http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
- 中文 을 입력하시려면 zhongwen을 입력하시고 space를누르시면됩니다.
- 北京 을 입력하시려면 beijing을 입력하시고 space를 누르시면 됩니다.
RISS 인기검색어
- 검색키워드
- 저자 : Rakvongthai, Yothin (검색결과 2 건)
- 무료
- 기관 내 무료
- 유료
-
Dual-Tracer Parathyroid Imaging Using Joint SPECT Reconstruction
Jaruwan Onwanna,Maythinee Chantadisai,Tawatchai Chaiwatanarata,Yothin Rakvongthai 대한핵의학회 2023 핵의학 분자영상 Vol.57 No.3
Purpose We assessed the lesion detection performance of the dual-tracer parathyroid SPECT imaging using the joint reconstructionmethod. Materials and Methods Thirty-six noise realizations were created from SPECT projections collected from an in-house neckphantom to emulate 99mTc-pertechnetate/99mTc-sestamibi parathyroid SPECT datasets. Difference images representing parathyroidlesions were reconstructed using the subtraction and the joint methods whose corresponding optimal iteration wasdefined as the iteration which maximized the channelized Hotelling observer signal-to-noise ratio (CHO-SNR). The jointmethod whose initial estimate was derived from the subtraction method at optimal iteration (the joint-AltInt method) wasalso assessed. In a study of 36 patients, a human-observer lesion-detection study was performed using difference images fromthe three methods at optimal iteration and the subtraction method with four iterations. The area under the receiver operatingcharacteristic curve (AUC) was calculated for each method. Results In the phantom study, both the joint-AltInt method and the joint method improved SNR compared to the subtractionmethod at their optimal iteration by 444% and 81%, respectively. In the patient study, the joint-AltInt method yielded thehighest AUC of 0.73 as compared with 0.72, 0.71, and 0.64 from the joint method, the subtraction method at optimal iteration,and the subtraction method at four iterations. At a specificity of at least 0.70, the joint-AltInt method yielded significantlyhigher sensitivity than the other methods (0.60 vs 0.46, 042, and 0.42; p < 0.05). Conclusions The joint reconstruction method yielded higher lesion detectability than the conventional method and holdspromise for dual-tracer parathyroid SPECT imaging.
-
Sparse-View Spectral CT Reconstruction Using Spectral Patch-Based Low-Rank Penalty
Kyungsang Kim,Jong Chul Ye,Worstell, William,Jinsong Ouyang,Rakvongthai, Yothin,El Fakhri, Georges,Quanzheng Li IEEE 2015 IEEE transactions on medical imaging Vol.34 No.3
<P>Spectral computed tomography (CT) is a promising technique with the potential for improving lesion detection, tissue characterization, and material decomposition. In this paper, we are interested in kVp switching-based spectral CT that alternates distinct kVp X-ray transmissions during gantry rotation. This system can acquire multiple X-ray energy transmissions without additional radiation dose. However, only sparse views are generated for each spectral measurement; and the spectra themselves are limited in number. To address these limitations, we propose a penalized maximum likelihood method using spectral patch-based low-rank penalty, which exploits the self-similarity of patches that are collected at the same position in spectral images. The main advantage is that the relatively small number of materials within each patch allows us to employ the low-rank penalty that is less sensitive to intensity changes while preserving edge directions. In our optimization formulation, the cost function consists of the Poisson log-likelihood for X-ray transmission and the nonconvex patch-based low-rank penalty. Since the original cost function is difficult to minimize directly, we propose an optimization method using separable quadratic surrogate and concave convex procedure algorithms for the log-likelihood and penalty terms, which results in an alternating minimization that provides a computational advantage because each subproblem can be solved independently. We performed computer simulations and a real experiment using a kVp switching-based spectral CT with sparse-view measurements, and compared the proposed method with conventional algorithms. We confirmed that the proposed method improves spectral images both qualitatively and quantitatively. Furthermore, our GPU implementation significantly reduces the computational cost.</P>
연관 검색어 추천
이 검색어로 많이 본 자료
활용도 높은 자료
