Language：Japanese   Publisher：(公社)日本放射線技術学会  

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Authorship：Lead author   Language：Japanese   Publishing type：Research paper (scientific journal)  

PURPOSE: We developed an easy-to-use method to generate computed tomography (CT) images that simulate the images obtained when using an actual scanner. METHODS: The developed method generates images by simulating the data acquisition and image reconstruction processes of a scanner from a linear attenuation coefficient map of an object numerically generated. This approach is similar to general image simulation methods. However, we introduced adjustable parameters for the CT data acquisition process, for example, parameters related to X-ray attenuation in the anode of the X-ray tube and the bowtie filter. These parameters were optimized in advance by minimizing the difference between the simulated and measured images of a water phantom. To verify the validity of the developed method, a simulated image was generated for a torso phantom and then compared with the measured image of the phantom obtained using the scanner. RESULTS: The simulated and measured images of the torso phantom were in good agreement. The spatial resolution and noise characteristics of these two images were also comparable, further indicating the accuracy of the developed method. CONCLUSION: In the existing methods, various information/data related to an actual scanner, including difficult-to-acquire ones, were essential for image simulation. In the developed method, instead of determining the difficult-to-acquire information/data, we introduced adjustable parameters. Therefore, the developed method was easier to use than the existing methods.

DOI： 10.6009/jjrt.2022-1222

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Authorship：Lead author   Language：Japanese   Publishing type：Research paper (scientific journal)  

PURPOSE: Various approaches in noise power spectrum (NPS) analysis are currently used for measuring a patient's longitudinal (z-direction) NPS from three-dimensional (3D) CT volume data. The purpose of this study was to clarify the relationship between those NPSs and 3D-NPS based on the central slice theorem. METHODS: We defined the 3D-NPS(fx, fy, fz) that was calculated by 3D Fourier transform (FT) from 3D noise data (3D-Noise(x, y, z), x-y scan plane). Here, fx, fy and fz are spatial frequencies corresponding to the axes of x, y and z, respectively. Based on the central slice theorem, we described three relationships as follows. (1) The fz-directional NPS calculated from the 3D-Noise(x=0, y=0, z) is equal to the profile obtained by projecting 3D-NPS(fx, fy, fz) in fx- and fy-directions. (2) The fz-directional NPS calculated from the profile obtained by projecting 3D-Noise(x=0, y, z) in the y-direction is equal to the profile at fy=0 in the data obtained by projecting 3D-NPS(fx, fy, fz) in the fx-direction. (3) The fz-directional NPS calculated from the profile obtained by projecting 3D-Noise(x, y, z) in x and y-directions is equal to the profile of 3D-NPS(fx=0, fy=0, fz). To verify them, we compared the NPSs measured from actual 3D noise data that were obtained using a cylindrical water phantom. RESULTS: In each relationship (1)-(3), the fz-directional NPS matched the profile obtained from the 3D-NPS(fx, fy, fz). CONCLUSION: Based on the central slice theorem, we clarified the relationships between fz-directional NPSs and 3D-NPS. We should understand them and then consider which method should be used for fz-directional NPS measurement.

DOI： 10.6009/jjrt.2022-1267

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Authorship：Lead author   Language：Japanese   Publishing type：Research paper (scientific journal)  

PURPOSE: The noise power spectrum (NPS) of a CT scanner is commonly measured from a single noise image. However, since CT images are three-dimensional (3D) volume data, they have 3D noise characteristics (3D-NPS). In this study, we clarify the relationship among NPSs measured by various approaches in NPS analysis based on the central slice theorem. Its validity is verified by the NPS measurements using actual 3D noise data. METHODS: We defined the NPSz-projection(fx, fy) that was calculated by the 2D Fourier transform (FT) from the 2D projection of 3D noise data in the patient longitudinal direction, the 3D-NPS(fx, fy, fz) that was calculated by the 3D-FT from the 3D noise data, and the 2D-NPS(fx, fy) that was calculated by the 2D-FT from a single noise image; fx, fy, and fz are spatial frequencies corresponding to the axes of x, y, and z in the reconstructed CT volume, respectively. Based on the central slice theorem, we described that the NPSz-projection(fx, fy=0) was equal to the 3D-NPS(fx, fy=0, fz=0), and the NPS(2D-NPS(fx, fy=0)) was different from the 3D-NPS(fx, fy=0, fz=0). To verify them, we compared the NPSs calculated from actual 3D noise data that were obtained using a cylindrical water phantom. RESULTS: The 3D-NPS(fx, fy=0, fz=0) matched the NPSz-projection(fx, fy=0) and was different from the 2D-NPS(fx, fy=0). CONCLUSION: Based on the central slice theorem, we clarified the relationship among NPSs measured by various approaches in NPS analysis; it is important to understand this and then select an appropriate noise data handling and NPS measurement method.

