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The purpose of this study was to develop a mammography-based deep learning (DL) model for predicting the risk of breast cancer in Asian women. This retrospective study included 287 examinations in 153 women in the cancer group and 736 examinations in 447 women in the negative group, obtained from the databases of two tertiary hospitals between November 2012 and March 2022. All examinations were labeled as either dense breast or nondense breast, and then randomly assigned to either training, validation, or test sets. DL models, referred to as image-level and examination-level models, were developed. Both models were trained to predict whether or not the breast would develop breast cancer with two datasets: the whole dataset and the dense-only dataset. The performance of DL models was evaluated using the accuracy, precision, sensitivity, specificity, F1 score, and area under the receiver operating characteristic curve (AUC). On a test set, performance metrics for the four scenarios were obtained: image-level model with whole dataset, image-level model with dense-only dataset, examination-level model with whole dataset, and examination-level model with dense-only dataset with AUCs of 0.71, 0.75, 0.66, and 0.67, respectively. Our DL models using mammograms have the potential to predict breast cancer risk in Asian women.

This study aimed to compare susceptibility map-weighted imaging (SMwI) using various MRI machines (three vendors) with N-3-fluoropropyl-2-β-carbomethoxy-3-β-(4-iodophe nyl)nortropane (We prospectively recruited 257 subjects, including 157 patients with neurodegenerative parkinsonism, 54 patients with non-neurodegenerative parkinsonism, and 46 healthy subjects from 10 hospitals between November 2019 and October 2020. All participants underwent both SMwI andThe inter-observer agreement was 0.924 and 0.942 per SN and participant, respectively. The diagnostic sensitivity of SMwI was 97.9% and 99.4% per SN and participant, respectively; its specificity was 95.9% and 95.2%, respectively, and its accuracy was 97.1% and 97.7%, respectively. There was no significant difference between the results of SMwI andThis study demonstrated that the high diagnostic performance of SMwI was maintained in a multi-centre setting with various MRI scanners, suggesting the generalisability of SMwI for determining nigrostriatal degeneration in patients with parkinsonism.• Susceptibility map-weighted imaging helps clinicians to predict nigrostriatal degeneration. • The protocol for susceptibility map-weighted imaging can be standardised across MRI vendors. • Susceptibility map-weighted imaging showed diagnostic performance comparable to that of dopamine transporter PET in a multi-centre setting with various MRI scanners.

With the ongoing COVID-19 pandemic, several previous studies from different countries showed that physical activity (PA) decreased during the COVID-19 outbreak. However, few studies have examined the recent tendency of PA in the adolescent population. Thus, we aimed to investigate the long-term trend of PA in Korean youth and the prevalence changes between before and during the COVID-19 pandemic.Data from Korea Youth Risk Behavior Web-Based Survey (KYRBS) was collected for consecutive years between 2009 and 2021. The period was separated into pre-pandemic (2009-2019), early-pandemic (2020), and mid-pandemic (2021). Self-reported amount of PA was categorized into four groups (insufficient, aerobic, muscle strengthening, and both physical activities) according to World Health Organization PA guidelines.A total of 840,488 adolescents aged 12-18 who fully responded to the survey were selected (response rate: 95.2%). The 13-year trends in the proportion of adolescents who reported aerobic and muscle-strengthening activities met or exceeded 2020 WHO exercise guidelines for adolescents plateaued (11.9% from 2009-2011, 14.2% from 2018-2019, 14.4% from 2020, and 14.0% from 2021); however, the slope decreased during the pandemic (βThrough a nationwide representative study, there was no significant difference with regard to the number of Korean adolescents who achieved the PA guidelines (pre-and-post-pandemic); however, the prevalence of recommended levels of PA needs to increase more based on the trend before the COVID-19 outbreak. The findings of this study suggest reinforcement of the importance of public health policies for Korean youths to be more physically active, especially during and after the pandemic. This article is protected by copyright. All rights reserved.

