A pitfall of using the circular-edge technique with image averaging for spatial resolution measurement in iteratively reconstructed CT images.

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.

[Measurement of the Dose Rate Using Dosimeters in Interventional Radiology and Its Difficulty].

In equipment used for interventional radiology (IVR), automatic exposure control (AEC) is incorporated to obtain the X-ray output suitable for the treatment of targeted lesions. For the AEC, users select a region as the signal sensing region (measuring field, MF) in the flat panel detector; MFs with various sizes and shapes were pre-defined and prepared in the system. The aim of this study was to examine the change of measured dose rate with the selection of MFs, the type of dosimeters (the ionization chamber dosimeter and the semiconductor dosimeter), and the dosimeter placement relative to the direction of X-ray tube (from cathode to anode). The IVR equipment was Allura Xper FD20/10 (Philips Medical Systems), and six kinds of built-in MFs were used. It was found that dose rate measured by the ionization chamber dosimeter showed a variation of -2 mGy/min with the MFs and the ionization chamber dosimeter placement. The dose rate measured by the semiconductor dosimeter showed more variation than the ionization chamber dosimeter. The change of dose rate with the dosimeter placement would be caused by the MF overlapping the dosimeter which would affect the AEC (the X-ray output). Also, the change of dose rate with the dosimeter placement was considered to be related to the heel effect of the X-ray beam. When performing dose rate measurements, we should notice that the selection of MFs, the type of dosimeters, and the dosimeter placement would affect the measured values.

Diffuse brain abnormalities in myotonic dystrophy type 1 detected by 3.0 T proton magnetic resonance spectroscopy.

Patients with myotonic dystrophy type 1 (DM1) (n = 14) were compared with healthy controls (n = 13) using 3.0 T proton magnetic resonance spectroscopy ((1)H-MRS) to investigate brain pathophysiology. (1)H-MRS imaging revealed reduced N-acetylaspartate to creatine ratio (NAA/Cr) in multiple brain regions (average 24%), suggesting diffuse brain abnormalities among patients with DM1. Single-voxel (1)H-MRS among patients with DM1 showed (1) reduced NAA in both the frontal cortex (23%) and frontal white matter (31%) and unaltered myo-inositol, suggesting neuronal abnormalities without significant gliosis; and (2) elevated glutamine in the frontal cortex (36%) and reduced glutamate in the frontal white matter (20%) among patients with DM1, suggesting abnormalities in the glutamatergic system in the brain of patients with DM1. We consider that these results reflect brain abnormalities that cannot be detected by neuropathological assessment in patients with DM1.

[Pixel values of [15O]H2O2 PET images with OSEM algorithm depending on numbers of subset and iteration times: comparative assessment to FBP].

To investigate a potential application of ordered subsets expectation maximization (OSEM) algorithm for clinical [15O]H2O PET studies, region of interest (ROI) measurements were performed on both images with OSEM and filtered back projection (FBP). Forty OSEM images were reconstructed with variable combinations of numbers of the subset (1-40) and iteration times (2-12). PET scans were acquired using a PET/CT scanner (Discovery ST Elite, GE), and 3T-MRI images were obtained for fusion images. The mean values were measured on the frontal cortical regions in the middle cerebral artery distribution. Differences of the values between the OSEM and FBP were evaluated as %Error. Relationship between ROI mean values and the iteration times was investigated on the OSEM images. The smallest %Error 0.4% was measured in the combination of the subset number 10 and iteration times 8 [10, 8], and in that of [28, 2].The mean values were stable with iteration number 8 or more. OSEM image with [28, 2] was reconstructed in a shorter time (2.5 min) than that with [10, 8] (6 min). OSEM image with [28, 2] was superior to that with [10, 8] in the qualitative evaluation. The mean values on OSEM images with [28, 2] were comparable with those on FBP images with little artifacts and higher spatial resolution. OSEM with optimal parameter setting seemed applicable for both quantitative and qualitative [15O]H2O PET studies.

Accurate determination of CT point-spread-function with high precision.

The measurement of modulation transfer functions (MTFs) in computed tomography (CT) is often performed by scanning a point source phantom such as a thin wire or a microbead. In these methods the region of interest (ROI) is generally placed on the scanned image to crop the point source response. The aim of the present study was to examine the effect of ROI size on MTF measurement, and to optimize the ROI size. Using a 4 multidetector-row CT, MTFs were measured by the wire and bead methods for three types of reconstruction kernels designated as 'smooth', 'standard', and 'edge-enhancement' kernels. The size of a square ROI was changed from 30 to 50 pixels (approximately 2.9 to 4.9 mm). The accuracies of the MTFs were evaluated using the verification method. The MTFs measured by the wire and bead methods were dependent on ROI size, particularly in MTF measurement for the 'edge-enhancement' kernel. MTF accuracy evaluated by the verification method changed with ROI size, and we were able to determine the optimum ROI size for each method (wire/bead) and for each kernel. Using these optimal ROI sizes, the MTF obtained by the wire method was in strong agreement with the MTF obtained by the bead method in each kernel. Our data demonstrate that the difficulties in obtaining accurate MTFs for some kernels such as edge-enhancement can be overcome by incorporating the verification method into the wire and bead methods, allowing optimization of the ROI size to accurately determine the MTF.

