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Diffusion tensor imaging (DTI) has been established its usefulness in evaluating normal-appearing white matter (NAWM) and other lesions that are difficult to evaluate with routine clinical MRI in the evaluation of the brain and spinal cord lesions in multiple sclerosis (MS), a demyelinating disease. With the recent advances in the software and hardware of MRI systems, increasingly complex and sophisticated MRI and analysis methods, such as q-space imaging, diffusional kurtosis imaging, neurite orientation dispersion and density imaging, white matter tract integrity, and multiple diffusion encoding, referred to as advanced diffusion MRI, have been proposed. These are capable of capturing in vivo microstructural changes in the brain and spinal cord in normal and pathological states in greater detail than DTI.This paper reviews the current status of recent advanced diffusion MRI for assessing MS in vivo as part of an issue celebrating two decades of magnetic resonance in medical sciences (MRMS), an official journal of the Japanese Society of Magnetic Resonance in Medicine.

The volumes of intracranial tissues of 40 healthy volunteers acquired from 0.3- and 3-T scanners were compared using intraclass correlation coefficients, correlation analyses, and Bland-Altman analyses. We found high intraclass correlation coefficients, high Pearson's correlation coefficients, and low percentage biases in all tissues and most of the brain regions, although small differences were observed in some areas. These findings may support the validity of brain volumetry with low-field magnetic resonance imaging.

Low-field magnetic resonance imaging (MRI) systems have seen a renaissance recently due to improvements in technology (both hardware and software). Originally, the performance of low-field MRI systems was rated lower than their actual clinical usefulness, and they were viewed as low-cost but poorly performing systems. However, various applications similar to high-field MRI systems (1.5 T and 3 T) have gradually become possible, culminating with high-performance low-field MRI systems and their adaptations now being proposed that have unique advantages over high-field MRI systems in various aspects. This review article describes the physical characteristics of low-field MRI systems and presents both their advantages and disadvantages for clinical use (past to present), along with their cutting-edge clinical applications.

Purpose The aim of this study was to investigate whether the detectability of brain volume change in voxel-based morphometry (VBM) with gray matter images is affected by mild white matter lesions (MWLs). Materials and methods Three-dimensional T1-weighted images (3D-T1WIs) of 11 healthy subjects were obtained using a 3T MR scanner. We initially created 3D-T1WIs with focal cortical atrophy simulated cortical atrophy in left amygdala (type A) and the left medial frontal lobe (type B) from control 3D-T1WIs. Next, the following three types of MWL images were created: type A + 1L and type B + 1L images, only one white matter lesion; type A + 4L and type B + 4L images, four white matter lesions at distant positions; and type A + 4L* and type B + 4L* images, four white matter lesions at clustered positions. Comparisons between the control group and the other groups were performed with VBM using segmented gray matter images. Results The gray matter volume was significantly lower in the type A group than in the control group, and similar results were observed in the type A + 1L, type A + 4L, and type A + 4L* groups. Additionally, the gray matter volume was significantly lower in the type B group than in the control group, and similar results were observed in the type B + 1L, type B + 4L, and type B + 4L* groups, but the cluster size in type B + 4L* was smaller than that in type B. Conclusion Our study showed that the detectability of brain volume change in VBM with gray matter images was not decreased by MWLs as lacunar infarctions. Therefore, we think that group comparisons with VBM should be analyzed by groups including and excluding subjects with MWLs, respectively.

The aim of this study was to evaluate the effect of changing the contrast of an analyzed image on the accuracy of intracranial volume (ICV) extraction using the Brain Extraction Tool (BET2) in healthy adults and patients with Sturge-Weber syndrome (SWS), including infants. Twelve SWS patients, including infants, and 12 healthy participants were imaged on a 3.0-T magnetic resonance imaging (MRI) machine. All individuals underwent quantification of relaxation times and proton density using multi-echo acquisition of saturation recovery with turbo-spin-echo readout (QRAPMASTER). Based on the QRAPMASTER data, we created images with seven contrasts (T1-WI, T2-WI, PD-WI, T2 short-tau inversion recovery [STIR], proton density [PD] STIR, T2STIR + PDSTIR, and T1-WI + T2-WI + PD-WI) by post-processing with SyMRI software. ICVs extracted with BET2 from the FMRIB (Functional Magnetic Resonance Imaging of the Brain) Software Library with each of the seven image contrasts were compared with manually extracted ICVs, which is the gold standard reviewed by a board-certificated neuroradiologist. Manual extraction was performed on T1-WI and T2STIR. Statistical analyses were performed with Jaccard similarity coefficients (J). The highest J score was found in T1-WI + T2-WI + PD-WI in all participants (0.8451); T1-WI in healthy participants (0.8984); T2STIR in participants with SWS (0.8325). Our findings suggest that T1-WI and T2STIR should be used in ICV extraction performed using BET2 on healthy participants and infants, respectively. Additionally, if the analyzed individuals include both healthy participants and infants, T1-WI + T2-WI + PD-WI should be used.