The oligomeric amyloid-β (oAβ) is a reliable feature for an early diagnosis of Alzheimer's disease (AD). Therefore, the objective of this study was to demonstrate imaging of oAβ deposits using our developed DNA aptamer called ob5 conjugated with gadolinium (Gd)-dodecane tetraacetic acid (DOTA) as a contrast agent for early diagnosis of AD using MRI. An oAβ-specific aptamer was developed by amide bond formation and conjugated to Gd-DOTA MRI contrast agent and/or cyanine5 (cy5). We verified the performance of our new contrast agent with an AD mouse model using in vivo and ex vivo fluorescent imaging and animal MRI experiments. The presence of soluble Aβ in 3xTg AD mice was detected using GdDOTA-ob5-cy5 probe ex vivo. Fluorescence intensities of the GdDOTA-ob5-cy5 contrast agent were high in the brains of 3xTg-AD mice, but relatively low in the brains of control mice. The GdDOTA-ob5 contrast agent had higher relaxivity than a clinically available contrast agent. T1-weighted MRI signals in 5-month-old 3xTg AD mice increased at 5 min, were prolonged until 10 min, then decreased 15 min after injecting the GdDOTA-ob5 contrast agent. Our targeted DNA aptamer GdDOTA-ob5 contrast agent could be potentially useful for validating the efficacy of a novel diagnostic contrast agent for selectively targeting neurotoxic oAβ. It could ultimately be used for early diagnosis of AD.

Parosmia, defined as the distorted perception of an odor stimulus, has been reported to be associated with head trauma, upper respiratory tract infections, sinonasal diseases, and toxin/drug consumption. To date, little is known about parosmia in right-lateralized semantic variant primary progressive aphasia. A 60-year-old right-handed man presented with a 2-year history of parosmia and prosopagnosia. Brain magnetic resonance imaging demonstrated severe atrophy of the right anterior and mesial temporal lobe, particularly in the fusiform cortex and the regions known as the primary olfactory cortex. 18F-fluorodeoxyglucose position emission tomography showed asymmetric hypometabolism of the bilateral temporal lobes (right > left). We clinically diagnosed him with right-lateralized semantic variant primary progressive aphasia. As the right hemisphere is known to be more involved in the processing of pleasant odors than the left hemisphere, we speculate that the unique manifestation of parosmia observed in this patient might be associated with the lateralization of the olfactory system.

Mammography is the initial examination to detect breast cancer symptoms, and quality control of mammography devices is crucial to maintain accurate diagnosis and to safeguard against degradation of performance. The objective of this study was to assist radiologists in mammography phantom image evaluation by developing and validating an interpretable deep learning model capable of objectively evaluating the quality of standard phantom images for mammography.A total of 2,208 mammography phantom images were collected for periodic accreditation of the scanner from 1,755 institutions. The dataset was randomly split into training (1,808 images) and testing (400 images) datasets with subgroups (76 images) for the multi-reader study. To develop an interpretable model that contains two deep learning networks in series, five processing steps were performed: mammography phantom detection, phantom object detection, post-processing, score evaluation, and a report with a comment about ambiguous results.For phantom detection, the accuracy and mean intersection over union (mIOU) were 1.00 and 0.938 in the test dataset, respectively. During phantom object detection, a total of 6,369 out of 6,400 objects were detected as the correct object class, and the accuracy and mIOU were 0.995 and 0.813, respectively. The predicted score for each object showed a consensus of 97.40% excluding ambiguous points and 59.10% for ambiguous points of the groups.The interpretable deep learning model using large-scale data from multiple centers shows high performance and reasonable object scoring, successfully validating the reliability and feasibility of mammography phantom image quality management.

