GPU‐based prompt gamma ray imaging from boron neutron capture therapy

Yoon, Do-Kun; Jung, Jin‐Woo; Hong, Ki‐Ha; Lee, Keum Sil; Suh, Tae Suk

doi:10.1118/1.4903265

Cited by 12 publications

(8 citation statements)

References 22 publications

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“…Therefore, we used the ordered subset expectation maximization (OSEM) using a graphics processing unit (GPU) for fast image reconstruction. This algorithm which computed unified device architecture (CUDA) has been used several times in previous studies [ 18 ].…”

Section: Methodsmentioning

confidence: 99%

“…The modified OSEM reconstruction method was performed using eight subsets and five iterations. To reduce the computation time for image reconstruction, parts of the domain were assigned to each subset calculation without overlap [ 18 ]. In our reconstruction process, there is no pre-process that includes filtering.…”

Section: Methodsmentioning

confidence: 99%

“…Although the conventional OSEM has several subsets for the maximum likelihood, the results of the reconstruction are almost same with those of MLEM. In our previous study, to reduce the image reconstruction time, we used the GPU-based computation method [ 18 ]. For the GPU system, there are several threads that can perform computations individually.…”

Section: Methodsmentioning

confidence: 99%

“…With this modified reconstruction algorithm that uses a GPU, we can acquire the prompt gamma ray at very fast speeds [ 18 ]. However, the time for image acquisition is limited to the treatment time.…”

Section: Methodsmentioning

confidence: 99%

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Quantitative analysis of prompt gamma ray imaging during proton boron fusion therapy according to boron concentration

Shin

Kim

et al. 2017

Oncotarget

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The purpose of this study is to evaluate the prompt gamma ray imaging technique according to the clinical boron concentration range during proton boron fusion therapy (PBFT). To acquire a prompt gamma ray image from 32 projections, we simulated four head single photon emission computed tomography and a proton beam nozzle using a Monte Carlo simulation. We used modified ordered subset expectation maximization reconstruction algorithm with a graphic processing unit for fast image acquisition. Boron concentration was set as 20 to 100 μg at intervals of 20 μg. For quantitative analysis of the prompt gamma ray image, we acquired an image profile drawn through two boron uptake regions (BURs) and calculated the contrast value, signal-to-noise ratio (SNR), and difference between the physical target volume and volume of the prompt gamma ray image. The relative counts of prompt gamma rays were noticeably increased with increasing boron concentration. Although the intensities on the image profiles showed a similar tendency according to the boron concentration, the SNR and contrast value were improved with increasing boron concentration. This study suggests that a tumor monitoring technique using prompt gamma ray detection can be clinically applicable even if the boron concentration is relatively low.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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Quantitative analysis of prompt gamma ray imaging during proton boron fusion therapy according to boron concentration

Shin

Kim

et al. 2017

Oncotarget

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“…The specification of the detector and source definition is referred from the previous study. 10,11) The water phantom which can emit the gamma ray of 662 keV (Cs-137) at the specific region was used to be scanned using SPECT in the simulation. This cylindrical water phantom (diameter=18 cm, height=6 cm, density=1 g/cm 3 ) involved three radioisotope uptake regions (RURs).…”

Section: Methodsmentioning

confidence: 99%

SPECT Image Analysis Using Computational ROC Curve Based on Threshold Setup

et al. 2017

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We proposed the objective ROC analysis method based on the setting of threshold value for evaluation of single photon emission computed tomography (SPECT) image. This proposed ROC analysis method uses the quantification computational threshold value to each signal on the SPECT image. The SPECT images for this study were acquired by using Monte Carlo n-particle extended simulation code (MCNPX, Ver. 2.6.0, Los Alamos National Laboratory, USA). The basic SPECT detectors and specific water phantom were realized in the simulation, and we could get the simulation results by the simulation operation. We tried to analyze the reconstructed images using threshold value application based objective ROC method. We can get the accuracy information of reconstructed region in the image. This proposed ROC technique can be helpful when we have to evaluate the weak signal for the NM image. In this study, the proposed threshold value based computational ROC analysis method can provide better objectivity than the conventional ROC analysis method.

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