• The Korean Journal of Nuclear Medicine Technology
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• v.27 no.2
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• pp.111-127
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• 2023

Purpose: Gallium-68 (⁶⁸Ga) is increasingly used in nuclear medicine imaging for various conditions such as lymphoma and neuroendocrine tumors by labeling tracers like Prostate Specific Membrane Antigen (PSMA) and DOTA-TOC. However, compared to Fluorine-18 (¹⁸F) used in conventional nuclear medicine imaging, ⁶⁸Ga has lower spatial resolution and relatively higher Signal to Background Ratio (SBR). Therefore, this study aimed to investigate the optimized parameters and reconstruction methods for PET/CT imaging using the ⁶⁸Ga radiotracer through model-based image evaluation. Materials and Methods: Based on clinical images of ⁶⁸Ga-PSMA PET/CT, a NEMA/IEC 2008 PET phantom model was prepared with a Hot vs Background (H/B) ratio of 10:1. Images were acquired for 9 minutes in list mode using DMIDR (GE, Milwaukee WI, USA). Subsequently, reconstructions were performed for 1 to 8 minutes using OS-EM (Ordered Subset Expectation Maximization) + TOF (Time of Flight) + Sharp IR (VPFX-S), and BSREM (Block Sequential Regularized Expectation Maximization) + TOF + Sharp IR (QCFX-S-400), followed by comparative evaluation. Based on the previous experimental results, images were reconstructed for BSREM + TOF + Sharp IR / 2 minutes (QCFX-S-2min) with varying β-strength values from 100 to 700. The image quality was evaluated using AMIDE (freeware, Ver.1.0.1) and Advanced Workstation (GE, USA). Results: Images reconstructed with QCFX-S-400 showed relatively higher values for SNR (Signal to Noise Ratio), CNR (Contrast to Noise Ratio), count, RC (Recovery Coefficient), and SUV (Standardized Uptake Value) compared to VPFX-S. SNR, CNR, and SUV exhibited the highest values at 2 minutes/bed acquisition time. RC showed the highest values for a 10 mm sphere at 2 minutes/bed acquisition time. For small spheres of 10 mm and 13 mm, an inverse relationship between β-strength increase and count was observed. SNR and CNR peaked at β-strength 400 and then decreased, while SUV and RC exhibited a normal distribution based on sphere size for β-strength values of 400 and above. Conclusion: Based on the experiments, PET/CT imaging using the ⁶⁸Ga radiotracer yielded the most favorable quantitative and qualitative results with a 2 minutes/bed acquisition time and BSREM reconstruction, particularly when applying β-strength 400. The application of BSREM can enhance accurate quantification and image quality in ⁶⁸Ga PET/CT imaging, and an optimization process tailored to each institution's imaging objectives appears necessary.

• Proceedings of the Acoustical Society of Korea Conference
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• spring
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• pp.103-106
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• 2002

본 논문에서는 입력음성신호로부터 음소간의 경계를 찾는 문제를 풀기위해 재귀적인 방식으로 EM 알고리즘을 적용한다. 즉, 예상되는 두 끝점 사이의 부분을 현재의 프레임 n 이라고 하면, 그 전 프레임 n-1 에서 구해진 끝점이 주는 정보와 그 끝점으로부터 이어지는 음성샘플로부터 현재 프레임의 끝점을 구한다. 또한 현재의 프레임 n 에서 끝점을 추정해 내면, 그 추정한 끝점과 그 점 이후에 이어지는 음성샘플값으로부터 다음 프레임 n+1 의 끝점을 구한다. 이러한 방식을 재귀적인 음소분리 방식이라고 한다. 그리고, 각 프레임에서 끝점을 구하기 위해서는 끝점의 좌표를 추정해야 할 파라메터로 하고, 그 주변의 음성샘플 값을 관찰 값으로 하여 EM(Expectation and Maximization) 알고리즘을 이용한다. 이 EM 알고리즘을 이용한 재귀적인 음소분리 방식을 실제 음성 DB 로부터 음소쌍을 추출하여 테스트 했을 때 약 5 회의 EM 반복 후에 경계간으로 수렴함을 볼 수 있었다.