Background Voxel-based morphometry (VBM) using magnetic resonance imaging (MR) has been used to estimate cortical atrophy associated with various diseases. However, there are mis-segmentations of segmented gray matter image in VBM. Purpose To study a twofold evaluation of single- and multi-channel segmentation using synthetic MR images: (1) mis-segmentation of segmented gray matter images in transverse and cavernous sinuses; and (2) accuracy and repeatability of segmented gray matter images. Material and Methods A total of 13 healthy individuals were scanned with 3D quantification using an interleaved Look–Locker acquisition sequence with a T2 preparation pulse (3D-QALAS) sequence on a 1.5-T scanner. Three of the 13 healthy participants were scanned five consecutive times for evaluation of repeatability. We used SyMRI software to create images with three contrasts: T1-weighted (T1W), T2-weighted (T2W), and proton density-weighted (PDW) images. Manual regions of interest (ROI) on T1W imaging were individually set as the gold standard in the transverse sinus, cavernous sinus, and putamen. Single-channel (T1W) and multi-channel (T1W + T2W, T1W + PDW, and T1W + T2W + PDW imaging) segmentations were performed with statistical parametric mapping 12 software. Results We found that mis-segmentations in both the transverse and cavernous sinuses were large in single-channel segmentation compared with multi-channel segmentations. Furthermore, the accuracy of segmented gray matter images in the putamen was high in both multi-channel T1W + PDW and T1W + T2W + PDW segmentations compared with other segmentations. Finally, the highest repeatability of left putamen volumetry was found with multi-channel segmentation T1WI + PDWI. Conclusion Multi-channel segmentation with T1WI + PDWI provides good results for VBM compared with single-channel and other multi-channel segmentations.

Background Four-dimensional cone-beam computed tomography (4D-CBCT) can visualize moving tumors, thus adaptive radiation therapy (ART) could be improved if 4D-CBCT were used. However, 4D-CBCT images suffer from severe imaging artifacts. The aim of this study is to investigate the use of synthetic 4D-CBCT (sCT) images created by a cycle generative adversarial network (CycleGAN) for ART for lung cancer. Methods Unpaired thoracic 4D-CBCT images and four-dimensional multislice computed tomography (4D-MSCT) images of 20 lung-cancer patients were used for training. High-quality sCT lung images generated by the CycleGAN model were tested on another 10 cases. The mean and mean absolute errors were calculated to assess changes in the computed tomography number. The structural similarity index measure (SSIM) and peak signal-to-noise ratio (PSNR) were used to compare the sCT and original 4D-CBCT images. Moreover, a volumetric modulation arc therapy plan with a dose of 48 Gy in four fractions was recalculated using the sCT images and compared with ideal dose distributions observed in 4D-MSCT images. Results The generated sCT images had fewer artifacts, and lung tumor regions were clearly observed in the sCT images. The mean and mean absolute errors were near 0 Hounsfield units in all organ regions. The SSIM and PSNR results were significantly improved in the sCT images by approximately 51% and 18%, respectively. Moreover, the results of gamma analysis were significantly improved; the pass rate reached over 90% in the doses recalculated using the sCT images. Moreover, each organ dose index of the sCT images agreed well with those of the 4D-MSCT images and were within approximately 5%. Conclusions The proposed CycleGAN enhances the quality of 4D-CBCT images, making them comparable to 4D-MSCT images. Thus, clinical implementation of sCT-based ART for lung cancer is feasible.

Background Atlas-based volumetry using three-dimensional T1-weighted (3D-T1W) magnetic resonance imaging (MRI) has been used previously to evaluate the volumes of intracranial tissues. Purpose To evaluate the detectability of volume difference and accuracy for volumetry using smoothed data with an atlas-based method. Material and Methods Twenty healthy individuals and 24 patients with idiopathic normal-pressure hydrocephalus (iNPH) underwent 3-T MRI, and sagittal 3D-T1W images were obtained in all participants. Signal values (as tissue probability) of voxels in five segmented data types (gray matter, white matter, cerebrospinal fluid [CSF], skull, soft tissue) derived from the 3D-T1W images with SPM 12 software were assigned simulated 3D-T1W signal intensities to each tissue image. The assigned data were termed “reference data.” We created a reference 3 D-T1W image that included the reference data of all five tissue types. Standard volumes were measured for the reference CSF data with region of interest of lateral ventricle in native space, and measured volumes were obtained for non-smoothed and smoothed-modulated data. Detectability was evaluated between measured volumes in the healthy control and iNPH groups. Accuracy was evaluated as the difference between the mean measured and standard volumes. Results In group comparison of measured volumes between the healthy control and iNPH groups, the lowest P value was for smoothed-modulated CSF data. In both groups, the largest difference from the standard volume was found for the mean of the measured volumes for smoothed-modulated CSF data. Conclusion Our study shows that using smoothed data can improve detectability in group comparison. However, using smoothed data reduces the accuracy of volumetry.

To minimize radiation risk, dose reduction is important in the diagnostic and therapeutic applications of computed tomography (CT). However, image noise degrades image quality owing to the reduced X-ray dose and a possible unacceptably reduced diagnostic performance. Deep learning approaches with convolutional neural networks (CNNs) have been proposed for natural image denoising; however, these approaches might introduce image blurring or loss of original gradients. The aim of this study was to compare the dose-dependent properties of a CNN-based denoising method for low-dose CT with those of other noise-reduction methods on unique CT noise-simulation images. To simulate a low-dose CT image, a Poisson noise distribution was introduced to normal-dose images while convoluting the CT unit-specific modulation transfer function. An abdominal CT of 100 images obtained from a public database was adopted, and simulated dose-reduction images were created from the original dose at equal 10-step dose-reduction intervals with a final dose of 1/100. These images were denoised using the denoising network structure of CNN (DnCNN) as the general CNN model and for transfer learning. To evaluate the image quality, image similarities determined by the structural similarity index (SSIM) and peak signal-to-noise ratio (PSNR) were calculated for the denoised images. Significantly better denoising, in terms of SSIM and PSNR, was achieved by the DnCNN than by other image denoising methods, especially at the ultra-low-dose levels used to generate the 10% and 5% dose-equivalent images. Moreover, the developed CNN model can eliminate noise and maintain image sharpness at these dose levels and improve SSIM by approximately 10% from that of the original method. In contrast, under small dose-reduction conditions, this model also led to excessive smoothing of the images. In quantitative evaluations, the CNN denoising method improved the low-dose CT and prevented over-smoothing by tailoring the CNN model.

