Metadata-Version: 2.1
Name: spectquant
Version: 0.0.1
Summary: Specialized Package for Extracting Image Features for Cardiac Amyloidosis Quantification on SPECT.
Home-page: https://github.com/MarkusStefan/spectquant
Author: MarkusStefan
Author-email: markus.koefler11@gmail.com
License: MIT License
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Medical Science Apps.
Classifier: Topic :: Scientific/Engineering :: Image Processing
Classifier: Topic :: Scientific/Engineering :: Visualization
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Description-Content-Type: text/markdown
License-File: LICENSE
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# SpectQuant

SpectQuant is a specialized package designed for the feature extraction of special photon emission computer tomography (SPECT) data.
It leverages advanced algorithms known from signal processing and methodologies to standardized results with the potential for scaled data mining.
*Note*: Package has been desigend particularly for assessing treatment response of cardiac amyloidosis.


Special Photon Emission Computer Tomography (SPECT) is an imaging technique that allows for the visualization of functional processes in the body. It involves the detection of gamma rays emitted by a radioactive tracer injected into the patient. The quantitative analysis of SPECT data is crucial for accurate diagnosis and research.

The key steps in the quantitative analysis of SPECT data include:

1. **Data Processing**: Preprocessing the original data by correcting for False Positives, and normalized voxel scores accross images, and image size.
2. **Feature Extraction**: Measuring the concentration of the tracer in different regions of the body.
3. **Visualization**: Creating visual representations of the processed data to facilitate interpretation and quick validity assessment. 