DOI： 10.6009/jjrt.2022-1217

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Language：Japanese   Publisher：(公社)日本放射線技術学会  

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Language：Japanese   Publisher：(公社)日本放射線技術学会  

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Authorship：Lead author   Language：English   Publishing type：Research paper (scientific journal)   Publisher：Springer Science and Business Media LLC  

A novel method for measuring the slice sensitivity profile (SSP) of computed tomography (CT) images reconstructed using an iterative reconstruction (IR) algorithm is proposed herein. A phantom that included a low-contrast spherical object was scanned and consecutive cross-sectional images were reconstructed. The mean CT values in a region including the sphere were measured for all images and plotted as a function of slice position along the longitudinal [Formula: see text] direction to yield a mean CT value profile [Formula: see text]. Next, we numerically generated an object function corresponding to the sphere and obtained the mean CT value profile [Formula: see text]. Subsequently, the SSP was modeled as a product of the Gaussian and cosine functions. We convolved [Formula: see text] with the modeled SSP to obtain [Formula: see text]. The difference between [Formula: see text] and [Formula: see text] was evaluated using the root mean square error (RMSE), which was minimized via optimization of the SSP model parameters. To validate the methodology, we first used filtered back projection (FBP) images to compare the SSPs determined using the proposed and standard coin methods. Subsequently, the proposed method was applied to measure the SSPs of four types of IR algorithms in two scanners. The SSPs of the FBP images determined using the proposed and coin methods showed good agreement. Additionally, in the SSP measurements using the proposed method, [Formula: see text] agreed well with [Formula: see text] for every IR algorithm. The RMSEs for all measurements were less than 0.7 HU, indicating the accuracy of the SSPs. Thus, the proposed method is effective for obtaining valid SSPs.

DOI： 10.1007/s12194-021-00636-0

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Other Link： https://link.springer.com/article/10.1007/s12194-021-00636-0/fulltext.html

Language：Japanese   Publisher：(公社)日本放射線技術学会  

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Language：Japanese   Publisher：(公社)日本放射線技術学会  

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Language：Japanese   Publishing type：Research paper (scientific journal)   Publisher：(公社)日本放射線技術学会  

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Other Link： https://search.jamas.or.jp/index.php?module=Default&action=Link&pub_year=2021&ichushi_jid=J01192&link_issn=&doc_id=20210602430001&doc_link_id=130008040225&url=https%3A%2F%2Fci.nii.ac.jp%2Fnaid%2F130008040225&type=CiNii&icon=https%3A%2F%2Fjk04.jamas.or.jp%2Ficon%2F00003_1.gif

Authorship：Lead author   Language：English   Publishing type：Research paper (scientific journal)   Publisher：Wiley  

PURPOSE: A method for measuring the slice sensitivity profile (SSP) of computed tomography (CT) images reconstructed with iterative reconstruction (IR) algorithms was reported by the AAPM Task Group 233 (TG233). In this method, the phantom plane edge is slightly slanted with respect to the scan plane to obtain a composite oversampled edge-spread function (ESF). However, it is expected that a fine-sampled ESF can be obtained directly from images reconstructed with a small slice increment without slanting the edge plane. This study aimed to investigate the validity of using a non-slanted edge plane. METHODS: In the proposed non-slanted edge method, the phantom was positioned so that the plane edge was perpendicular to the longitudinal z-axis, and images were reconstructed with a 1-mm slice thickness and 0.1-mm increment. The mean CT value was obtained in each slice and plotted as a function of slice position along the z-axis, thereby generating the ESF. The SSP was calculated from the ESF by differentiation. In the TG 233-recommended slanted edge method, the SSP was obtained by following the procedure described in the TG233 report. To validate the methodology, we first used filtered back projection (FBP) images to compare SSPs obtained using the non-slanted edge method, slanted edge method, and a standard method using a high-contrast thin object (coin). Next, for two types of IR algorithms, we compared the SSPs obtained using the non-slanted and slanted edge methods. RESULTS: For the FBP images, the SSP measured using the non-slanted edge method agreed well with SSPs measured using the coin and slanted edge methods. For the IR images, the SSPs measured using the non-slanted and slanted edge methods showed good agreement. CONCLUSIONS: The non-slanted edge method was demonstrated to be valid. The simplicity and practicality of the method allows routine and accurate determination of the SSP.