Background: Because Alzheimer’s Disease (AD) has very complicated pattern changes, it is difficult to evaluate it with a specific factor. Recently, novel machine learning methods have been applied to solve limitations. Objective: The objective of this study was to investigate the approach of classification and prediction methods using the Machine Learning (ML)-based Optimized Combination-Feature (OCF) set on Gray Matter Volume (GMV) and Quantitative Susceptibility Mapping (QSM) in the subjects of Cognitive Normal (CN) elderly, Amnestic Mild Cognitive Impairment (aMCI), and mild and moderate AD. Materials and Methods: 57 subjects were included: 19 CN, 19 aMCI, and 19 AD with GMV and QSM. Regions-of-Interest (ROIs) were defined at the well-known regions for rich iron contents and amyloid accumulation areas in the AD brain. To differentiate the three subject groups, the Support Vector Machine (SVM) with the three different kernels and with the OCF set was conducted with GMV and QSM values. To predict the aMCI stage, regression-based ML models were performed with the OCF set. The result of prediction was compared with the accuracy of clinical data. Results: In the group classification between CN and aMCI, the highest accuracy was shown using the combination of GMVs (hippocampus and entorhinal cortex) and QSMs (hippocampus and pulvinar) data using the 2nd SVM classifier (AUC = 0.94). In the group classification between aMCI and AD, the highest accuracy was shown using the combination of GMVs (amygdala, entorhinal cortex, and posterior cingulate cortex) and QSMs (hippocampus and pulvinar) data using the 2nd SVM classifier (AUC = 0.93). In the group classification between CN and AD, the highest accuracy was shown using the combination of GMVs (amygdala, entorhinal cortex, and posterior cingulate cortex) and QSMs (hippocampus and pulvinar) data using the 2nd SVM classifier (AUC = 0.99). To predict aMCI from CN, the exponential Gaussian process regression model with the OCF set using GMV and QSM data was shown the most similar result (RMSE = 0.371) to clinical data (RMSE = 0.319). Conclusion: The proposed OCF based ML approach with GMV and QSM was shown the effective performance of the subject group classification and prediction for aMCI stage. Therefore, it can be used as personalized analysis or diagnostic aid program for diagnosis.

We aimed to analyze plasma amyloid-β (Aβ)1–40 and Aβ1–42 using a highly sensitive dielectrophoretic-driven biosensor platform to demonstrate the possibility of precise cerebral amyloid angiopathy (CAA) diagnosis in participants classified according to Aβ-positron emission tomography (PET) positivity and the neuroimaging criteria for CAA. We prospectively recruited 25 people with non-Alzheimer's disease (non-AD) and 19 patients with Alzheimer's disease (AD), which were further classified into the CAA− and CAA+ (possible and probable CAA) groups according to the modified Boston criteria. Patients underwent plasma Aβ analysis using a highly sensitive nano-biosensor platform, Aβ-PET scanning, and detailed neuropsychological testing. As a result, the average signal levels of Aβ1–42/1–40 differed significantly between the non-AD and AD groups, and the CAA+ group exhibited significantly higher Aβ1–40 signal levels than the CAA− group in both non-AD and AD groups. The concordance between the Aβ1–40 signal level and the neuroimaging criteria for CAA was nearly perfect, with areas under the curve of 0.954 (95% confidence interval (CI) 0.856–1.000), 0.969 (0.894–1.000), 0.867 (0.648–1.000), and 1.000 (1.000–1.000) in the non-AD/CAA− vs. non-AD/possible CAA, non-AD/CAA− vs. non-AD/probable CAA, AD/CAA− vs. AD/possible CAA, and AD/CAA− vs. AD/probable CAA groups, respectively. Higher Aβ1–40 signal levels were significantly associated with the presence of CAA according to regression analyses, and the neuroimaging pattern analysis partly supported this result. Our findings suggest that measuring plasma Aβ1–40 signal levels using a highly sensitive biosensor platform could be a useful non-invasive CAA diagnostic method.

