• The Korean Journal of Nuclear Medicine Technology
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• v.16 no.1
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• pp.12-16
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• 2012

Purpose : In conventional PET image reconstruction, iterative reconstruction methods such as OSEM (Ordered Subsets Expectation Maximization) have now generally replaced traditional analytic methods such as filtered back-projection. This includes improvements in components of the system model geometry, fully 3D scatter and low noise randoms estimates. SharpIR algorithm is to improve PET image contrast to noise by incorporating information about the PET detector response into the 3D iterative reconstruction algorithm. The aim of this study is evaluation of SharpIR reconstruction method in PET/CT. Materials and Methods: For the measurement of detector response for the spatial resolution, a capillary tube was filled with FDG and scanned at varying distances from the iso-center (5, 10, 15, 20 cm). To measure image quality for contrast recovery, the NEMA IEC body phantom (Data Spectrum Corporation, Hillsborough, NC) with diameters of 1, 13, 17 and 22 for simulating hot and 28 and 37 mm for simulating cold lesions. A solution of 5.4 kBq/mL of $^{18}F$-FDG in water was used as a radioactive background obtaining a lesion of background ratio of 4.0. Images were reconstructed with VUE point HD and VUE point HD using SharpIR reconstruction algorithm. For the clinical evaluation, a whole body FDG scan acquired and to demonstrate contrast recovery, ROIs were drawn on a metabolic hot spot and also on a uniform region of the liver. Images were reconstructed with function of varying iteration number (1~10). Results: The result of increases axial distance from iso-center, full width at half maximum (FWHM) is also increasing in VUE point HD reconstruction image. Even showed an increasing distances constant FWHM. VUE point HD with SharpIR than VUE point HD showed improves contrast recovery in phantom and clinical study. Conclusion: By incorporating more information about the detector system response, the SharpIR algorithm improves the accuracy of underlying model used in VUE point HD. SharpIR algorithm improve spatial resolution for a line source in air, and improves contrast recovery at equivalent noise levels in phantoms and clinical studies. Therefore, SharpIR algorithm can be applied as through a longitudinal study will be useful in clinical.

• Journal of the Korea Convergence Society
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• v.9 no.1
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• pp.433-440
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• 2018

Recently, MTV(metabolic tumor volume) has been used as indices of the whole tumor FDG uptake on FDG PET image but it is influenced by image reconstruction. The purpose of this study was to evaluate the correlation between actual volume and metabolic tumor volume applying different SUVmax threshold for different reconstruction algorithm on phantom study. Measurement were performed on a Siemens Biograph mCT40 using a NEMA IEC body phantom containing different size six spheres filled with F18-FDG applying four SBRs (4:1, 8:1, 10:1, 20:1). Images reconstructed four algorithms (OSEM3D, OSEM3D+PSF, OSEM3D +TOF, OSEM3D+TOF+PSF) and MTV were measured with different SUVmax threshold. Overall, the use of increasing thresholds result in decreasing MTV. and increasing the signal to background ratio decreased MTV by applying same SUVmax threshold. The 40% SUVmax threshold gave the best concordance between measured and actual volume in PSF and PSF+TOF reconstruction image. and the 45% threshold had the best correlation between the volume measured and actual volume in OSEM3D and TOF reconstruction image. we believe that this study will be used when the measurement of MTV applying various reconstruction image.

• The Korean Journal of Nuclear Medicine Technology
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• v.18 no.1
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• pp.115-121
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• 2014