DOI： 10.1002/mp.14668

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Other Link： https://onlinelibrary.wiley.com/doi/full-xml/10.1002/mp.14668

Language：English   Publishing type：Research paper (scientific journal)   Publisher：Wiley  

PURPOSE: We sought to develop machine learning models to detect multileaf collimator (MLC) modeling errors with the use of radiomic features of fluence maps measured in patient-specific quality assurance (QA) for intensity-modulated radiation therapy (IMRT) with an electric portal imaging device (EPID). METHODS: Fluence maps measured with EPID for 38 beams from 19 clinical IMRT plans were assessed. Plans with various degrees of error in MLC modeling parameters [i.e., MLC transmission factor (TF) and dosimetric leaf gap (DLG)] and plans with an MLC positional error for comparison were created. For a total of 152 error plans for each type of error, we calculated fluence difference maps for each beam by subtracting the calculated maps from the measured maps. A total of 837 radiomic features were extracted from each fluence difference map, and we determined the number of features used for the training dataset in the machine learning models by using random forest regression. Machine learning models using the five typical algorithms [decision tree, k-nearest neighbor (kNN), support vector machine (SVM), logistic regression, and random forest] for binary classification between the error-free plan and the plan with the corresponding error for each type of error were developed. We used part of the total dataset to perform fourfold cross-validation to tune the models, and we used the remaining test dataset to evaluate the performance of the developed models. A gamma analysis was also performed between the measured and calculated fluence maps with the criteria of 3%/2 and 2%/2 mm for all of the types of error. RESULTS: The radiomic features and its optimal number were similar for the models for the TF and the DLG error detection, which was different from the MLC positional error. The highest sensitivity was obtained as 0.913 for the TF error with SVM and logistic regression, 0.978 for the DLG error with kNN and SVM, and 1.000 for the MLC positional error with kNN, SVM, and random forest. The highest specificity was obtained as 1.000 for the TF error with a decision tree, SVM, and logistic regression, 1.000 for the DLG error with a decision tree, logistic regression, and random forest, and 0.909 for the MLC positional error with a decision tree and logistic regression. The gamma analysis showed the poorest performance in which sensitivities were 0.737 for the TF error and the DLG error and 0.882 for the MLC positional error for 3%/2 mm. The addition of another type of error to fluence maps significantly reduced the sensitivity for the TF and the DLG error, whereas no effect was observed for the MLC positional error detection. CONCLUSIONS: Compared to the conventional gamma analysis, the radiomics-based machine learning models showed higher sensitivity and specificity in detecting a single type of the MLC modeling error and the MLC positional error. Although the developed models need further improvement for detecting multiple types of error, radiomics-based IMRT QA was shown to be a promising approach for detecting the MLC modeling error.

DOI： 10.1002/mp.14699

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Language：Japanese   Publisher：(公社)日本放射線技術学会  

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Authorship：Lead author   Language：Japanese   Publishing type：Research paper (scientific journal)  

