Mapeo sistemático de parámetros para la corrección de dispersión basada en wavelets en SPECT: percepción clínica...

Photon scatter significantly degrades Single Photon Emission Computed Tomography (SPECT) image quality, with scattered photons accounting for 30-40% of detected counts within standard energy windows. While conventional scatter correction methods face limitations including noise amplification and computational demands, wavelet transforms offer promising capabilities for sinogram-domain correction. However, comprehensive parameter optimization remains unexplored.

We evaluated 96 mother wavelets across seven families, implementing three decomposition levels and five thresholding strategies in a Monte Carlo simulation framework. Scatter-contaminated sinograms were processed using discrete wavelet transforms and reconstructed via filtered backprojection. Quantitative assessment employed Universal Image Quality Index (UIQI) with varying block sizes (3 × 3, 25 × 25, 128 × 128) and Root Mean Square Error (RMSE). Three nuclear medicine physicians performed blinded qualitative assessment of the processed images.

Among 94 viable wavelets (excluding outliers db45 and rbio3.1), global optimization identified Rigrsure and Heursure thresholding at decomposition level 1 as optimal for maximizing UIQI (0.559 ± 0.002), while per-slice optimization favored Minimaxi thresholding at level 2. Strong positive correlation existed between UIQI (25 × 25) and UIQI (128 × 128) (r = 0.887, p < 0.01), with both metrics inversely related to RMSE error (r≈ - 0.73, p < 0.01). Despite UIQI optimization, RMSE-optimized images received significantly higher visual quality rankings from physicians (69% improvement, p < 0.001), revealing critical divergence between quantitative metrics and diagnostic utility.

This study establishes wavelet-based scatter correction as a viable approach for SPECT image enhancement through systematic parameter mapping. The marked preference for RMSE-optimized images over UIQI-optimized ones underscores the necessity of aligning algorithmic optimization with clinical perception rather than technical metrics alone. These findings provide a foundation for standardizing wavelet implementation in SPECT scatter correction, directly connecting mathematical optimization to diagnostic relevance in nuclear medicine imaging.