Background In many brain volumetry reports, data from both controls and patients have been used to create the diffeomorphic anatomical registration through exponentiated lie algebra (DARTEL) templates for spatial normalization, whereas some studies used data from only the control group. Purpose To investigate the association between lesion detection and the processes used in creating DARTEL template in group comparisons with voxel-based morphometry. Material and Methods Three-dimensional T1-weighted MRI data of 31 healthy controls, 31 patients with Alzheimer’s disease, and 31 patients with idiopathic normal pressure hydrocephalus were obtained using a 3-T magnetic resonance scanner. Segmented gray matter images were then deformed into three types of DARTEL templates using the Statistical Parametric Mapping 12 software. Results Processes 1 (DARTEL template with all individuals) and 2 (DARTEL template with healthy controls) showed significant lesions, as observed in previous reports, whereas process 3 (DARTEL template with individuals divided into two groups) showed significantly larger lesions than those noted with processes 1 and 2. Conclusion The present study demonstrated that lesion detection depends on the processes used in the creation of the DARTEL template.

Background Previous quantitative synthetic MRI of the brain has been solely performed in 2D. Purpose To evaluate the feasibility of the recently developed sequence 3D-QALAS for brain cortical thickness and volumetric analysis. Study type Reproducibility/repeatability study. Subjects Twenty-one healthy volunteers (35.6 ± 13.8 years). Field strength/sequence 3D T1 -weighted fast spoiled gradient recalled echo (FSPGR) sequence was performed once, and 3D-QALAS sequence was performed twice with a 3T scanner. Assessment FreeSurfer and FIRST were used to measure cortical thickness and volume of subcortical structures, respectively. Agreement with FSPGR and scan-rescan repeatability were evaluated for 3D-QALAS. Statistical tests Percent relative difference and intraclass correlation coefficient (ICC) were used to assess reproducibility and scan-rescan repeatability of the 3D-QALAS sequence-derived measurements. Results Percent relative difference compared with FSPGR in cortical thickness of the whole cortex was 3.1%, and 89% of the regional areas showed less than 10% relative difference in cortical thickness. The mean ICC across all regions was 0.65, and 74% of the structures showed substantial to almost perfect agreement. For volumes of subcortical structures, the median percent relative differences were lower than 10% across all subcortical structures, except for the accumbens area, and all structures showed ICCs of substantial to almost perfect agreement. For the scan-rescan test, percent relative difference in cortical thickness of the whole cortex was 2.3%, and 97% of the regional areas showed less than 10% relative difference in cortical thickness. The mean ICC across all regions was 0.73, and 80% showed substantial to almost perfect agreement. For volumes of subcortical structures, relative differences were less than 10% across all subcortical structures except for the accumbens area, and all structures showed ICCs of substantial to almost perfect agreement. Data conclusion 3D-QALAS could be reliably used for measuring cortical thickness and subcortical volumes in most brain regions. Level of evidence 3 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2019;50:1834-1842.

Introduction Recently, three-dimensional (3D) quantitative synthetic magnetic resonance imaging (MRI), which quantifies tissue properties and creates multiple contrast-weighted images, has been enabled by 3D-quantification using an interleaved Look-Locker acquisition sequence with a T2 preparation pulse (3D-QALAS). However, the relatively long scan time has hindered its introduction into clinical practice. A hybrid of compressed sensing and parallel imaging (Compressed sensing-sensitivity encoding: CS-SENSE) can accelerate 3D-QALAS; however, whether CS-SENSE affects the quantitative values acquired by 3D-QALAS remains unexplored. Therefore, this study aimed to examine the effects of reduction factors of CS-SENSE (RCSS) on the quantitative values derived from 3D-QALAS, by assessing the signal-to-noise ratio (SNR) of the quantitative maps, as well as accuracy (linearity and bias) and repeatability of measured quantitative values. Methods In this study, the ISMRM/NIST standardized phantom was scanned on a 1.5-T MRI scanner with 3D-QALAS using RCSS in the range between 1 and 3, with intervals of 0.2, and between 3 and 10 with intervals of 0.5. The T1, T2, and proton density (PD) values were calculated from the imaging data. For each quantitative value, the SNR, the coefficient of determination (R2) of a linear regression model, the error rate, and the within-subject coefficient of variation (wCV) were calculated for each RCSS and compared. Results Within the clinically-relevant dynamic range of the brain of T1 and T2 (T1: 200–1400 ms; T2; 50–400 ms) and PD value of 15–100% calculated from 3D-QALAS, the effects of RCSS on quantitative values was small between 1 and 2.8, with SNR ≧ 10, R2 ≧ 0.9, error rate ≦ 10%, and wCV ≦ 10%, except for T2 values of 186.1 and 258.4 ms. Conclusions CS-SENSE enabled the reduction of the scan time of 3D-QALAS by 63.5% (RCSS = 2.8) while maintaining the SNR of quantitative maps and accuracy and repeatability of the quantitative values.