Objective: To investigate the association between plasma amyloid-β (Aβ) levels and neuropsychological performance in patients with cognitive decline using a highly sensitive nano-biosensing platform.Methods: We prospectively recruited 44 patients with cognitive decline who underwent plasma Aβ analysis, amyloid positron emission tomography (PET) scanning, and detailed neuropsychological tests. Patients were classified into a normal control (NC, n = 25) or Alzheimer’s disease (AD, n = 19) group based on amyloid PET positivity. Multiple linear regression was performed to determine whether plasma Aβ (Aβ40, Aβ42, and Aβ42/40) levels were associated with neuropsychological test results.Results: The plasma levels of Aβ42/40 were significantly different between the NC and AD groups and were the best predictor of amyloid PET positivity by receiver operating characteristic curve analysis [area under the curve of 0.952 (95% confidence interval, 0.892–1.000)]. Although there were significant differences in the neuropsychological performance of cognitive domains (language, visuospatial, verbal/visual memory, and frontal/executive functions) between the NC and AD groups, higher levels of plasma Aβ42/40 were negatively correlated only with verbal and visual memory performance.Conclusion: Our results demonstrated that plasma Aβ analysis using a nano-biosensing platform could be a useful tool for diagnosing AD and assessing memory performance in patients with cognitive decline.

SUMMARY Two cases of complicated crown fracture of the maxillary incisors were restored using the fragment reattachment technique. Root canal treatment was performed, and the fractured fragment was bonded to the tooth structure using a dentin adhesive system and a flowable composite resin, followed by the insertion of a fiber post using dual-cured resin cement. Reattached fragments have shown reliable prognosis without inflammatory signs around bonded junctions after long-term follow-up.

Background Rete mirabile is a very rare vascular malformation and superior cerebellar artery (SCA) rete mirabile is not reported previously. We report a new case of rete mirabile of SCA initially detected by magnetic resonance imaging and transfemoral cerebral angiography. Case Description This report illustrates the case of a 58-year-old man who presented with vertical diplopia. Brain magnetic resonance angiography and transfemoral cerebral angiography revealed a rete mirabile of SCA and 3-dimensional volume isotropic turbo spin echo acquisition, brain magnetic resonance imaging sequence, demonstrated that the cisternal segment of the ipsilateral trochlear nerve was compressed by this vascular malformation. We assumed that his cranial nerve palsy was caused by the rete mirabile of the right SCA. During the 8 weeks presence of diplopia, the patient was observed and the symptom was relieved spontaneously. Conclusions We provide a first report in the literature of rete mirabile involving the SCA and suggest a descriptive knowledge of rete mirabile for clinicians during decision-making of treatment.

Background Sinus X-ray imaging is still used in the initial evaluation of paranasal sinusitis, which is diagnosed by the opacification or air/fluid level in the sinuses and best seen in the Waters' view of the paranasal sinus (PNS). The objective of this study was to investigate the feasibility of recognizing the maxillary sinusitis features using PNS X-ray images, as well as to propose the most effective method of determining a reasonable consensus using multiple deep learning models. Methods A total of 4,860 patients, which included 2,430 normal and maxillary sinusitis subjects each, underwent Waters' view PNS X-ray scan. The datasets were randomly split into training (70%), validation (15%), and test (15%) subsets. We implemented a majority decision algorithm to determine a reasonable consensus using three multiple convolutional neural network (CNN) models: VGG-16, VGG-19, and ResNet-101. The performance of sinusitis detection was evaluated with quantitative accuracy (ACC) and activation maps. Results We compared the results of our approaches with ACC and activation maps. ACC [and area under the curve (AUC)] of the internal test dataset was evaluated as 87.4% (0.891), 90.8% (0.891), 93.7% (0.937), and 94.1% (0.948) for VGG-16, VGG-19, ResNet-101, and the majority decision, respectively. ACC (and AUC) of the external test dataset was evaluated as 87.58% (0.877), 87.58% (0.877), 92.12% (0.929), and 94.12% (0.942) for VGG-16, VGG-19, ResNet-101, and the majority decision, respectively. Majority decision algorithms can detect missing and correct lesions using a compensation function of the majority decision. Conclusions The majority decision algorithm showed high accuracy and significantly more accurate lesion detection compared with those of individual CNN models. The proposed deep learning method with PNS X-ray images can be used as an adjunct to classify maxillary sinusitis.

