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Tuning Butterworth filter's parameters in SPECT reconstructions via kernel-based Bayesian optimization with a

Luca Pastrello1, Diego Cecchin2, Gabriele Santin3

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Mathematical Medicine and Biology : a Journal of the IMA
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Summary
This summary is machine-generated.

This study introduces a novel No-Reference Image Quality Assessment (NR IQA) method for Single Photon Emission Computed Tomography (SPECT) imaging. It optimizes reconstruction parameters using Bayesian methods for objective image quality evaluation without a reference image.

Keywords:
Bayesian optimizationSPECT imaginggreedy kernel modelsno-reference metric

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Area of Science:

  • Medical Imaging
  • Computational Imaging
  • Image Processing

Background:

  • Single Photon Emission Computed Tomography (SPECT) image reconstruction involves critical parameters affecting clinical image quality.
  • Current image quality assessment methods, like Mean Squared Error (MSE) and Structural Similarity Index (SSIM), are subjective or require a ground-truth image.
  • Objective and quantitative assessment of SPECT image quality is needed.

Purpose of the Study:

  • To investigate the application of a No-Reference Image Quality Assessment (NR IQA) method, specifically the Perception-based Image QUality Evaluator (PIQUE) score, for SPECT imaging.
  • To propose a novel approach for optimizing SPECT image reconstruction parameters using PIQUE.
  • To enable objective and quantitative image quality assessment without a reference image.

Main Methods:

  • Utilized filtered backprojection with a parameter-dependent Butterworth filter for SPECT image reconstruction.
  • Employed a kernel-based Bayesian optimization framework, rooted in reproducing kernel Hilbert space theory, for optimizing filter parameters.
  • Investigated connections to greedy approximation techniques like P- and f-greedy.

Main Results:

  • Demonstrated the potential of the proposed NR IQA approach for evaluating SPECT images.
  • Showcased the effectiveness of the Bayesian optimization framework for tuning reconstruction parameters.
  • Achieved objective and quantitative image quality assessment in a clinical SPECT setting.

Conclusions:

  • The novel application of PIQUE offers a promising solution for objective image quality assessment in SPECT.
  • Bayesian optimization provides an effective framework for tuning reconstruction parameters in SPECT.
  • This approach overcomes the limitations of traditional subjective and full-reference methods in SPECT image quality evaluation.