Brain extraction represents an important step in numerous neuroimaging analyses. The brain extraction tool (BET)2 is a widely used deformable model-based approach for extraction of intracranial volume (ICV). The aim of this study is to estimate the ICV extraction accuracy using synthetic MR(SyMRI) method and BET2 in healthy adult participants and patients with Sturge-Weber Syndrome (SWS), including infants. 'Quantification of relaxation times and proton density by multi-echo acquisition of saturation recovery with turbo-spin-echo readout' (QRAPMASTER) with a 3.0 T magnetic resonance image (MRI) system was used for data acquisition. Statistical evaluations were performed with linear regression analysis and the Jaccard similarity coefficient (J). ICV extraction accuracy with synthetic MR method is found to be higher than BET2, for both aged healthy participants and SWS.

Purpose/Objective(s) To achieve high-precision stereotactic body radiotherapy, visualization of moving tumor/target estimation is performed using 4-dimensional cone-beam computed tomography (4D-CBCT) before each treatment fraction. Currently, respiration-correlated 4D-CBCT is achieved with subgroups of cone-beam projections into different respiratory phases. However, insufficient projections inside each phase bin cause severe imaging artifacts. Convolutional neural networks are widely used to correct CBCT specific artifacts. Previous research has shown that mean absolute error in Hounsfield units improved up to 93% over the original CBCT image. However, slight voxel-wise misalignment between the training images may lead to blurred synthesis, and hence, due to respiratory motion, it is impossible to perform 4D-CBCT imaging without organ misalignment with the 4D-computed tomography (4D-CT). Cycle-generative adversarial network (cycle-GAN) framework enforces an inverse transformation and achieves high accurate consistency even in mapping nonlinear domains. The aim of this study was to improve 4D-CBCT image quality using cycle-GAN and evaluate its use in adaptive radiation therapy (ART) planning for lung cancer. Materials/Methods Unpaired 4D-CT and 4D-CBCT taken from publicly available datasets in 5 patients who underwent lung cancer treatment were used for training using cycle-GAN model. Then, Synthesis of 4D-CBCT (Syn-CBCT) images with improved quality conferred by the cycle-GAN model was tested in another 5 cases in the lung region. To assess pixel value deviation and image similarity, mean absolute errors (MAE) and structural similarity index measure (SSIM) were calculated for Syn-CBCT and paired 4D-CT images and compared with those of original 4D-CBCT images. Moreover, volumetric modulation arc therapy plans with a dose of 48 Gy in 4 fractions were recalculated based on Syn-CBCT images, and 3-dimensional (3D)-gamma (3%/3 mm) analysis was conducted to compare the former with ideal dose distributions observed in 4D-CT images. Results 4D-CBCT imaging quality was improved and lung tumor regions were clearly observed after noise and artifact removal by applying the trained cycle-GAN model. MAEs were improved by 21% and SSIMs by 46% for whole images. Hence, pixel value and structural visualization in Syn-CBCT images could achieve near-ideal quality of 4D-CT images. Large structures were generally consistent with those in paired 4D-CT images, whereas fine structures (pulmonary vessels and fine bones) were not completely duplicated. However, a mean 3D gamma pass rate of > 95% was achieved in all cases. Conclusion The proposed cycle-GAN method enhances 4D-CBCT image quality, making it comparable to that of 4D-CT images. Thus, clinical implementation of Syn-CBCT-based ART is feasible.

The aims of this study were to demonstrate an association between changes in the signal intensity of the putamen on three-dimensional T1-weighted magnetic resonance images (3D-T1WI) and mis-segmentation, using the voxel-based morphometry (VBM) 8 toolbox. The sagittal 3D-T1WIs of 22 healthy volunteers were obtained for VBM analysis using the 1.5-T MR scanner. We prepared five levels of 3D-T1WI signal intensity (baseline, same level, background level, low level, and high level) in regions of interest containing the putamen. Groups of smoothed, spatially normalized tissue images were compared to the baseline group using a paired t test. The baseline was compared to the other four levels. In all comparisons, significant volume changes were observed around and outside the area that included the signal intensity change. The present study demonstrated an association between a change in the signal intensity of the putamen on 3D-T1WI and changed volume in segmented tissue images.

To investigate methods to reduce metal artifacts during digital tomosynthesis for arthroplasty, we evaluated five algorithms with and without metal artifact reduction (MAR)-processing tested under different radiation doses (0.54, 0.47, and 0.33mSv): adaptive steepest descent projection onto convex sets (ASD-POCS), simultaneous algebraic reconstruction technique total variation (SART-TV), filtered back projection (FBP), maximum likelihood expectation maximization (MLEM), and SART. The algorithms were assessed by determining the artifact index (AI) and artifact spread function (ASF) on a prosthesis phantom. The AI data were statistically analyzed by two-way analysis of variance. Without MAR-processing, the greatest degree of effectiveness of the MLEM algorithm for reducing prosthetic phantom-related metal artifacts was achieved by quantification using the AI (MLEM vs. ASD-POCS, SART-TV, SART, and FBP; all P

Background The relationship between hippocampal and amygdaloid volumes and depression in patients with Parkinson’s disease (PD) is a controversial issue. Purpose To investigate the correlation between the 15-item shortened version of the Geriatric Depression Scale (GDS-15) and gray matter volume in PD. Material and Methods In the present study, 46 participants with PD were scanned with 3 T magnetic resonance imaging (MRI) to obtain three-dimensional (3D) T1-weighted (T1W) images. Neurologists specializing in movement disorders performed clinical evaluations of the participants (e.g. GDS-15, Mini-Mental State Examination, PD duration, age, sex). Statistical Parametric Mapping 8 software was used for image gray matter segmentation and for a correlation analysis between gray matter volume and GDS-15 score. Results The results showed a significant negative correlation between GDS-15 score and left hippocampal volume, and between GDS-15 score and right parahippocampal gyrus volume. No significant positive correlations were found in the whole brain. Conclusion The current results provide new evidence regarding the relationship between depression in PD and hippocampal volume.