Purpose: In addition to improving the quality of the PET image, through much research, the development of various programs are performed. Astonish TF reconstruction techniques by Philips can confirm the improved contrast of the lesion. Also, It's image reconstruction of 2 mm is possible with rapid reconstruction rate than conventional. In this study, we compared and evaluated Standardized Uptake Value (SUV) in accordance with the 2 mm reconstruction techniques and traditional 4 mm from the $^{18}F-FDG$ PET whole body image. Materials and Methods: In the phantom experiment, NEMA IEC body phantom (sphere: 10, 13, 17, 22, 28, 37 mm) was used to obtain images by using GEMINI TF 64 PET/CT (Philips, Cleveland, USA). Also, In the clinical images, we performed $^{18}F-FDG$ PET/CT examination to 30 women (age: $55.1{\pm}11.3$, BMI: $24.1{\pm}2.9$) with a diagnosis of breast cancer. After that, we reconstructed images in 2 mm and 4 mm respectively. The region of interest was drawn to acquired images. Since then, we measured SUV and statistically analyzed with SPSS ver.18 by using EBW (Extended Brilliance Workstation) NM ver.1.0. Results: After analyzing the result of the phantom study, there was a tendency that the bigger hot sphere size, the higher SUV. If you compared the 2 mm reconstruction techniques to 4 mm, it increased 95.78% in 10 mm, 50.60% in 13 mm, 25.00% in 17 mm, 30.04% in 22 mm, 31.81% in 28 mm, and 27.84% in 37 mm. Through the result of the analysis of the 2 mm reconstruction techniques and 4 mm in clinical images, it appeared that SUV of 2 mm was higher than that of 4 mm. Also the smaller the volume was, the more the change rate increased. Conclusion: After analyzing the result of the clinical picture and phantom experiments applied by Astonish TF reconstruction techniques, as the size of the volume was small, the change rate of the SUV increased. Therefore, it was necessary to further research about the SUV correction for accurate and active utilization of 2 mm reconstruction techniques which had excellent lesion discrimination ability and contrast in clinic.

• Nuclear Medicine and Molecular Imaging
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• v.42 no.1
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• pp.52-60
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• 2008

Purpose: To establish the methods for sinogram formation and correction in order to appropriately apply the filtered backprojection (FBP) reconstruction algorithm to the data acquired using PET scanner with multiple scintillation crystal layers. Materials and Methods: Formation for raw PET data storage and conversion methods from listmode data to histogram and sinogram were optimized. To solve the various problems occurred while the raw histogram was converted into sinogram, optimal sampling strategy and sampling efficiency correction method were investigated. Gap compensation methods that is unique in this system were also investigated. All the sinogram data were reconstructed using 20 filtered backprojection algorithm and compared to estimate the improvements by the correction algorithms. Results: Optimal radial sampling interval and number of angular samples in terms of the sampling theorem and sampling efficiency correction algorithm were pitch/2 and 120, respectively. By applying the sampling efficiency correction and gap compensation, artifacts and background noise on the reconstructed image could be reduced. Conclusion: Conversion method from the histogram to sinogram was investigated for the FBP reconstruction of data acquired using multiple scintillation crystal layers. This method will be useful for the fast 20 reconstruction of multiple crystal layer PET data.

• Journal of Korea Multimedia Society
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• v.8 no.12
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• pp.1565-1571
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• 2005

Images reconstructed with Maximum-Likelihood Expectation-Maximization (MLEM) algorithm have been observed to have checkerboard effects and have noise artifacts near edges as iterations proceed. To compensate this ill-posed nature, numerous penalized maximum-likelihood methods have been proposed. We suggest a simple algorithm of applying edge detecting process to the MLEM and Bayesian Expectation-Maximization (BEM) to reduce the noise artifacts near edges and remove checkerboard effects. We have shown by simulation that this algorithm removes checkerboard effects and improves the clarity of the reconstructed image and has good properties based on root mean square error (RMS).

• The Korean Journal of Nuclear Medicine Technology
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• v.18 no.1
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• pp.69-75
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• 2014