Objective Intracranial aneurysm (IA) is the leading cause of subarachnoid hemorrhage. The pathomechanisms of IA are poorly understood but can be related to arterial tortuosity resulting from underlying systemic factors leading to arterial wall weakening. We aimed to analyze the tortuosity of the intracranial artery in a cohort with IA, hypothesizing that the tortuosity of intracranial arteries differs depending on the characteristics of the IA. Methods Patients with saccular IA were consecutively enrolled. Clinical factors and vascular tortuosity of the right and left middle cerebral arteries and basilar artery (BA) of all patients with IA were compared according to the characteristics of the IA: 1) ruptured versus unruptured, 2) multiple versus single, and 3) large (>5 cm) versus small (≤5 cm). Unruptured IAs were comparatively analyzed according to aneurysm size and aspect ratio, whereas ruptured IAs were analyzed according to aneurysm size. Results Two hundred eighty-five patients were enrolled (mean age, 59 years; 71.2% women). The tortuosity of the BA was higher in the large IA group (5.63 ± 6.26; n = 133; P = 0.009), large unruptured IA group (6.64 ± 6.32; n = 53; P = 0.039), and large ruptured IA group (5.50 ± 6.52; n = 80; P = 0.033) compared with the small IA, small unruptured IA, and small ruptured IA group. In multivariate analysis, increased BA tortuosity was significantly associated with large IAs (β = 1.066; P = 0.008), unruptured large IAs (β = 1.077; P = 0.033), and ruptured large IAs (β = 1.086; P = 0.025). Conclusions The BA tortuosity was higher in patients with large IAs, which may represent an imaging biomarker of aneurysm growth.

Background: The aim of the present study was to investigate the associations between thyroid hormones, cognitive performance, and apolipoprotein E (APOE) genotype in euthyroid patients with subjective cognitive decline (SCD). Methods: We recruited 197 euthyroid patients that fulfilled the criteria for SCD. All participants were classified into APOE ε4 carriers and non-carriers based on the presence of the APOE ε4 allele. Patients with SCD who had the APOE ε2/ε4 genotype were excluded from the study. We then performed correlation and regression analyses to evaluate the associations between cognitive performance and thyroid hormones in APOE ε4 carriers and non-carriers. Results: We found no significant differences in cognitive function between APOE ε4 carriers and non-carriers. However, higher levels of triiodothyronine (T3) were associated with better verbal memory performance (immediate and delayed recall tasks) in APOE ε4 carriers, whereas a negative association was found in APOE ε4 non-carriers. Furthermore, there was a significant interactive effect of APOE ε4 status and T3 levels on verbal memory performance (immediate and delayed recall tasks). Conclusions: These findings suggest that in patients with SCD, T3 might have a protective effect on memory in those who are APOE ε4 carriers. The differential susceptibility hypothesis would thus support a gene-by-hormone crossover interaction between APOE ε4 allele and T3 in this study. Early identification and intervention of high-risk individuals for cognitive decline is important to establish new strategies for preventing dementia.

BACKGROUND Although several outcome prediction scores incorporated with pretreatment variables have been developed for acute ischemic stroke (AIS) patients, there is not currently a prediction score that includes pretreatment imaging that can show salvageable brain tissue. OBJECTIVE To evaluate whether addition of the collateral grade on computed tomography angiography to previously published prediction scores could increase accuracy of clinical outcome prediction in endovascular thrombectomy (EVT) for AIS. METHODS This study used a retrospective multicenter registry for patients undergoing EVT for anterior circulation large vessel occlusion. Three previously published outcome prediction scores (Houston intra-arterial therapy 2, HIAT2; totaled health risks in vascular events, THRIVE; and Pittsburgh response to endovascular therapy, PRE scores) were tested in this study. Using 482 deprivation cohorts, areas under the receiver operating characteristic curves (AUC-ROCs) were compared between prediction scores with/without collateral grades in predicting the poor outcomes (modified Rankin Scale 4-6 at 3-mo follow-up) after EVT. We developed modified prediction scores by adding the collateral grade, and their advancement of outcome prediction was validated using 208 independent validation cohorts. RESULTS AUC-ROCs of HIAT2, THRIVE, and PRE scores that incorporated with collateral grade were superior in predicting poor outcomes when compared to that of the unmodified scores (P

