# Optimization of Image Quality in Pelvis Lymphoscintigraphy SPECT/CT Using Discovery NM/CT 670

## TL;DR

This study improves image quality in pelvis lymphoscintigraphy using optimized reconstruction and filtering techniques, leading to better diagnostic accuracy.

## Contribution

The paper introduces optimized postprocessing and reconstruction parameters for SPECT/CT imaging to enhance image quality in pelvis lymphoscintigraphy.

## Key findings

- Using the RR algorithm improved CNR by 74%, contrast by 35%, and reduced noise by 38%.
- Optimal noise reduction and CNR were achieved with subiteration 4×12 using a Gaussian filter (4 mm FWHM) or Butterworth filter (cutoff 1, power 10).
- Qualitative validation by nuclear medicine specialists confirmed the improved image quality.

## Abstract

Aim
 A lymphoscintigraphy is a crucial diagnostic tool for visualizing lymph nodes. This scan plays a significant role in determining the treatment and recovery plan for the patients. Due to the small lymph node size, obtaining high-quality images is important to prevent inaccurate results. We aimed to identify the most effective method for enhancing image quality through postprocessing techniques and altering the image reconstruction process.

Methods
 Two data sets were utilized in this study. First, National Electrical Manufacturers Association body phantom was filled with [
99m
Tc]Tc-pertechnetate and prepared with and without any activity in the background of the body. Second, the images of 50 patients who underwent single-photon emission computed tomography/computed tomography imaging received [
99m
Tc]Tc-phytate were collected. Discovery 670 GE gamma camera was used for imaging. Preprocessing of all images was performed by Xeleris and 3DSlicer 5.2.2 software was used for quantification. The effect of image reconstruction parameters such as resolution recovery (RR) algorithm, iteration, subsets, cutoff, and power in Butterworth filter, and full width at half maximum (FWHM) of Gaussian filter was assessed. The image quality index was determined based on contrast-to-noise ratio (CNR), contrast, and coefficient of variation.

Results
 The utilization of the RR algorithm showed notable improvements equal to 74, 35, and 38% of CNR, contrast, and noise reduction, respectively. Significant differences were observed in subiteration of 40 to 112 (
p
-value < 0.05). The alteration of effective parameters in both smoothing filters yielded statistically significant results, leading to enhanced detectability, reduced noise, and improved contrast simultaneously. Optimum results in terms of noise reduction and CNR were achieved with subiteration (i × s) 4 × 12 using a Gaussian filter with FWHM of 4 or Butterworth filter with power of 10 and cutoff of 1. The highest contrast was observed at subiteration 40 using the Butterworth filter with cutoff of 0.5 and power of 5 or Gaussian filter with 2 mm FWHM. Qualitative analysis by two nuclear medicine specialists validated the quantified image quality.

Conclusion
 The reconstruction setting involving subiteration 48 with the Butterworth filter using cutoff of 1 and power of 10 or 4 mm FWHM of Gaussian filter produced the highest quality images.