The aim of this study was to evaluate changes in measured volumes using Statistical Parametric Mapping (SPM) 12 caused by contrast changes in magnetic resonance (MR) image. Twenty-one healthy subjects participated in the study. From all subjects, 3D T1-weighted images (T1WIs) were obtained using a 3T scanner. In the first step of creating reference volume data, we used SPM12 to binarize all the segmented data. In the second step, we assigned simulated 3D-T1WI signal intensities to each tissue image and used the following values. The last step was integration of each tissue image to generate 3D-T1WI simulated reference volume data for each participant. To create the reference 3D-T1WIs with various contrasts from the reference volume data, we varied the signal intensity of gray matter from 900 to 600, 700, 1100, 1300, and 1400. The reference 3D-T2WI was acquired using the method used for 3D-T1WIs. Then, six 3D-T1WIs were processed using intrasubject bias-correction processing with SPM12, resulting in six new 3D-T1WIs of nonuniform signal intensities. Thirteen volume data sets were segmented into native-space tissue probability data using SPM12. Examination of the 3D data without nonuniform signal intensity showed that significant differences in measured volumes were not observed on repeated analysis of variance, but examination of the 3D data with nonuniform signal intensity did show significant differences in measured volumes in gray matter and CSF but not in white matter. Measured volumes using segmented tissue probability data obtained using SPM12 were not influenced by the contrasts of analyzed images.

The relationship between long-term intensive training and brain plasticity in gymnasts has recently been reported. However, the relationship between abilities in different gymnastic events and brain structural changes has not been explored. This study aimed to evaluate the correlation between world-class gymnasts (WCGs)’ specific abilities in different gymnastics events and their gray matter (GM) volume. Ten right-handed Japanese male WCGs and 10 right-handed gender- and age-matched controls with no history of gymnastic training participated in this study. Whole brain three-dimensional T1-weighted images (magnetization-prepared rapid gradient-echo sequence) with 0.90 mm3 voxels were obtained using a 3 T-MRI scanner from each subject. Volume-based morphometry (VolBM) was used to compare GM volume differences between WCGs and controls. We then explored the correlation between specific gymnastic abilities using different gymnastic apparatuses, and GM volume. Significantly higher GM volumes (false discovery rate-corrected p

Background Diffusion tensor imaging (DTI) is a magnetic resonance imaging (MRI) technique that reflects the Brownian motion of water molecules constrained within brain tissue. Fractional anisotropy (FA) is one of the most commonly measured DTI parameters, and can be applied to quantitative analysis of white matter as tract-based spatial statistics (TBSS) and voxel-wise analysis. Purpose To show an association between metallic implants and the results of statistical analysis (voxel-wise group comparison and TBSS) for fractional anisotropy (FA) mapping, in DTI of healthy adults. Material and Methods Sixteen healthy volunteers were scanned with 3-Tesla MRI. A magnetic keeper type of dental implant was used as the metallic implant. DTI was acquired three times in each participant: (i) without a magnetic keeper (FAnon1); (ii) with a magnetic keeper (FAimp); and (iii) without a magnetic keeper (FAnon2) as reproducibility of FAnon1. Group comparisons with paired t-test were performed as FAnon1 vs. FAnon2, and as FAnon1 vs. FAimp. Results Regions of significantly reduced and increased local FA values were revealed by voxel-wise group comparison analysis (a P value of less than 0.05, corrected with family-wise error), but not by TBSS. Conclusion Metallic implants existing outside the field of view produce artifacts that affect the statistical analysis (voxel-wise group comparisons) for FA mapping. When statistical analysis for FA mapping is conducted by researchers, it is important to pay attention to any dental implants present in the mouths of the participants.

PURPOSE An understanding of the repeatability of measured results is important for both the atlas-based and voxel-based morphometry (VBM) methods of magnetic resonance (MR) brain volumetry. However, many recent studies that have investigated the repeatability of brain volume measurements have been performed using static magnetic fields of 1-4 tesla, and no study has used a low-strength static magnetic field. The aim of this study was to investigate the repeatability of measured volumes using the atlas-based method and a low-strength static magnetic field (0.4 tesla). MATERIALS AND METHODS Ten healthy volunteers participated in this study. Using a 0.4 tesla magnetic resonance imaging (MRI) scanner and a quadrature head coil, three-dimensional T1-weighted images (3D-T1WIs) were obtained from each subject, twice on the same day. VBM8 software was used to construct segmented normalized images [gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) images]. The regions-of-interest (ROIs) of GM, WM, CSF, hippocampus (HC), orbital gyrus (OG), and cerebellum posterior lobe (CPL) were generated using WFU PickAtlas. The percentage change was defined as[100 × (measured volume with first segmented image - mean volume in each subject)/(mean volume in each subject)]The average percentage change was calculated as the percentage change in the 6 ROIs of the 10 subjects. RESULTS The mean of the average percentage changes for each ROI was as follows: GM, 0.556%; WM, 0.324%; CSF, 0.573%; HC, 0.645%; OG, 1.74%; and CPL, 0.471%. The average percentage change was higher for the orbital gyrus than for the other ROIs. CONCLUSION We consider that repeatability of the atlas-based method is similar between 0.4 and 1.5 tesla MR scanners. To our knowledge, this is the first report to show that the level of repeatability with a 0.4 tesla MR scanner is adequate for the estimation of brain volume change by the atlas-based method.

