[Method for Measuring Noise Power Spectrum Depending on X-ray Tube Angle in Spiral Orbit of Helical CT Scan].

PURPOSE: The noise power spectrum (NPS) in computed tomography (CT) images potentially varies with the X-ray tube angle in a spiral orbit of the helical scan. The purpose of this study was to propose a method for measuring the NPS for each angle of the X-ray tube. METHODS: Images of the water phantom were acquired using a helical scan. As a conventional method, we measured the two-dimensional (2D) NPS from each image and averaged them; the obtained 2D-NPS was referred to as NPSconventional. In the proposed method, we made the X-ray tube angle θ (0°≤θ<360°) to correspond to the image according to each slice position of the images that located within the travel distance of the CT scan table per 360° rotation of the X-ray tube. We obtained the 2D-NPS from each image and assigned the θ (0°, 30°, 60°, 90°, 120°, 150°, 180°); the obtained 2D-NPS was referred to as NPSsθ. The NPSsθ was compared to the NPSconventional. Also, we investigated the dependency of the NPSsθ on the θ. RESULTS: The NPSconventional was found to be isotropic, and in contrast, the NPSsθ was anisotropic. The NPSsθ showed a continuously rotational change while increasing the θ. There was an excellent correlation (R2>0.999) between the rotation angle of NPSθ and the θ. CONCLUSION: The proposed method was demonstrated to be effective for evaluating anisotropic noise characteristics depending on the X-ray tube angle.

Method for measuring noise-power spectrum independent of the effect of extracting the region of interest from a noise image.

This study aimed to evaluate the impact of region of interest (ROI) size on noise-power spectrum (NPS) measurement in computed tomography (CT) images and to propose a novel method for measuring NPS independent of ROI size. The NPS was measured using the conventional method with an ROI of size P × P pixels in a uniform region in the CT image; the NPS is referred to as NPSR=P. NPSsR=256, 128, 64, 32, 16, and 8 were obtained and compared to assess their dependency on ROI size. In the proposed method, the true NPS was numerically modeled as an NPSmodel, with adjustable parameters, and a noise image with the property of the NPSmodel was generated. From the generated noise image, the NPS was measured using the conventional method with a P × P pixel ROI size; the obtained NPS was referred to as NPS'R=P. The adjustable parameters of the NPSmodel were optimized such that NPS'R=P was most similar to NPSR=P. When NPS'R=P was almost equivalent to NPSR=P, the NPSmodel was considered the true NPS. NPSsR=256, 128, 64, 32, 16, and 8 obtained using the conventional method were dependent on the ROI size. Conversely, the NPSs (optimized NPSsmodel) measured using the proposed method were not dependent on the ROI size, even when a much smaller ROI (P = 16 or 8) was used. The proposed method for NPS measurement was confirmed to be precise, independent of the ROI size, and useful for measuring local NPSs using a small ROI.

[A Study of Longitudinal NPS Measurement in CT Images Based on the Central Cross-section Theorem].

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.

[A Study of 3D-NPS Analysis in CT Images Based on the Central Cross-section Theorem].

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.

Method for determining slice sensitivity profile of iterative reconstruction CT images using low-contrast sphere phantom.

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.

[Central Slice Theorem-based Relationship between 1D-NPS Obtained by the Slit Method and 2D-NPS for CT Images].

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.

Technical Note: A simple method for measuring the slice sensitivity profile of iteratively reconstructed CT images using a non-slanted edge plane.

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.

Improvement of the VEGF binding ability of DNA aptamers through in silico maturation and multimerization strategy.

Aptamers are mainly selected by in vitro selection using random nucleic acid libraries. These aptamers have often shown insufficient affinity for biomedical applications. We improved DNA aptamer binding affinity for vascular endothelial growth factor (VEGF) through in silico maturation (ISM) and aptamer multimerization. ISM is one of a number of evolutionary approaches and aptamer multimerization is one of several semi-rational strategies to improve function. We first reselected VEGF-binding aptamers using a partially randomized DNA library and identified two aptamers with higher binding ability than that of a known aptamer. We conducted ISM using the re-selected aptamers to optimize the key loop sequences created by a three-way junction structure. After five ISM rounds, we identified aptamer 2G19 [dissociation constant (Kd), 52 nM] as a local optimum of the defined search space. We characterized the aptamer and found that a specific stem-loop structure was involved in aptamer VEGF recognition. To further improve its affinity for VEGF, we multimerized 2G19 or its stem-loop structure. The designed SL5-trivalent aptamer (Kd, 0.37 nM) with three binding motifs significantly increased binding affinity, representing a 500-fold improvement from systematic evolution of ligands by exponential enrichment-selected aptamers.

Enzyme linking to DNA aptamers via a zinc finger as a bridge.

We propose a novel enzyme-labeling method for DNA aptamers using enzyme-fused zinc finger proteins. We achieved thrombin detection and vascular endothelial growth factor detection using zinc finger-fused firefly luciferase.

Vascular Endothelial Growth Factor (VEGF) Detection Using an Aptamer and PNA-Based Bound/Free Separation System.

We have developed a bound/free separation system using a vascular endothelial growth factor (VEGF) aptamer and a peptide nucleic acid (PNA) to detect VEGF. In this system, we designed capture PNA (CaPNA), which hybridizes with the aptamer in the absence of the target protein, but does not hybridize with the aptamer in the presence of the target protein due to steric hindrance and/or stabilization of the aptamer's structure. By removing the aptamers not bound to the target protein using CaPNA immobilized beads, we can detect the target protein by measuring signals labeled with the aptamer in the supernatant. In this study, we detected VEGF using CaPNA-immobilized beads without the time-consuming washing step. This simple and rapid system can detect 25 nM of VEGF in 15 min.

[Simulation study for artifacts on Gd-EOB-DTPA-enhanced liver dynamic MR imaging in arterial dominant phase].

We performed a simulation for artifacts on liver dynamic MR imaging with the contrast agent gadolinium-ethoxybenzyl (Gd-EOB)-DTPA. The signal enhancement of the image by the contrast agent in the arterial dominant phase was assumed, and the time-enhancement curve was numerically generated. The data in k-space was obtained by the Fourier transform of a liver image. By assuming the scan timing and duration in the time-enhancement curve, the data set of each phase-encoding step in k-space was increased in proportion to the corresponding intensity in the time-enhancement curve. We obtained the simulated image by the Fourier transform of the k-space data, and investigated artifacts in the image. Assuming the use of the centric k-space filling scheme, blurring in the image is found when the scan timing is delayed. When the scan is started in an early timing, we observe the effect of edge enhancement in the image. These artifacts of blurring and edge enhancement are decreased by shortening the scan duration. Assuming the use of the sequential k-space filling scheme, those artifacts are not prominent. The use of the sequential scheme would be effective for the purpose of avoiding the artifacts. It is known that the contrast enhancement would not be sufficient without optimal scan timing; in addition, artifacts should be noted. For basic study of the contrast enhancement and artifacts, our simulation technique based on the time-enhancement curve would be useful.