[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.

Accuracy of lung nodule density on HRCT: analysis by PSF-based image simulation.

A computed tomography (CT) image simulation technique based on the point spread function (PSF) was applied to analyze the accuracy of CT-based clinical evaluations of lung nodule density. The PSF of the CT system was measured and used to perform the lung nodule image simulation. Then, the simulated image was resampled at intervals equal to the pixel size and the slice interval found in clinical high-resolution CT (HRCT) images. On those images, the nodule density was measured by placing a region of interest (ROI) commonly used for routine clinical practice, and comparing the measured value with the true value (a known density of object function used in the image simulation). It was quantitatively determined that the measured nodule density depended on the nodule diameter and the image reconstruction parameters (kernel and slice thickness). In addition, the measured density fluctuated, depending on the offset between the nodule center and the image voxel center. This fluctuation was reduced by decreasing the slice interval (i.e., with the use of overlapping reconstruction), leading to a stable density evaluation. Our proposed method of PSF-based image simulation accompanied with resampling enables a quantitative analysis of the accuracy of CT-based evaluations of lung nodule density. These results could potentially reveal clinical misreadings in diagnosis, and lead to more accurate and precise density evaluations. They would also be of value for determining the optimum scan and reconstruction parameters, such as image reconstruction kernels and slice thicknesses/intervals.

[An Easy-to-use Method for CT Image Simulation with Parameter Optimization Using a Water Phantom].

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.

[A Method for Evaluating the T2∗-weighting Effect in MRI].

PURPOSE: To propose a method for evaluating the T2*-weighting effect in MRI. METHODS: Multiple solutions with different concentrations of a superparamagnetic iron oxide contrast agent were made and their signal intensities on T2*-weighted images were measured. The relationship between iron concentration and signal intensity was determined, and we simulated an iron concentration map representing a simplified model of a brain microbleed and converted the pixel values in the map to signal intensity based on the determined relationship, generating a simulated T2*-weighted image. An 'S-value' parameter was defined to evaluate the low-intensity regions in the simulated image. S-values were obtained using T2*-weighted sequences acquired with different echo time (TE) values on three MRI scanners (Philips 1.5 T, GE 3.0 T, and Siemens 3.0 T). Another parameter (A-value) defined by the American Society for Testing and Materials (ASTM-F2119) for assessing artifacts was applied to evaluate the weighting effect in the T2*-weighted image of a laboratory-made susceptibility-effect phantom. RESULTS: With all three scanners, the S-values increased as the TE increased, indicating enhancement of the T2*-weighting effect. For every TE, the S-values obtained for the Philips scanner were the largest, followed by those for the GE and Siemens scanners. The results of this comparative evaluation were similar to those obtained using A-values. CONCLUSION: Comparisons with the established A-value parameter showed our proposed method for the quantitative evaluation of the T2*-weighting effect using S-values to be valid. The proposed method has the advantage that the S-values do not depend on a specific susceptibility-effect phantom.

[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.

[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.

Image filtering as an alternative to the application of a different reconstruction kernel in CT imaging: feasibility study in lung cancer screening.

PURPOSE: While the acquisition of projection data in a computed tomography (CT) scanner is generally cqrried out once, the projection data is often removed from the system, making further reconstruction with a different reconstruction filter impossible. The reconstruction kernel is one of the most important parameters. To have access to all the reconstructions, either prior reconstructions with multiple kernels must be performed or the projection data must be stored. Each of these requirements would increase the burden on data archiving. This study aimed to design an effective method to achieve similar image quality using an image filtering technique in the image space, instead of a reconstruction filter in the projection space for CT imaging. The authors evaluated the clinical feasibility of the proposed method in lung cancer screening. METHODS: The proposed technique is essentially the same as common image filtering, which performs processing in the spatial-frequency domain with a filter function. However, the filter function was determined based on the quantitative analysis of the point spread functions (PSFs) measured in the system. The modulation transfer functions (MTFs) were derived from the PSFs, and the ratio of the MTFs was used as the filter function. Therefore, using an image reconstructed with a kernel, an image reconstructed with a different kernel was obtained by filtering, which used the ratio of the MTFs obtained for the two kernels. The performance of the method was evaluated by using routine clinical images obtained from CT screening for lung cancer in five subjects. RESULTS: Filtered images for all combinations of three types of reconstruction kernels ("smooth," "standard," and "sharp" kernels) showed good agreement with original reconstructed images regarded as the gold standard. On the filtered images, abnormal shadows suspected as being lung cancers were identical to those on the reconstructed images. The standard deviations (SDs) for the difference between filtered images and reconstructed images ranged from 1.9 to 23.5 Hounsfield units for all kernel combinations; these SDs were much smaller than the noise SDs in the reconstructed images. CONCLUSIONS: The proposed method has good performance and is clinically feasible in lung cancer screening. This method can be applied to images reconstructed on any scanner by measuring the PSFs in each system.