Background Previous quantitative synthetic MRI of the brain has been solely performed in 2D. Purpose To evaluate the feasibility of the recently developed sequence 3D‐QALAS for brain cortical thickness and volumetric analysis. Study Type Reproducibility/repeatability study. Subjects Twenty‐one healthy volunteers (35.6 ± 13.8 years). Field Strength/Sequence 3D T1‐weighted fast spoiled gradient recalled echo (FSPGR) sequence was performed once, and 3D‐QALAS sequence was performed twice with a 3T scanner. Assessment FreeSurfer and FIRST were used to measure cortical thickness and volume of subcortical structures, respectively. Agreement with FSPGR and scan–rescan repeatability were evaluated for 3D‐QALAS. Statistical Tests Percent relative difference and intraclass correlation coefficient (ICC) were used to assess reproducibility and scan–rescan repeatability of the 3D‐QALAS sequence‐derived measurements. Results Percent relative difference compared with FSPGR in cortical thickness of the whole cortex was 3.1%, and 89% of the regional areas showed less than 10% relative difference in cortical thickness. The mean ICC across all regions was 0.65, and 74% of the structures showed substantial to almost perfect agreement. For volumes of subcortical structures, the median percent relative differences were lower than 10% across all subcortical structures, except for the accumbens area, and all structures showed ICCs of substantial to almost perfect agreement. For the scan–rescan test, percent relative difference in cortical thickness of the whole cortex was 2.3%, and 97% of the regional areas showed less than 10% relative difference in cortical thickness. The mean ICC across all regions was 0.73, and 80% showed substantial to almost perfect agreement. For volumes of subcortical structures, relative differences were less than 10% across all subcortical structures except for the accumbens area, and all structures showed ICCs of substantial to almost perfect agreement. Data Conclusion 3D‐QALAS could be reliably used for measuring cortical thickness and subcortical volumes in most brain regions. Level of Evidence: 3 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2019;50:1834–1842.

In this study, a novel dual-energy (DE) material decomposition reconstruction algorithm (DEMDRA) was developed using projection data with the aim of reducing metal artifacts during digital tomosynthesis (DT) for implants. Using the three-material decomposition method and decomposition projection data specific for each material, a novel DEMDRA was implemented to reduce metal artifacts via weighted hybrid reconstructed images [maximum likelihood expectation maximization (MLEM) and shift-and-add (SAA)]. Pulsed X-ray exposures with rapid switching between low and high tube potential kVp were used for DE-DT imaging, and the images were compared using conventional filtered back projection (FBP), MLEM, the simultaneous algebraic reconstruction technique total variation (SART-TV), virtual monochromatic processing, and metal artifact reduction (MAR)-processing algorithms. The reductions in metal artifacts were compared using an artifact index (AI), Gumbel distribution of the largest variations, and the artifact spread functions (ASFs) for prosthesis phantom. The novel DEMDRA yielded an adequately effective overall performance in terms of the AI, and the resulting images yielded good results independently of the type of metal used in the prosthetic phantom, as well as good noise artifact removal, particularly at greater distances from metal objects. Furthermore, the DEMDRA represented the minimum in the model of largest variations. Regarding the ASF analysis, the novel DEMDRA yielded superior metal artifact reduction when compared with conventional reconstruction algorithms with and without MAR processing. Finally, the DEMDRA was particularly useful for reducing high-frequency artifacts.

Summary Objectives: To investigate whether reproducibility of gray matter volumetry is influenced by parameter settings for VBM 8 using Diffeomorphic Anatomical Registration Through Exponentiated Lie Algebra (DARTEL) with region-of-interest (ROI) analyses. Methods: We prepared three-dimensional T1-weighted magnetic resonance images (3D-T1WIs) of 21 healthy subjects. All subjects were imaged with each of five MRI systems. Voxel-based morphometry 8 (VBM 8) and WFU PickAtlas software were used for gray matter volumetry. The bilateral ROI labels used were those provided as default settings with the software: Frontal Lobe, Hippocampus, Occipital Lobe, Orbital Gyrus, Parietal Lobe, Putamen, and Temporal Lobe. All 3D-T1WIs were segmented to gray matter with six parameters of VBM 8, with each parameter having between three and eight selectable levels. Reproducibility was evaluated as the standard deviation (mm3) of measured values for the five MRI systems. Results: Reproducibility was influenced by ‘Bias regularization (BiasR)’, ‘Bias FWHM’, and ‘De-noising filter’ settings, but not by ‘MRF weighting’, ‘Sampling distance’, or ‘Warping regularization’ settings. Reproducibility in BiasR was influenced by ROI. Superior reproducibility was observed in Frontal Lobe with the BiasR1 setting, and in Hippocampus, Parietal Lobe, and Putamen with the BiasR3*, BiasR1, and BiasR5 settings, respectively. Conclusion: Reproducibility of gray matter volumetry was influenced by parameter settings in VBM 8 using DARTEL and ROI. In multi-center studies, the use of appropriate settings in VBM 8 with DARTEL results in reduced scanner effect.

Objective The aim of this study was to investigate the effects of single-scan and scan-pair intensity inhomogeneity correction methods on the repeatability of voxel-based morphometry (VBM) using images acquired with multiple magnetic resonance (MR) scanners. Methods Three-dimensional T1-weighed MR images of the brain were obtained from 22 healthy participants using each of 5 MR scanners, yielding 110 images (5 scanners × 22 subjects) in total. Six patterns of intensity inhomogeneity corrections (no correction, single-scan corrections, and scan-pair correction, and their combinations) were applied in the VBM procedure to investigate the effect of the corrections on the repeatability of gray and white matter volume measurements. Results Single-scan and scan-pair intensity inhomogeneity corrections significantly reduced the variance in spatially normalized gray and white matter volumes. However, combining the 2 methods did not significantly improve the repeatability when evaluated as whole brain. Conclusions Single-scan and scan-pair intensity inhomogeneity corrections improved the repeatability of gray and white matter volumes obtained by multiple MR scanners and assessed by VBM.