PURPOSE: The method using a numerical slit (slit method) is used commonly to obtain the one-dimensional (1D) noise power spectrum (NPS) in computed tomography. However, the relationship between the 1D-NPS obtained by the slit method and the original two-dimensional (2D) NPS derived by the 2D Fourier transformation has not been elucidated clearly. The purpose of this study was to clarify their relationship based on the well-known central slice theorem (projection slice theorem) and validate it using computer simulation analysis. METHODS: With the application of the central slice theorem, we described that the 1D-NPS obtained by the slit method was equal to the central slice (profile) in the 2D-NPS when we set the slit length to the maximum (i.e. the matrix size of the noise image). To verify this, we generated computer-simulated noise images with the known 2D-NPS (true 2D-NPS). From those images, we obtained the 1D-NPS that was obtained by the slit method and compared it with the central slice in the true 2D-NPS. RESULTS: When we set the slit length to the maximum, the 1D-NPS obtained by the slit method showed good agreement with the central slice in the true 2D-NPS. CONCLUSION: We clarified the relationship between the 1D-NPS obtained by the slit method and the 2D-NPS using a theoretical approach and the computer simulation. We had to maximize the slit length to achieve the accurate measurement of the 1D-NPS using the slit method.

DOI： 10.6009/jjrt.2021_JSRT_77.8.828

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Language：Japanese   Publisher：新潟大学医学部保健学科  

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Other Link： https://search.jamas.or.jp/index.php?module=Default&action=Link&pub_year=2020&ichushi_jid=J06421&link_issn=&doc_id=20200507530002&doc_link_id=http%3A%2F%2Fhdl.handle.net%2F10191%2F00051611&url=http%3A%2F%2Fhdl.handle.net%2F10191%2F00051611&type=%90V%8A%83%91%E5%8Aw%81F%90V%8A%83%91%E5%8Aw%8Aw%8Fp%83%8A%83%7C%83W%83g%83%8A_Nuar&icon=https%3A%2F%2Fjk04.jamas.or.jp%2Ficon%2F80230_3.gif

Language：Japanese   Publisher：(公社)日本放射線技術学会  

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Authorship：Lead author   Language：English   Publishing type：Research paper (scientific journal)   Publisher：Wiley  

The circular-edge technique using a low-contrast cylindrical object is commonly used to measure the modulation transfer functions (MTFs) in computed tomography (CT) images reconstructed with iterative reconstruction (IR) algorithms. This method generally entails averaging multiple images of the cylinder to reduce the image noise. We suspected that the cylinder edge shape depicted in the IR images might exhibit slight deformation with respect to the true shape because of the intrinsic nonlinearity of IR algorithms. Image averaging can reduce the image noise, but does not effectively improve the deformation of the edge shape; thereby causing errors in the MTF measurements. We address this issue and propose a method to correct the MTF. We scanned a phantom including cylindrical objects with a CT scanner (Ingenuity Elite, Philips Healthcare). We obtained cylinder images with iterative model reconstruction (IMR) algorithms. The images suggested that the depicted edge shape deforms and fluctuates depending on slice positions. Because of this deformation, image averaging can potentially cause additional blurring. We define the deformation function D that describes the additional blurring, and obtain D by analyzing multiple images. The MTF measured by the circular-edge method (referred to as MTF') can be thought of as the multiplication of the true MTF by the Fourier transformation (FT) of D. We thus obtain the corrected MTF (MTFcorrected ) by dividing MTF' by the FT of D. We validate our correction method by comparing the calculated images based on the convolution theorem using MTF' and MTFcorrected with the actual images obtained with the scanner. The calculated image using MTFcorrected is more similar to the actual image compared with the image calculated using MTF', particularly in edge regions. We describe a pitfall in MTF measurement using the circular-edge technique with image averaging, and suggest a method to correct it.

DOI： 10.1002/acm2.12821

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Language：Japanese   Publishing type：Research paper (scientific journal)  

DOI： 10.11323/jjmp.39.4_77

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Language：Japanese   Publishing type：Research paper (scientific journal)   Publisher：(公社)日本医学物理学会  

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Language：Japanese   Publishing type：Research paper (scientific journal)   Publisher：(公社)日本医学物理学会  

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Authorship：Lead author   Language：English   Publishing type：Research paper (scientific journal)  

DOI： 10.1002/mp.12503

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DOI： 10.1259/bjr.20160313

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DOI： 10.1118/1.4953247

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Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：European Society of Radiology  

DOI： 10.1594/ecr2015/C-0946

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Authorship：Lead author   Language：English   Publishing type：Research paper (scientific journal)   Publisher：John Wiley and Sons Ltd  

DOI： 10.1120/jacmp.v14i4.3905

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DOI： 10.1118/1.3590363

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Authorship：Lead author   Language：Japanese   Publishing type：Research paper (scientific journal)   Publisher：Japan Society of Medical Physics  