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.

[A Simple Method for Computationally Generating Metal Artifacts in CT Images for Treatment Planning: A Pilot Phantom Study].

PURPOSE: In treatment planning for radiation therapy, the use of computed tomography (CT) images including metal artifacts causes a reduction in the dose calculation accuracy. In clinical practice, the artifacts are manually contoured and assigned an appropriate fixed CT number. To validate the procedure, images taken before and after metal insertion into a patient are required, which may be impractical. We propose a simple method for computationally generating metal artifacts in clinical images. METHODS: In the proposed method, a clinical image free of metal artifacts is used. To simulate metal inside a patient, CT numbers of a region in the image are replaced with a fixed extremely high value. A sinogram is created by the forward projection of the image. Data values of the sinogram in the metal region are converted into smaller values. From the sinogram, an image including artifacts is reconstructed with the filtered back projection. RESULTS: The simulated artifacts consisted of dark and bright bands and were observed to be similar to the actual metal artifacts. CT numbers in multiple small regions of interest in the image obtained by the proposed method showed a good agreement with those in the actual image. CONCLUSION: The proposed method was demonstrated to generate the metal artifacts additionally on the clinical images. The method would be potentially applicable to a validation study for the clinical procedure of manually contouring and assigning CT numbers to metal artifacts.

[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.

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.

GlyCEST: Magnetic Resonance Imaging of Glycine-Distribution in the Normal Murine Brain and Alterations in 5xFAD Mice.

The glycine level in the brain is known to be altered in neuropsychiatric disorders, such as schizophrenia and Alzheimer's disease (AD). Several studies have reported the in vivo measurement of glycine concentrations in the brain using proton magnetic resonance spectroscopy (1H-MRS), but 1H-MRS is not capable of imaging the distribution of glycine concentration with high spatial resolution. Chemical exchange saturation transfer magnetic resonance imaging (CEST-MRI) is a new technology that can detect specific molecules, including amino acids, in tissues. To validate the measurements of glycine concentrations in living tissues using CEST from glycine to water (GlyCEST), we extracted the brain tissues from mice and performed biochemical tests. In wild-type C57BL/6 mice, GlyCEST effects were found to be higher in the thalamus than in the cerebral cortex (P < 0.0001, paired t-test), and this result was in good agreement with the biochemical results. In 5xFAD mice, an animal model of AD, GlyCEST measurements demonstrated that glycine concentrations in the cerebral cortex (P < 0.05, unpaired t-test) and thalamus (P < 0.0001, unpaired t-test), but not in the hippocampus, were decreased compared to those in wild-type mice. These findings suggest that we have successfully applied the CEST-MRI technique to map the distribution of glycine concentrations in the murine brain. The present method also captured the changes in cerebral glycine concentrations in mice with AD. Imaging the distribution of glycine concentrations in the brain can be useful in investigating and elucidating the pathological mechanisms of neuropsychiatric disorders.

[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.

Longitudinal GluCEST MRI Changes and Cerebral Blood Flow in 5xFAD Mice.

Many of the focal neurological symptoms associated with Alzheimer's disease (AD) are due to synaptic loss. Glutamate chemical exchange saturation transfer (GluCEST) magnetic resonance imaging (MRI) is a candidate method to assess synaptic dysfunction. We assessed chronological changes in GluCEST in a 5xFAD mouse model of AD, comparing Glucest effects and regional cerebral blood flow (CBF). GluCEST effects and CBF in 5xFAD mice aged 1-15 months and their littermates (WT) were measured. Neurite orientation dispersion and density imaging (NODDI) MRI reflecting dendritic/axonal density was also measured and compared with GluCEST in 7-month-old mice. While regional CBF's decrease began at 7 months, GluCEST-reduction effects preceded hypoperfusion of the temporal cortex and hippocampus. While longitudinal 5xFAD mouse measurements revealed a correlation between the regional GluCEST effects and CBF, a generalized linear mixed model revealed statistically different correlations in cortical and basal brain regions. Further, NODDI-derived neurite density correlated with GluCEST effects in the parietal cortex, but not in the hippocampus, thereby revealing regional differences in pathophysiological mechanisms. Finally, GluCEST's effects correlated with regional synaptophysin. These results demonstrate that GluCEST can reflect subtle synaptic changes and may be a potential imaging method for AD diagnosis as well as serve as a biomarker of AD progression.