Related Experiment Video
Updated: May 13, 2025
A Two-interval Forced-choice Task for Multisensory Comparisons
Published on: November 9, 2018
Comparison of 18F-FDG PET image quality and quantitative parameters between DPR and OSEM reconstruction algorithm in
Ziyi Zhang1, Wei Han1, Zhehao Lyu1
1Department of Nuclear Medicine, First Clinical Hospital affiliated of Harbin Medical University, Harbin, 150001, People's Republic of China.
Objectives:
The present study aimed to investigate the influence of the deep progressive learning reconstruction (DPR) algorithm on the 18F-FDG PET image quality and quantitative parameters.
Methods:
In this retrospective study, data were collected from 55 healthy individuals and 184 patients with primary malignant pulmonary tumors who underwent 18F-FDG PET/CT examinations. PET data were reconstructed using the ordered subset expectation maximization (OSEM) and DPR algorithms. The influence of DPR algorithm on quantitative parameters was explored, including the SUVmax, SUVmean, standard deviation of SUV (SUVSD), metabolic tumor volume (MTV), total lesion glycolysis (TLG), and tumor-to-background uptake ratio (TBR). Finally, the differences in image quality parameters, including signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR), between the two reconstruction algorithms were evaluated.
Results:
DPR algorithm significantly reduced the SUVmax and SUVSD of background tissues (all, P < 0.001) compared to OSEM algorithm, while no statistical difference was observed in SUVmean between the two algorithms (all, P > 0.05). DPR algorithm notably increased the SUVmax, SUVmean, and TBR of lesions (all, P < 0.001) and reduced MTV (P = 0.005), with minimal differences in TLG noted between the reconstruction algorithms (P < 0.001). The percentage differences in SUVmax (P = 0.001), SUVmean (P = 0.005), and TBR (P = 0.001) between the two algorithms were significantly higher in solid nodules than in pure ground glass nodules (pGGNs). The ΔCNR between solid nodules (P = 0.031) and mixed ground glass nodules (P = 0.020) was greater than that between pGGNs. SNR and CNR obtained using the DPR algorithm were markedly improved compared to those determined using the OSEM algorithm (all, P < 0.001).
Conclusion:
Under identical acquisition conditions, the DPR algorithm enhanced the accuracy of quantitative parameters in pulmonary lesions and potentially improved lesion detectability. The DPR algorithm increased image SNR and CNR compared to those obtained using the OSEM algorithm, significantly optimizing overall image quality. This advancement facilitated precise clinical diagnosis, underpinning its potential to significantly contribute to the field of medical imaging.
More Related Videos
12:35Comparison of Three Different Methods for Determining Cell Proliferation in Breast Cancer Cell Lines
Published on: September 3, 2016
06:19Comparison of Three Clinical Stereoscopic Methods for Measuring Binocular Visual Function During Amblyopic Treatment in Unilateral Amblyopia
Published on: September 27, 2024
Related Concept Videos
01:13Multiple Comparison Tests
It would be easy to compare two samples using a significance alpha level of 0.05. In other words, there is only one sample pair to be compared. However, it would be difficult to identify a significantly different sample if the number...
01:24Comparison Between Electrical And Gravitational Forces
Since both are inverse square law forces, the distance gets canceled when the ratio of the two forces is considered. Instead, the ratio of the electrical and gravitational forces depends on...
01:24Comparison between RL and RC circuits
02:54Evolutionary Relationships through Genome Comparisons
01:20Comparing the Survival Analysis of Two or More Groups
02:26Comparing Copy Number Variations and SNPs
Copy number variations or CNVs are the structural variations that cover more than 1kb of DNA sequence. The single nucleotide polymorphism (SNP), on the other hand, is a single nucleotide change or a point mutation that is found in more than 1%...