We propose a new method of converting thin-slice CT images to thick-slice images without image raw data. Slice sensitivity profiles (SSPs) were measured in the thin- and thick-slice settings. The spatial frequency property in the slice direction of the thin-slice images was converted to that of thick-slice images, based on the spatial frequency response calculated using the each SSP. In this study, we used CT images of seven subjects obtained in lung cancer screening, with a 4-slice scanner. The images were reconstructed with slice thicknesses of 5 and 8 mm, and the SSPs were measured in the scanner for the two slice settings, respectively. We calculated 8 mm-thick images from the 5 mm-thick images by the proposed method, and compared them with the actual 8 mm-thick images; this difference was evaluated by the root mean square error (RMSE). The mean value of RMSEs for seven subjects was approximately 6.0 Hounsfield Unit (HU). In addition, we obtained 8 mm-thick images by a conventional technique of weighted average from the 5 mm-thick images; the mean value of RMSEs was approximately 10.0 HU. The proposed method was demonstrated to be effective for converting thin-slice images to thick-slice images accurately.

DOI： 10.11323/jjmp.30.1_3

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Other Link： https://jlc.jst.go.jp/DN/JLC/20019128137?from=CiNii

Language：Japanese   Publishing type：Research paper (scientific journal)  

DOI： 10.11323/jjmp.30.2_39

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Language：English   Publishing type：Research paper (scientific journal)  

DOI： 10.1118/1.3123762

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Language：Japanese   Publisher：(NPO)日本CT検診学会  

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Language：Japanese   Publisher：(公社)日本放射線技術学会  

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Language：Japanese   Publisher：(公社)日本放射線技術学会  

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Language：Japanese   Publisher：(公社)日本放射線技術学会-東北支部  

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Language：Japanese   Publisher：(公社)日本放射線技術学会-東北支部  

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Language：Japanese   Publisher：(公社)日本放射線技術学会-東北支部  

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Language：Japanese   Publisher：(公社)日本放射線技術学会-東北支部  

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Language：Japanese   Publisher：(公社)日本放射線技術学会-東北支部  

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Language：Japanese   Publisher：(公社)日本放射線技術学会-東北支部  

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Language：Japanese   Publisher：群馬県核医学研究会  

骨転移ありと診断された14例(男6例、女8例、平均62.1歳)を対象に、2機種のガンマカメラによるデータで99mTc-MDP骨シンチグラフィー診断支援ソフト(BONENAVI)処理を行い、その結果を比較した。ガンマカメラはGE Infinia Howkeye(GE)とPhilips Precedence(PP)を用い、解析はANN、BSI、Hot spot数の他、集積部位ごとのANNを用いて1症例あたりの偽陽性の数(FP)と全転移数に対する真陽性率(TPR)を算出した。転移数5ヶ所以上の多発転移群(8例)においては、TPR最大値はGEが0.87、PPが0.94、5ヶ所未満の少数転移群(6例)はそれぞれ0.96、0.47と差異が認められた。一方Patch処理が行われなかった5例(P-群)では、TPRの最大値がGE 0.85、PP 0.90で、特性曲線は比較的一致しており、ほぼ同程度の検出率となった。BSIは14例中12例でPPが、Hot spot数は9例でGEが高値となり、P-群ではこの傾向が顕著で、それぞれ5例中5例、4例であった。

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Language：Japanese   Publisher：(公社)日本放射線技術学会  

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Language：Japanese   Publisher：(公社)日本放射線技術学会  

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Other Link： https://search.jamas.or.jp/index.php?module=Default&action=Link&pub_year=2012&ichushi_jid=J01192&link_issn=&doc_id=20120606500006&doc_link_id=10030431285&url=https%3A%2F%2Fci.nii.ac.jp%2Fnaid%2F10030431285&type=CiNii&icon=https%3A%2F%2Fjk04.jamas.or.jp%2Ficon%2F00003_1.gif

Language：Japanese   Publisher：(公社)日本放射線技術学会  

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Language：Japanese   Publisher：(公社)日本放射線技術学会  

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Language：Japanese   Publisher：(公社)日本放射線技術学会-東北支部  