• ETRI Journal
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• v.41 no.3
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• pp.298-307
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• 2019

This paper investigates the use of the inverse-free sparse Bayesian learning (SBL) approach for peak-to-average power ratio (PAPR) reduction in orthogonal frequency-division multiplexing (OFDM)-based multiuser massive multiple-input multiple-output (MIMO) systems. The Bayesian inference method employs a truncated Gaussian mixture prior for the sought-after low-PAPR signal. To learn the prior signal, associated hyperparameters and underlying statistical parameters, we use the variational expectation-maximization (EM) iterative algorithm. The matrix inversion involved in the expectation step (E-step) is averted by invoking a relaxed evidence lower bound (relaxed-ELBO). The resulting inverse-free SBL algorithm has a much lower complexity than the standard SBL algorithm. Numerical experiments confirm the substantial improvement over existing methods in terms of PAPR reduction for different MIMO configurations.

• 대한핵의학회:학술대회논문집
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• 2000.05a
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• pp.84-84
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• 2000

• Proceedings of the IEEK Conference
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• 1999.11a
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• pp.569-572
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• 1999

In this paper we present a color image segmentation algorithm based on statistical models. A novel deterministic annealing Expectation Maximization(EM) formula is derived to estimate the parameters of the Gaussian Mixture Model(GMM) which represents the multi-colored objects statistically. The experimental results show that the proposed deterministic annealing EM is a global optimal solution for the ML parameter estimation and the image field is segmented efficiently by using the parameter estimates.

• Proceedings of the Korean Nuclear Society Conference
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• 1996.05b
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• pp.719-724
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• 1996

본 연구는 유지, 보수되는 상시 작동 시스템에 대한 신뢰성 분석을 위해 동적 불완전 수리모형 및 분석 절차의 개발을 수행하였다. 또한 수리 상태에 대한 데이타가 완전히 잠재적(masked)이라 하더라도 기본 분포(base-line distribution)가 와이블 분포라는 가정하에 모수적 추정 절차를 개발하였다. 개발된 추정 절차는 기본적으로 EM(Expectation and Maximization : EM) 알고리즘의 틀(framework)을 유지하고 있다. 특히 최소 수리 특성으로 인해 분포가 변화함에 따라 발생하는 추정의 어려움을 해결하기 위해 데이타 변환(transformation)식을 제시하고 이러한 변환 데이타를 사용함으로써 추가적 데이타의 요구없이 잠재적 데이타를 사용하여 추정을 가능하게 하는 모수 추정 알고리즘을 제시하였다.

• Proceedings of the Korean Information Science Society Conference
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• 2001.04b
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• pp.529-531
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• 2001

We present an expectation-maximization algorithm for principal component analysis via orthogonalization. The algorithm finds actual principal components, whereas previously proposed EM algorithms can only find principal subspace. New algorithm is simple and more efficient thant probabilistic PCA specially in noiseless cases. Conventional PCA needs computation of inverse of the covariance matrices, which makes the algorithm prohibitively expensive when the dimensions of data space is large. This EM algorithm is very powerful for high dimensional data when only a few principal components are needed.

• Proceedings of the Korean Information Science Society Conference
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• 1998.10c
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• pp.360-362
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• 1998

In the original mixtures of experts framework, the parameters of the network are determined by gradient descent, which is naturally slow. In [2], the Expectation-Maximization(EM) algorithm is used instead, to obtain the network parameters, resulting in substantially reduced training times. This paper presents the new EM algorithm for prediction. We show that an Efficient training algorithm may be derived for the HME network. To verify the utility of the algorithm we look at specific examples in time series prediction. The application of the new EM algorithm to time series prediction has been quiet successful.

• Proceedings of the IEEK Conference
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• 2006.06a
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• pp.405-406
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• 2006

This paper proposes the new algorithm for the extraction of the corresponding points. Our algorithm is based on RANSAC(Random Sample Consensus) with EM(Expectation-Maximization). In the procedure of RANSAC, N-points are selected by the result of EM instead of the random selection. EM+SAC algorithm is applied to the correspondence for the mosaicing.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.13 no.11
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• pp.5311-5316
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• 2012

Single Photon Emission Computed tomography(SPECT) was performed on 13 healthy adults (average age: 39) to investigate the changes of cerebral blood flow according to brain imaging analysis algorithm. The acquired images were filtered and reconstructed through Filtered Back Projection (FBP) and Ordered Subset Expectation Maximization (OSEM). The brain distribution data of radiopharmaceuticals were compared using Statistical Parametric Mapping (SPM), and the changes of blood flow was expressed in Cluster. As a result, uptake rate was increased in Sub-gyral, Sub-Lobar, Extra-Nuclear, Limbic lobe and Cingulate Gyrus, while uptake rate was decreased in Middle frontal gyrus, Inferior Frontal Gyrus and Precentral Gyrus. The discriminable SPM was shown according to cerebral blood flows in Cluster by the reconstruction algorithm.