Purpose: The aim of this study was to evaluate changes of quantitative and semi-quantitative myocardial perfusion indices and image quality by image reconstruction methods in $^{13}N$-ammonia ($^{13}N-NH_3$) myocardial perfusion PET/CT. Materials and Methods: Data of 14 (8 men, 6 women) patients underwent rest and adenosine stress $^{13}N-NH_3$ PET/CT (Biograph TruePoint 40 with TrueV, Siemens) were collected. Listmode scans were acquired for 10 minutes by injecting 370MBq of $^{13}N-NH_3$. Dynamic and static reconstruction was performed by use of FBP, iterative2D (2D), iterative3D (3D) and iterative TrueX (TrueX) algorithm. Coronary flow reserve (CFR) of dynamic reconstruction data, extent(%) and total perfusion deficit (TPD) (%) measured in sum of 4-10 minutes scan were evaluated by comparing with 2D method which was recommended by vendor. The image quality of each reconstructed data was compared and evaluated by five nuclear medicine physicians through a blind test. Results: CFR were lower in TrueX 18.68% (P=0.0002), FBP 4.35% (P=0.1243) and higher in 3D 7.91% (P<0.0001). As semi-quantitative values, extent and TPD of stress were higher in 3D 3.07%p (P=0.001), 2.36%p (P=0.0002), FBP 1.93%p (P=0.4275), 1.57%p (P=0.4595), TrueX 5.43%p (P=0.0003), 3.93%p (P<0.0001). Extent and TPD of rest were lower in FBP 0.86%p (P=0.1953), 0.57%p (P=0.2053) and higher in 3D 3.21%p (P=0.0006), 2.57%p (P=0.0001) and TrueX 5.36%p (P<0.0001), 4.36%p (P<0.0001). Based on the results of the blind test for image resolution and noise from the snapshot, 3D obtained the highest score, followed by 2D, TrueX and FBP. Conclusion: We found that quantitative and semi-quantitative myocardial perfusion values could be under- or over-estimated according to the reconstruction algorithm in $^{13}N-NH_3$ PET/CT. Therefore, proper dynamic and static reconstruction method should be established to provide accurate myocardial perfusion value.

• Journal of Korea Multimedia Society
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• v.20 no.6
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• pp.865-872
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• 2017

Recent advance in electronics and scintillators makes it possible to utilize the time-of-flight (TOF) information in improving image reconstruction of positron emission tomography(PET). In this paper, we propose a TOF-based fast image reconstruction method for PET. The proposed method uses the deconvolution of TOF data for each angle view and the rotational averaging of deconvolved images. Simulation results show an improved performance of the proposed method, as compared with filtered backprojection (FBP) method, TOF-FBP, and TOF version of expectation-maximization(EM) methods. Simulation results also show a great potentiality of the proposed method in limited angle tomography applications.

• The Korean Journal of Nuclear Medicine Technology
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• v.16 no.2
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• pp.110-114
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• 2012

Purpose : PET/CT is widely used for early checking up of cancer and following up of pre and post operation. Image reconstruction method is advanced with mechanical function. We want to evaluate image quality of each reconstruction program based on time of flight (TOF). Materials and Methods : After acquiring phantom images during 2 minutes with Gemini TF (Philips, USA), Biograph mCT (Siemens, USA) and Discovery 690 (GE, USA), we reconstructed image applied to Astonish TF (Philips, USA), ultraHD PET (Siemens, USA), Sharp IR (GE, USA) and not applied. inside of Flangeless Esser PET phantom (Data Spectrum corp., USA) was filled with $^{18}F$-FDG 1.11 kBq/ml (30 Ci/ml) and 4 hot inserts (8. 12. 16. 25 mm) were filled with 8.88 kBq/ml (240 ${\mu}Ci/ml$) the ratio of background activity and hot inserts activity was 1 : 8. Inside of triple line phantom (Data Spectrum corp., USA) was filled with $^{18}F$-FDG 37 MBq/ml (1 mCi). Three of lines were filled with 0.37 MBq (100 ${\mu}Ci$). Contrast ratio and background variability were acquired from reconstruction image used Flangeless Esser PET phantom and resolution was acquired from reconstruction image used triple line phantom. Results : The contrast ratio of image which was not applied to Astonish TF was 8.69, 12.28, 19.31, 25.80% in phantom lid of which size was 8, 12, 16, 25 mm and it which was applied to Astonish TF was 6.24, 13.24, 19.55, 27.60%. It which was not applied to ultraHD PET was 4.94, 12.68, 22.09, 30.14%, it which was applied to ultraHD PET was 4.76, 13.23, 23.72, 31.65%. It which was not applied to SharpIR was 13.18, 17.44, 28.76, 34.67%, it which was applied to SharpIR was 13.15, 18.32, 30.33, 35.73%. The background variability of image which was not applied to Astonish TF was 5.51, 5.42, 7.13, 6.28%. it which was applied to Astonish TF was 7.81, 7.94, 6.40 6.28%. It which was not applied to ultraHD PET was 6.46, 6.63, 5.33, 5.21%, it which was applied to ultraHD PET was 6.08, 6.08, 4.45, 4.58%. It which was not applied to SharpIR was 5.93, 4.82, 4.45, 5.09%, it which was applied to SharpIR was 4.80, 3.92, 3.63, 4.50%. The resolution of phantom line of which location was upper, center, right, which was not applied to Astonish TF was 10.77, 11.54, 9.34 mm it which was applied to Astonish TF was 9.54, 8.90, 8.88 mm. It which was not applied to ultraHD PET was 7.84, 6.95, 8.32 mm, it which was applied to ultraHD PET was 7.51, 6.66, 8.27 mm. It which was not applied to SharpIR was 9.35, 8.69, 8.99, it which was applied to SharpIR was 9.88, 9.18, 9.00 mm. Conclusion : Image quality was advanced generally while reconstruction program which is based on time of flight was used. Futhermore difference of result compared each manufacture reconstruction program showed up, however this is caused by specification of instrument of each manufacture and difference of reconstruction algorithm. Therefore we need further examination to find out appropriate reconstruction condition while using reconstruction program used for advance of image quality.

