3D Anatomy Scan Reformatting With Landmark Mask Alignment
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Solution Overview
Problem
Current clinical imaging techniques, such as MRI and CT, require repetitive scanning and manual reformatting of 3D data to obtain contiguous views of anatomical landmarks, which is time-consuming and sensitive to technician skill, leading to potential errors in diagnosis.
Innovation Solution
A system utilizing a pre-trained neural network to generate deep-learning estimated scan prescription masks, allowing for automated alignment and reformatting of 3D image data to provide continuous views of anatomical landmarks from a single scan, reducing scan time and improving accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If repetitive scanning from different acquisition planes is performed to obtain contiguous views of anatomical landmarks, then the quality and completeness of anatomical imaging is improved, but the scan time is significantly increased
Solution Approach 1:
The patent performs preliminary actions by acquiring a single 3D data stack with comprehensive anatomical coverage before reformatting. The 3D acquisition is performed in advance with sufficient field of view to include all desired anatomical landmarks, eliminating the need for repetitive scanning. The reformatting process then retrospectively generates the required views from this pre-acquired 3D data.
Solution Approach 2:
The patent transitions from multiple 2D acquisition planes to a single 3D volumetric acquisition. By acquiring data in three dimensions with isotropic or near-isotropic resolution, the system can retrospectively generate contiguous views of any anatomical landmark from the 3D data stack, eliminating the need for repetitive 2D scanning from different planes.
2Adaptability or versatility
If manual reformatting of 3D data is performed by technicians to obtain views of desired landmarks, then the flexibility to obtain specific anatomical views is improved, but the process becomes time-consuming and sensitive to technician skill
Solution Approach 1:
The patent implements self-service by using automated algorithms and software to perform the reformatting process without requiring technician intervention. The system automatically identifies anatomical landmarks, determines optimal reformatting planes, and generates contiguous views of desired landmarks from the 3D data stack, making the process independent of technician skill and significantly reducing reformatting time.
Solution Approach 2:
The patent replaces the manual mechanical process of technician-based reformatting with an automated computational system. Instead of technicians manually manipulating imaging consoles to achieve desired views, the system uses automated image processing algorithms to retrospectively generate reformatted views from the 3D data, eliminating the time-consuming manual process while maintaining flexibility.
3Manufacturing precision
If higher resolution 3D data stack is acquired to improve anatomical detail, then the quality of reformatted views is improved, but the data processing and reformatting complexity increases
Solution Approach 1:
The patent performs preliminary actions by acquiring the high-resolution 3D data stack with sufficient field of view to include all desired anatomical landmarks in a single acquisition. This pre-acquisition of comprehensive 3D data with isotropic resolution eliminates the need for subsequent repetitive scanning and simplifies the reformatting process, as all necessary anatomical information is already captured in the initial 3D acquisition.
4Measurement precision
If graphical prescription or reformatting parameters are manually adjusted to achieve accurate anatomical alignment, then the accuracy of anatomical landmark visualization is improved, but the process becomes sensitive to small errors and requires repeated scans
Solution Approach 1:
The patent replaces manual graphical prescription and reformatting parameter adjustment with automated computational algorithms. The system automatically identifies anatomical landmarks, determines optimal reformatting planes and orientations, and generates contiguous views with high precision. This automated approach eliminates human error and sensitivity to small parameter adjustments, significantly improving the reliability of anatomical landmark visualization and reducing the need for repeated scans.
Data Source
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AI summary
Techniques are described for generating reformatted views of a three-dimensional (3D) anatomy scan using deep-learning estimated scan prescription masks. According to an embodiment, a system is provided that comprises a memory that stores computer executable components, and a processor that executes the computer executable components stored in the memory. The computer executable components comprise a mask generation component that employs a pre-trained neural network model to generate masks for different anatomical landmarks depicted in one or more calibration images captured of an anatomical region of a patient. The computer executable components further comprise a reformatting component that reformats 3D image data captured of the anatomical region of the patient using the masks to generate different representations of the 3D image data that correspond to the different anatomical landmarks.