Phantom and clinical evaluation of bone SPECT/CT image reconstruction with xSPECT algorithm

13301甲第5287号博士（保健学）金沢大学博士論文本文Full 以下に掲載：EJNMMI Research 10(1) 2020. Springer. 共著者：Noriaki Miyaji, Kenta Miwa, Ayaka Tokiwa, Hajime Ichikawa, Takashi Terauchi, Mitsuru Koizumi, Masahisa Onoguch

Cross-reactivity between major IgE core epitopes on Cry j 2 allergen of Japanese cedar pollen and relevant sequences on Cha o 2 allergen of Japanese cypress pollen

Background: Cry j 2 and Cha o 2 are major allergens in Japanese cedar (Cryptomeria japonica; CJ) and Japanese cypress (Chamaecyparis obtusa; CO) pollen, respectively. Here, we assessed the epitopes related to the cross-reactivity between Cry j 2 and Cha o 2 using in vitro analyses. Methods: Peptides were synthesized based on Cry j 2 sequential epitopes and relevant Cha o 2 amino acid sequences. Four representative monoclonal antibodies (mAbs) against Cry j 2 were used according to their epitope recognitions. Serum samples were collected from 31 patients with CJ pollinosis. To investigate cross-reactivity between Cry j 2 and Cha o 2, ELISA and inhibition ELISA were performed with mAbs and sera from patients with CJ pollinosis. Results: Two of four mAbs had reactivity to both Cry j 2 and Cha o 2. Of these two mAbs, one mAb (T27) recognized the amino acid sequence 169KVVNGRTV176 on Cha o 2. This is related to the core epitope 169KWVNGREI176 on Cry j 2, which is an important IgE epitope. In addition, we found that these correlative sequences and purified allergens showed cross-reactivity between Cry j 2 and Cha o 2 in IgE of CJ patients. Conclusions: We demonstrated the importance of 169KVVNGRTV176 in Cha o 2 for cross-reactivity with the Cry j 2 epitope 169KWVNGREI176, which plays an important role in allergenicity in CJ pollinosis. Our results are useful for the development of safer and more efficient therapeutic strategies for the treatment of CJ and CO pollen allergies

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