• Journal of the Korean Physical Society
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• v.73 no.11
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• pp.1764-1773
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• 2018

A-PET is a quad-head PET scanner developed for use in small-animal imaging. The dimensions of its volumetric field of view (FOV) are $46.1{\times}46.1{\times}46.1mm^3$ and the gap between the detector modules has been minimized in order to provide a highly sensitive system. However, such a small FOV together with the quad-head geometry causes image quality degradation. The main factor related to image degradation for the quad-head PET is the mispositioning of events caused by the penetration effect in the detector. In this paper, we propose a precise method for modelling the system at the high spatial resolution of the A-PET using a LOR (line of response) based ML-EM (maximum likelihood expectation maximization) that allows for penetration effects. The proposed system model provides the detection probability of every possible ray-path via crystal sampling methods. For the ray-path sampling, the sub-LORs are defined by connecting the sampling points of the crystal pair. We incorporate the detection probability of each sub-LOR into the model by calculating the penetration effect. For comparison, we used a standard LOR-based model and a Monte Carlo-based modeling approach, and evaluated the reconstructed images using both the National Electrical Manufacturers Association NU 4-2008 standards and the Geant4 Application for Tomographic Emission simulation toolkit (GATE). An average full width at half maximum (FWHM) at different locations of 1.77 mm and 1.79 mm are obtained using the proposed system model and standard LOR system model, which does not include penetration effects, respectively. The standard deviation of the uniform region in the NEMA image quality phantom is 2.14% for the proposed method and 14.3% for the LOR system model, indicating that the proposed model out-performs the standard LOR-based model.

• IEIE Transactions on Smart Processing and Computing
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• v.4 no.6
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• pp.384-390
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• 2015

There are two main artifacts in reconstructed images from in-beam positron emission tomography (PET). Unlike generic PET, in-beam PET uses the annihilation photons that occur during heavy ion therapy. Therefore, the geometry of in-beam PET is not a full ring, but a partial ring that has one or two openings around the rings in order for the hadrons to arrive at the tumor without prevention of detector blocks. This causes truncation in the projection data due to an absence of detector modules in the openings. The other is a ring artifact caused by the gaps between detector modules also found in generic PET. To sum up, in-beam PET has two kinds of gap: openings for hadrons, and gaps between the modules. We acquired three types of simulation results from a PET system: full-ring, C-ring and dual head. In this study, we aim to compensate for the artifacts that come from the two types of gap. In the case of truncation, we propose a method that uses prior knowledge of the location where annihilations occur, and we applied the discrete-cosine transform (DCT) gap-filling method proposed by Tuna et al. for inter-detector gap.