Improving 3D PET imaging by restoration: a phantom study

Restoration of Tc-99m methyl diphosphonate bone scan image using Richardson-Lucy algorithm

INTRODUCTION

In this study, the optimal input parameters point spread function (PSF) and the number of iterations of the Richardson-Lucy algorithm were experimentally determined to restore Tc-99 m methyl diphosphonate (MDP) whole-body bone scan images.

MATERIALS AND METHODS

The experiment was performed on 60 anonymized Tc-99 m MDP whole-body bone scan images. Ten images were used for estimating the optimum value of PSF and the number of iterations to restore scintigraphic images. The remaining 50 images were used for validation of estimated parameters. The image quality of observed and restored images was assessed objectively using blind/referenceless image spatial quality evaluator (BRISQUE), mean brightness (MB), discrete entropy (DE), and edge-based contrast measure (EBCM) image quality metrics. Image quality was subjectively assessed by two nuclear medicine physicians (NMPs) by comparing the restored image quality with observed image quality and assigning a score to each image on the scale of 0-5.

RESULTS

Based on BRISQUE, MB, DE, and EBCM scores, the restored images were significantly sharper, less bright, had more detailed information, and had less contrast around edges compared to the input images. The restored images had improved resolution based on visual assessment as well; NMPs assigned an average image quality score of 4.00 to restored images. Maximum resolution enhancement was noticed at PSF (size: 11 pixels, sigma: 1.75 pixels) and the number of iterations = 10. With the increase in the number of iterations, noise also gets amplified along with resolution enhancement and affects the detectability of small lesions; in the case of relatively low noisy input images, the number of iterations = 5 gave better results.

CONCLUSION

Tc-99 m MDP bone scan images were restored to improve image quality using the Richardson-Lucy algorithm. The optimum value of the PSF parameter was found to be of size = 11 pixels and sigma = 1.75 pixels.

Improving (18)F-Fluoro-D-Glucose-Positron Emission Tomography/Computed Tomography Imaging in Alzheimer's Disease Studies

The goal was to improve Alzheimer's 2-deoxy-2-¹⁸F-fluoro-D-glucose (¹⁸F FDG)-positron emission tomography (PET)/computed tomography (CT) imaging through application of a novel, hybrid Fourier-wavelet windowed Fourier transform (WFT) restoration technique, in order to provide earlier and more accurate clinical results. General Electric Medical Systems downward-looking sonar PET/CT 16 slice system was used to acquire studies. Patient data were acquired according the Alzheimer's disease Neuroimaging Initiative (ADNI) protocol. Here, we implemented Fourier-wavelet regularized restoration, with a Butterworth low-pass filter, order n = 6 and a cut-off frequency f = 0.35 cycles/pixel and wavelet (Daubechies, order 2) noise suppression. The original (PET-O) and restored (PET-R) ADNI subject PET images were compared using the Alzheimer's discrimination analysis by dedicated software. Forty-two PET/CT scans were used in the study. They were performed on eleven ADNI subjects at intervals of approximately 6 months. The final clinical diagnosis was used as a gold standard. For three subjects, the final clinical diagnosis was mild cognitive impairment and those 13 PET/CT studies were not included in the final comparison, as the result was considered as inconclusive. Using the reminding 29 PET/CT studies (23 AD and 6 normal), the sensitivity and specificity of the PET-O and PET-R were calculated. The sensitivity was 0.65 and 0.96 for PET-O and PET-R, respectively, and the specificity was 0.67 and 0.50 for PET-O and PET-R. The accuracy was 0.66 and 0.86 for PET-O and PET-R, respectively. The results of the study demonstrated that the accuracy of three-dimensional brain F-18 FDG PET images was significantly improved by Fourier-wavelet restoration filtering.

A statistical study of the factors influencing the extent of respiratory motion blur in PET imaging

Respiratory motion results in significant motion blur in thoracic and abdomen PET imaging. The extent of respiratory motion blur is mainly correlated with breathing amplitude, tumor size and location. In this paper we introduce a statistical study to quantitatively show the factors influencing the extent of respiratory motion blur in thoracic PET images. The study is centered on two regression models, one is linked with motion blur induced loss of mean intensity(LMI), tumor motion magnitude and tumor size, and another is to investigate the influence of tumor location, patient gender and patient height on tumor motion magnitude. We use the blur identification and image restoration technique to estimate the tumor motion and compute the LMI. The regression model was validated by simulation and phantom data before extended to 39 cases of clinical lung tumor PET images corrupted with blurring artifact. Results show that the motion magnitude of lung tumor during breathing is 10.9±3.7mm in transaxial plane, and it is significantly greater in lower lung lobes than in upper lobes. The LMI is 7.1±2.4% in the region of interest (ROI) above 40% of the image's maximum intensity. The least-square estimate of regression equations demonstrates that LMI is proportional to tumor motion magnitude and is inversely proportional to tumor size; the two factors play the same role in determining the extent of respiratory motion blur in thoraco-abdominal PET imaging. The location of tumor was shown as the major factor determining its motion magnitude, while the influencing of patient gender and height on tumor motion was not shown significant.