Background and purpose Segmented brain tissue and myelin volumes can now be automatically calculated using dedicated software (SyMRI), which is based on quantification of R1 and R2 relaxation rates and proton density. The aim of this study was to determine the validity of SyMRI brain tissue and myelin volumetry using various in-plane resolutions. Methods We scanned 10 healthy subjects on a 1.5T MR scanner with in-plane resolutions of 0.8, 2.0 and 3.0 mm. Two scans were performed for each resolution. The acquisition time was 7-min and 24-sec for 0.8 mm, 3-min and 9-sec for 2.0 mm and 1-min and 56-sec for 3.0 mm resolutions. The volumes of white matter (WM), gray matter (GM), cerebrospinal fluid (CSF), non-WM/GM/CSF (NoN), brain parenchymal volume (BPV), intracranial volume (ICV) and myelin were compared between in-plane resolutions. Repeatability for each resolution was then analyzed. Results No significant differences in volumes measured were found between the different in-plane resolutions, except for NoN between 0.8 mm and 2.0 mm and between 2.0 mm and 3.0 mm. The repeatability error value for the WM, GM, CSF, NoN, BPV and myelin volumes relative to ICV was 0.97%, 1.01%, 0.65%, 0.86%, 1.06% and 0.25% in 0.8 mm; 1.22%, 1.36%, 0.73%, 0.37%, 1.18% and 0.35% in 2.0 mm and 1.18%, 1.02%, 0.96%, 0.45%, 1.36%, and 0.28% in 3.0 mm resolutions. Conclusion SyMRI brain tissue and myelin volumetry with low in-plane resolution and short acquisition times is robust and has a good repeatability so could be useful for follow-up studies.

Background To evaluate the sensitivity, specificity, and accuracy of time-of-flight MR angiography (MRA) compared with CT angiography (CTA) for detection of branch vessels arising from the intracranial aneurysm sac, using a 3-tesla MR scanner and an area detector CT scanner. Methods Fifty patients with an intracranial aneurysm (25 patients with a branch vessel arising from the sac and 25 patients without) underwent rotational intraarterial digital subtraction angiography (rotational DSA), MRA, and CTA. The following six image sets were assessed using a 3-point scale for the existence of a branch vessel: (1) volume rendering with MRA (MRA-VR); (2) CTA-VR; (3) source images with MRA; (4) source images with CTA; (5) VR and source images with MRA (‘MRA-VR + source images’); and (6) ‘CTA-VR + source images’. Each set comprised the data of all 50 patients. Three radiologists then performed consensus review and calculated the sensitivity, specificity, and accuracy for MRA and CTA; rotational DSA was considered as the gold standard. Results Higher accuracies were obtained in review using VR + source compared with both VR and source alone. In addition, higher accuracies were obtained in review using MRA compared with CTA. The highest accuracy of 0.96 was obtained for ‘MRA-VR + source images’; in contrast, accuracy on ‘CTA-VR + source images’ was 0.86. Conclusions The results show that 3-tesla MRA is an ideal non-invasive imaging examination for detection of a branch vessel arising from an intracranial aneurysm sac. Higher accuracy, especially sensitivity, with MRA compared with CTA was found in ICA, but accuracy with MRA was same as that with CTA in MCA.

Our aim was to show whether sensitivity for detecting volume changes in regional gray matter in default mode network (DMN) at converted [from mild cognitive impairment to Alzheimer's disease (from MCI to AD)] phase was improved by use of a standardized volume with global gray-matter volume. T1-weighted MR images (T1WI) of seven normal subjects and seven converted (from MCI to AD) patients were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. Gray-matter images segmented with Statistical Parametric Mapping 5 were measured by the atlas-based method. We focused on five nodes of the DMN. For each phase, region of interest (ROI) volumes in the five nodes were standardized by two methods: (1) the ratio to the screening phase (S_volume) and (2) the ratio to the screening phase after both volumes were standardized by the global gray-matter volume (S_N_volume). Significant group differences between longitudinal gray-matter volume change of the converted (from MCI to AD) group and that of the normal group were found in lateral temporal cortex by S_N_volume, and precuneus by S_N_volume. These findings are useful for improving the understanding of DMN volume changes at the converted (from MCI to AD) phase.

Purpose To show the gray matter volumes in the default mode network (DMN) using the atlas-based method and to evaluate age-related volume change in the DMN region. Estimation of gray matter volumes is interesting research because previous reports showed an association with gray matter volume (GMV) and diseases. Materials and Methods We focused on five nodes of the DMN (posterior cingulate, precuneus, lateral temporal cortex [LTC], medial prefrontal cortex, and inferior parietal lobule). In all, 1122 healthy adults were included in the present study. T1-weighted magnetic resonance (MR) images were obtained using a 3T-MR scanner. To investigate GMV in the DMN, segmented gray matter images were measured by the atlas-based method, using Statistical Parametric Mapping 5. Volumes were expressed using three different methods: region of interest (ROI)-volume (mL), the volume itself; ROI-TIV (%), as a percentage of total intracranial volume (individual difference of head size is corrected); and ROI-GMV (%), as a percentage of gray matter volume (individual difference of atrophy speed for aging is corrected). Results Negative correlations between measurement values on ROI and age were observed in all five ROIs of the DMN region by two measures of volume (ROI-volume (mL) and ROI-TIV (%)), in both genders. In contrast, positive correlations between measurement values on ROI and age were observed in the posterior cingulate and LTC with ROI-GMV (%), in both genders. Conclusion The present study is the first report about volume change in the DMN that includes age-related effects. J. Magn. Reson. Imaging 2014;39:132–142. © 2013 Wiley Periodicals, Inc.

