovation Solution

An image processing method that clusters similar frames into groups and uses a single frame group-specific reconstruction parameter, such as scatter estimation, to reduce computational demands and improve image quality.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If scatter estimates and random estimates are performed during reconstruction of each dynamic frame, then image quality is improved, but computational cost increases significantly

Engineering Contradiction:

Improveimage qualityVSAvoidcomputational cost

Core Design Contradiction:

Measurement precisionVSPower

Solution Approach 1:

The patent segments the dynamic frames into multiple frame groups based on temporal proximity and similarity of scatter/random estimates. By performing scatter and random estimates only for representative frames within each group rather than every frame, the computational burden is divided and reduced while maintaining image quality through selective application of these estimates.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The patent performs scatter and random estimates in advance for selected representative frames before final image reconstruction. These pre-computed estimates are then applied to multiple frames within each group, eliminating the need to repeat these computationally intensive operations for every frame while still improving image quality.

Inventive Principle:

Principle #10Preliminary action

2Measurement precision

If scatter estimates and random estimates are performed during reconstruction of each dynamic frame, then image quality is improved, but reconstruction time increases

Engineering Contradiction:

Improveimage qualityVSAvoidreconstruction time

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The patent divides the sequence of dynamic frames into multiple frame groups, performing scatter and random estimates only for representative frames in each group. This segmentation approach reduces the total number of times these estimates must be computed, thereby reducing reconstruction time while maintaining image quality through the selective application of estimates to multiple frames within each group.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The patent implements periodic scatter and random estimates by selecting representative frames at specific intervals (e.g., first and last frames of each frame group) rather than continuously for every frame. This periodic approach maintains image quality by periodically updating estimates while significantly reducing the overall reconstruction time compared to continuous per-frame estimation.

Inventive Principle:

Principle #19Periodic action

3Productivity

If frame group-specific reconstruction parameters are used, then computational time is reduced, but measurement precision may be affected

Engineering Contradiction:

Improvecomputational timeVSAvoidscatter estimation accuracy

Core Design Contradiction:

ProductivityVSMeasurement precision

Solution Approach 1:

The patent applies local quality by using frame group-specific reconstruction parameters (scatter and random estimates) that are tailored to each local group of frames rather than using a single global parameter for all frames. Each frame group receives parameters optimized for its specific temporal and spatial characteristics, maintaining scatter estimation accuracy while reducing computational time compared to per-frame estimation.

Inventive Principle:

Principle #3Local quality

Solution Approach 2:

The patent implements dynamics by adaptively determining frame groups based on the temporal proximity and similarity of scatter and random estimates. The frame group boundaries and representative frame selections are dynamically adjusted to ensure that groups with similar characteristics share parameters, preserving measurement precision while enabling computational efficiency through parameter sharing within each dynamic group.

Inventive Principle:

Principle #15Dynamics

Data Source

PatentEP4318400B1Image processing apparatus, image processing method and program

Publication Date: 2026.02.18 CANON MEDICAL SYST CORP

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AI summary

An image processing apparatus according to an embodiment includes processing circuitry (9070). The processing circuitry (9070) is configured to acquire list-mode data corresponding to a plurality of detection times, generate a plurality of frames based on the list-mode data, assign at least one frame of the plurality of frames into a first frame group based on a similarity of each frame, assign at least one frame of the plurality of frames into a second frame group based on a similarity of each frame, estimate first and second frame group-specific reconstruction parameters based on frames in first frame group, and frames in the second frame group, respectively, wherein the first and second frame group-specific reconstruction parameters are different, reconstruct a first set of frame data from any frame of the first frame group based on the first frame group-specific reconstruction parameter, and reconstruct a second set of frame data from any frame of the second frame group based on the second frame group-specific reconstruction parameter.