Impact of γ factor in the penalty function of Bayesian penalized likelihood reconstruction (Q.Clear) to achieve high-resolution PET images

Abstract Background The Bayesian penalized likelihood PET reconstruction (BPL) algorithm, Q.Clear (GE Healthcare), has recently been clinically applied to clinical image reconstruction. The BPL includes a relative difference penalty (RDP) as a penalty function. The β value that controls the behavior of RDP determines the global strength of noise suppression, whereas the γ factor in RDP controls the degree of edge preservation. The present study aimed to assess the effects of various γ factors in RDP on the ability to detect sub-centimeter lesions. Methods All PET data were acquired for 10 min using a Discovery MI PET/CT system (GE Healthcare). We used a NEMA IEC body phantom containing spheres with inner diameters of 10, 13, 17, 22, 28 and 37 mm and 4.0, 5.0, 6.2, 7.9, 10 and 13 mm. The target-to-background ratio of the phantom was 4:1, and the background activity concentration was 5.3 kBq/mL. We also evaluated cold spheres containing only non-radioactive water with the same background activity concentration. All images were reconstructed using BPL + time of flight (TOF). The ranges of β values and γ factors in BPL were 50–600 and 2–20, respectively. We reconstructed PET images using the Duetto toolbox for MATLAB software. We calculated the % hot contrast recovery coefficient (CRChot) of each hot sphere, the cold CRC (CRCcold) of each cold sphere, the background variability (BV) and residual lung error (LE). We measured the full width at half maximum (FWHM) of the micro hollow hot spheres ≤ 13 mm to assess spatial resolution on the reconstructed PET images. Results The CRChot and CRCcold for different β values and γ factors depended on the size of the small spheres. The CRChot, CRCcold and BV increased along with the γ factor. A 6.2-mm hot sphere was obvious in BPL as lower β values and higher γ factors, whereas γ factors ≥ 10 resulted in images with increased background noise. The FWHM became smaller when the γ factor increased. Conclusion High and low γ factors, respectively, preserved the edges of reconstructed PET images and promoted image smoothing. The BPL with a γ factor above the default value in Q.Clear (γ factor = 2) generated high-resolution PET images, although image noise slightly diverged. Optimizing the β value and the γ factor in BPL enabled the detection of lesions ≤ 6.2 mm

Determination of optimal regularization factor in Bayesian penalized likelihood reconstruction of brain PET images using [ F]FDG and [ C]PiB.

The Bayesian penalized likelihood (BPL) reconstruction algorithm, Q.Clear, can achieve a higher signal-to-noise ratio on images and more accurate quantitation than ordered subset-expectation maximization (OSEM). The reconstruction parameter (β) in BPL requires optimization according to the radiopharmaceutical tracer. The present study aimed to define the optimal β value in BPL required to diagnose Alzheimer disease from brain positron emission tomography (PET) images acquired using F-fluoro-2-deoxy-D-glucose ([ F]FDG) and C-labeled Pittsburg compound B ([ C]PiB)

Predictive factors for postoperative tachyarrhythmia after thoracoscopic esophagectomy and the usefulness of landiolol hydrochloride for its treatment

Background: Tachyarrhythmia after esophagectomy is a severe complication that should not be underestimated because of its negative impact. The aims of this study were to clarify the cause and impact of postoperative tachyarrhythmia after thoracoscopic esophagectomy. Additionally, we analyzed the usefulness of landiolol administration for postoperative tachyarrhythmia. Methods: We evaluated the predictive factors for tachyarrhythmia onset after surgery and its clinical impact in 127 patients who underwent thoracoscopic esophagectomy with extended lymphadenectomy. Moreover, we analyzed the efficacy of landiolol for postoperative tachyarrhythmia. Results: Tachyarrhythmia developed in 38 of the 127 patients. Multivariate analysis showed that advanced age, heart disease, and hyperlipidemia were associated with postoperative tachyarrhythmia. Hyponatremia, hypoalbuminemia, and leukocytosis on postoperative day 3 were significantly associated with tachyarrhythmia onset. The incidence of all complications and respiratory complications, including pneumonia, was significantly higher in patients with than in those without tachyarrhythmia. The mortality rate in the tachyarrhythmia group tended to be higher than that in the nontachyarrhythmia group. Landiolol as a treatment for tachyarrhythmia immediately decreased heart rate and safely reduced subsequent respiratory complications. Conclusion: In elderly patients with cardiac disease or hyperlipidemia, surgeons should be alert for the occurrence of tachyarrhythmia after esophagectomy. Postoperative tachyarrhythmia is a marker of morbidities with particular emphasis on respiratory complications. However, it can be adequately managed by landiolol, resulting in fewer respiratory complications. Landiolol might be a safe and convenient agent for managing postoperative tachyarrhythmia after thoracoscopic esophagectomy, resulting in lower mortality and morbidity rates. © 2013 The Japan Esophageal Society and Springer