This study aimed to investigate whether the effect of scanner for cortex volumetry with atlas-based method is reduced using Diffeomorphic Anatomical Registration Through Exponentiated Lie Algebra (DARTEL) normalization compared with standard normalization. Three-dimensional T1-weighted magnetic resonance images (3D-T1WIs) of 21 healthy subjects were obtained and evaluated for effect of scanner in cortex volumetry. 3D-T1WIs of the 21 subjects were obtained with five MRI systems. Imaging of each subject was performed on each of five different MRI scanners. We used the Voxel-Based Morphometry 8 tool implemented in Statistical Parametric Mapping 8 and WFU PickAtlas software (Talairach brain atlas theory). The following software default settings were used as bilateral region-of-interest labels: “Frontal Lobe,” “Hippocampus,” “Occipital Lobe,” “Orbital Gyrus,” “Parietal Lobe,” “Putamen,” and “Temporal Lobe.” Effect of scanner for cortex volumetry using the atlas-based method was reduced with DARTEL normalization compared with standard normalization in Frontal Lobe, Occipital Lobe, Orbital Gyrus, Putamen, and Temporal Lobe; was the same in Hippocampus and Parietal Lobe; and showed no increase with DARTEL normalization for any region of interest (ROI). DARTEL normalization reduces the effect of scanner, which is a major problem in multicenter studies.

To explore the possibility of decreasing the radiation dose during digital tomosynthesis (DT) for arthroplasty, we compared the image qualities of several reconstruction algorithms, such as filtered back projection (FBP) and two iterative reconstruction (IR), methods maximum likelihood expectation maximization (MLEM) and the simultaneous iterative reconstruction technique (SIRT) under different radiation doses. The three algorithms were implemented using a DT system and experimentally evaluated by contrast-to-noise ratio (CNR), artifact spread function (ASF), and power spectrum measurements on a prosthesis phantom. The CNR and ASF data were statistically analyzed by a one-way analysis of variance. The effectiveness of each technique for enhancing the visibility of the prosthesis phantom was quantified by the CNR (reference dose vs. 20 % reduced dose in FBP, P = 0.62; reference vs. 37 % reduced dose in FBP, P = 0.16; reference vs. 55 % reduced dose in FBP, P

Purpose: An understanding of the repeatability of measured results is important for both the atlas-based and voxel-based morphometry (VBM) methods of magnetic resonance (MR) brain volumetry. However, many recent studies that have investigated the repeatability of brain volume measurements have been performed using static magnetic fields of 1–4 tesla, and no study has used a low-strength static magnetic field. The aim of this study was to investigate the repeatability of measured volumes using the atlas-based method and a low-strength static magnetic field (0.4 tesla). Materials and Methods: Ten healthy volunteers participated in this study. Using a 0.4 tesla magnetic resonance imaging (MRI) scanner and a quadrature head coil, three-dimensional T1-weighted images (3D-T1WIs) were obtained from each subject, twice on the same day. VBM8 software was used to construct segmented normalized images [gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) images]. The regions-of-interest (ROIs) of GM, WM, CSF, hippocampus (HC), orbital gyrus (OG), and cerebellum posterior lobe (CPL) were generated using WFU PickAtlas. The percentage change was defined as [100 × (measured volume with first segmented image−mean volume in each subject)/(mean volume in each subject)] The average percentage change was calculated as the percentage change in the 6 ROIs of the 10 subjects. Results: The mean of the average percentage changes for each ROI was as follows: GM, 0.556%; WM, 0.324%; CSF, 0.573%; HC, 0.645%; OG, 1.74%; and CPL, 0.471%. The average percentage change was higher for the orbital gyrus than for the other ROIs. Conclusion: We consider that repeatability of the atlas-based method is similar between 0.4 and 1.5 tesla MR scanners. To our knowledge, this is the first report to show that the level of repeatability with a 0.4 tesla MR scanner is adequate for the estimation of brain volume change by the atlas-based method.

The aim of the present study was to investigate the correlation between local gray matter volume and signal-to-noise ratio on T2-weighted imaging (T2-SNR) of putamen in healthy adults using two tools: voxel-based morphometry (VBM) treating age as a confounding covariate to control for age-related gray matter volume changes and high spatial resolution T1-weighted imaging acquired with a 3.0-T magnetic resonance (3T-MR) scanner.Contiguous sagittal T1-weighted images and axial T2-weighted images of the brain were obtained from 1,380 healthy participants. T2-SNR of putamen was defined as A/B, where A is the mean T2-weighted signal intensity (T2-SI) in the right and left sides of putamen, and B is the background noise. The software Statistical Parametric Mapping 5 was used for image segmentation. The association between T2-SNR of putamen and gray matter volume was assessed with VBM, treating age as a confounding covariate.A significant positive correlation was obtained between T2-SNR of putamen and bilateral pre- and postcentral gyrus volume.To the best of our knowledge, this is the first VBM study to show an age-independent relationship between T2-SNR of putamen and bilateral pre- and postcentral gyrus volumes in healthy adults.