磁化率アーチファクトによる画像歪みの影響が、静磁場(Bo)方向と脳動脈瘤クリップの長軸方向とのなす角度に依存するか検討した。磁化率アーチファクトの影響を低減させるには、バンド幅が非常に重要であることが再認識された。Boと脳動脈瘤クリップの長軸方向とのなす角度が大きくなると、磁化率アーチファクト領域は大きくなり、0度〜90度でSE系が2.3倍、GE系が1.5倍となり、Boと垂直で最大となった。また、SE系に比べGE系の撮像法は磁化率アーチファクト領域が2.5〜3倍ほど大きくなった。Bo方向に対する脳動脈瘤クリップの長軸方向の角度が、磁化率アーチファクト領域に対し依存性があることが示唆された。

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Language：Japanese   Publisher：(NPO)日本核医学技術学会  

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Language：Japanese   Publisher：(公社)日本放射線技術学会  

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Language：Japanese   Publisher：(NPO)日本CT検診学会  

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Language：Japanese   Publisher：(一社)日本医学物理学会  

胸部CTによる肺内結節に対する精度の高い診断や、三次元的な評価を行う際、濃度閾値が重要な因子の一つになる場合が多い。結節を想定した球体を被写体関数に設定し、PSF(点拡がり関数)を用いた三次元画像シミュレーションに基づき、濃度閾値設定を行った。閾値設定と、それによって抽出される領域の体積との関係は、球体ファントムを用いた結果と比べ約5%の差異であったことから、シミュレーションの妥当性が確認された。結節の体積を正確に算出するための設定閾値は、結節の濃度が一定である場合でも大きさによって変動し、更に画像再構成関数、スライス厚およびスライス位置にも大きく依存することが定量的に示された。

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Language：Japanese   Publisher：(公社)日本放射線技術学会  

ガドキセト酸ナトリウムを用いた肝ダイナミックMRIの動脈優位相における画像アーチファクトについてシミュレーションした。撮像は、5通りの時刻範囲(撮像タイミングt1〜t5)で行われたと想定し、いずれも撮像に要する時間は、横軸の50%に相当する時間とした。Sequential法は、いずれのタイミングにおける画像も、明らかなアーチファクトはみられなかった。Centric法の場合、t4およびt5の場合、原画像に比べ、大きなボケが生じ、t2の場合、輪郭強調のような効果がみられた。Centric法では、sequentia1法に比べ、撮像タイミングによってアーチファクトが生じやすかった。撮像時間を25%にした。Sequential法での撮像の場合、明らかなアーチファクトはみられず、Centric法での撮像の場合、t4とt5の場合にボケが生じ、t2の場合に輪郭強調の効果がみられた。

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Other Link： https://search.jamas.or.jp/index.php?module=Default&action=Link&pub_year=2010&ichushi_jid=J01192&link_issn=&doc_id=20101001470490&doc_link_id=10026649024&url=https%3A%2F%2Fci.nii.ac.jp%2Fnaid%2F10026649024&type=CiNii&icon=https%3A%2F%2Fjk04.jamas.or.jp%2Ficon%2F00003_1.gif

Language：Japanese   Publisher：(公社)日本放射線技術学会-東北支部  

肝臓dynamic MRIにおける動脈優位相でのアーチファクトについて検討した。肝特異性造影剤Gd-EOB-DTPA(EOB)を用いたテストインジェクションのデータを参考に、模擬的に時間濃度曲線を作成した。時間濃度曲線上で、撮像時間および撮像開始のタイミングを想定し、その間の濃度曲線の変化パターンを用いた。この変化パターンに基づいて、k-spaceにおける位相エンコード方向の各データを変調した。撮像時間20秒の場合、セントリック法にて、造影剤の立ち上がり時に撮像が開始された場合、輪郭強調の影響がみられた。逆に立ち下がり時に撮像が開始された場合、ボケが生じた。これらの影響は、撮像時間が短い(10秒)方が小さかった。また、シーケンシャル法では、撮像時間、タイミングに関わらずこれらのアーチファクトはほとんどみられなかった。

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Language：Japanese   Publisher：(公社)日本放射線技術学会  

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Language：Japanese   Publisher：(NPO)日本CT検診学会  

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