Molecular Imaging Spectral Segmentation for Memory-Limited Analysis
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Solution Overview
Problem
Molecular imaging data analysis is hindered by high volumes of data exceeding classic computer RAM, leading to memory constraints and analysis biases, with existing storage formats not optimized for rapid querying or comparative analysis across multiple datasets.
Innovation Solution
A method involving sectioning of molecular spectra into non-overlapping or partially overlapping sections during data acquisition or analysis, allowing for efficient data processing and storage in a high-volume database, enabling normalization and comparison of datasets without information loss, and optimizing memory usage for statistical processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If molecular imaging data is stored in its entirety for comprehensive analysis, then complete molecular information is preserved, but memory requirements exceed classic computer RAM capacity
Solution Approach 1:
The patent divides molecular imaging data into multiple data cubes, where each cube represents a segmented portion of the complete dataset. This segmentation allows the data to be stored and processed in manageable chunks that fit within classic computer RAM, while still preserving all molecular information across the complete set of cubes.
Solution Approach 2:
The patent introduces a new dimensional organization by arranging data cubes in a multi-cube structure with additional spatial dimensions. This dimensional transformation enables efficient memory utilization by organizing data in a way that fits computational memory constraints while maintaining comprehensive molecular information through the extended dimensional structure.
2Productivity
If data reduction calculations are performed to fit memory constraints, then data can be processed in classic computers, but analysis bias is introduced by not taking into account all available information
Solution Approach 1:
By segmenting data into multiple cubes that can be processed individually, the system enables comprehensive analysis of all molecular information without requiring reduction calculations. Each cube can be processed in memory while the complete set of cubes preserves all original data for unbiased analysis.
Solution Approach 2:
The patent performs preliminary organization of data into structured cubes during data acquisition or preprocessing, preparing the data in advance for efficient processing. This preliminary structuring enables subsequent analysis to access all molecular information without requiring reduction, as the data is already organized for memory-efficient processing.
3Adaptability or versatility
If multiple datasets are analyzed simultaneously for comprehensive pharmacokinetics and pharmacodynamics studies, then complete analytical capability is achieved, but memory requirements increase beyond classic computer capacity
Solution Approach 1:
Multiple datasets are organized as separate collections of data cubes, allowing each dataset to be processed independently in memory while maintaining the capability for comprehensive comparative analysis. The segmented cube structure enables loading and analyzing multiple datasets sequentially or in parallel without exceeding memory constraints.
Solution Approach 2:
The multi-cube data structure provides a universal framework that can handle multiple datasets with different sizes and dimensions. This multi-functional structure enables the same processing architecture to analyze various datasets simultaneously or sequentially, providing comprehensive pharmacokinetics and pharmacodynamics analysis capabilities within classic computer memory limits.
4Ease of manufacture
If data is stored in traditional formats for compatibility with existing systems, then ease of implementation is maintained, but querying and analysis efficiency is reduced
Solution Approach 1:
The patent implements a segmented cube data structure that organizes molecular imaging data into discrete, indexable units. This segmentation enables efficient querying by allowing the system to access specific cubes or portions of cubes relevant to particular analysis needs, dramatically improving querying speed compared to traditional formats that require loading entire datasets.
Solution Approach 2:
The system performs preliminary organization of data into structured cubes with optimized indexing during data import or preprocessing. This preliminary structuring enables rapid querying and analysis by pre-organizing data in a format that minimizes access time, while maintaining implementation feasibility through automated conversion processes from traditional formats.
Data Source
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AI summary
The invention relates mainly to a method for processing a plurality of spectral datasets (J1-Jn) intended for being used by a molecular imaging method or a method for recording a plurality of spectral datasets (J1-Jn), each spectral dataset (J1-Jn) being defined by a set of spatial positions (Xi, Yj) each of which is associated with a molecular spectrum with at least two dimensions containing a set of molecular information (S(Xi, Yj)), characterised in that it comprises in particular the following steps: for each dataset (J1-Jn), cutting the molecular spectrum associated with each position (Xi, Yj) into a plurality of spectrum segments (T1-Tm); inserting the segments (T1-Tm) obtained for each position (Xi, Yj) of each dataset (J1-Jn) into a database (BDD); selecting in the database (BDD), following a request relating to molecular information of interest, the one or more segments (T1-Tm) containing the molecular information of interest; and selecting, within each segment (T1-Tm), said molecular information of interest.