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Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

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Quantifying Differences Between Affine and Nonlinear Spatial Normalization of FP-CIT Spect Images.

Diego Castillo-Barnes¹, Carmen Jimenez-Mesa¹, Francisco J Martinez-Murcia¹

¹Department of Signal Theory, Telematics and Communications, University of Granada, Periodista Daniel Saucedo Aranda, 18071 Granada, Spain.

International Journal of Neural Systems

|March 22, 2022

Summary

Nonlinear spatial normalization in brain scans can introduce bias, particularly affecting Parkinson's Disease diagnosis. This study quantifies deformation-induced bias in FP-CIT SPECT imaging, highlighting potential machine learning inaccuracies.

Keywords:

FP-CIT SPECT Neuroimaging Parkinson’s disease spatial registration statistical maps

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Area of Science:

Neuroimaging
Medical Image Analysis
Radiology

Background:

Spatial normalization is crucial for quantitative comparison of brain scans.
Nonlinear transformations are commonly used for voxel-wise comparisons in functional and MRI scans.
Nonlinear transformations can introduce bias, potentially impacting neurological disorder diagnoses.

Purpose of the Study:

To quantify the bias introduced by affine and nonlinear spatial registration in FP-CIT SPECT volumes.
To assess the impact of spatial normalization bias on Parkinson's Disease (PD) patient data.
To evaluate the influence of normalization-induced bias on machine learning diagnostic models.

Main Methods:

Calculated deformation fields from spatial registration of FP-CIT SPECT scans from healthy controls and PD patients.
Applied calculated deformation fields to a 3D grid to quantify volumetric changes (enlargement/compression).
Compared bias introduced by affine versus nonlinear spatial registration methods.

Main Results:

Nonlinear registration of PD patient scans resulted in a striatal region artificially similar in shape to healthy subjects.
This artificial similarity increases interclass separation between PD patients and controls.
Local intensity decreases in the affected region of healthy subjects, leading to biased machine learning results.

Conclusions:

Nonlinear spatial registration can introduce significant bias in FP-CIT SPECT imaging for PD.
This bias can lead to inaccurate diagnostic outcomes and unreliable machine learning model performance.
Careful consideration of spatial normalization methods is essential for accurate neuroimaging analysis in neurological disorders.