An intraosseous lipoma is a rare histologic variant of lipoma, accounting for only 0.1% of all primary bone tumors. This may not be the actual incidence because most of these lesions are frequently asymptomatic, but imaging modalities, such as computed tomography or magnetic resonance imaging (MRI) seem to have increased the detection rate. Lipoma occasionally undergoes osseous metaplasia and becomes an osseous lipoma. Although there are numerous papers discussing intraosseous lipoma and some authors have tried to differentiate lipomas from osseous lipomas, there is still a great deal of confusion with regard to characteristic radiologic features and the use of terms. Use of the mDixon sequence in MRI could be an effective, noninvasive method of lesion detection and differential diagnosis. Texture analysis is a useful technique for capturing intratumoral characteristics. We report what is possibly the first use of the mDixon MRI sequence in the measurement of tumoral texture in a case of the extremely rare inferior nasal turbinate intraosseous lipoma in a 58-year-old female. We conclude that mDixon and texture analysis are helpful methods for differentiating intraosseous lipomas from other masses and confirming the benign characteristics of lipoma. Our review of head and neck intraosseous lipoma could be of particular interest to head and neck surgeons and dental clinicians.

Purpose: The hyperintense acute reperfusion marker (HARM) is characterized by the delayed enhancement of the subarachnoid or subpial space observed on postcontrast fluid-attenuated inversion recovery (FLAIR) images, and is considered a cerebral reperfusion marker for various brain disorders, including infarction. In this study, we evaluated the cerebral distribution patterns of HARM for discriminating between an enhancing subacute infarction and an enhancing mass located in the cortex and subcortical white matter. Materials and methods: We analyzed consecutive patients who experienced a subacute ischemic stroke, were hospitalized, and underwent conventional brain magnetic resonance imaging including postcontrast FLAIR within 14 days from symptom onset, as well as those who had lesions corresponding to a clinical sign detected by diffusion-weighted imaging and postcontrast T1-weighted imaging between May 2019 and May 2021. A total of 199 patients were included in the study. Of them, 94 were finally included in the subacute infarction group. During the same period, 76 enhancing masses located in the cortex or subcortical white matter, which were subcategorized as metastasis, malignant glioma, and lymphoma, were analyzed. We analyzed the overall incidence of HARM in subacute ischemic stroke cases, and compared the enhancement patterns between cortical infarctions and cortical masses. Results: Among 94 patients with subacute stroke, 78 patients (83%) presented HARM, and among 76 patients with subcortical masses, 48 patients (63%) presented peripheral rim enhancement. Of 170 subcortical enhancing lesions, 88 (51.8%) showed HARM, and 78 (88.6%) were determined to be subacute infarction. Among 94 patients with subacute stroke, 48 patients (51%) had diffusion restrictions, and HARM was found in 39 patients (81.2%). Of the 46 patients (49%) without diffusion restriction, 39 patients (84.8%) showed HARM. Conclusions: The presence of HARM was significantly associated with subacute infarctions. For the masses, a peripheral rim enhancement pattern was observed around the mass rather than the cerebral sulci on postcontrast FLAIR.

This study evaluated the susceptibility effects using quantitative susceptibility mapping (QSM) as a marker for regional brain iron alternations in diabetic rat brains. Otsuka Long-Evans Tokushima Fatty (OLETF) rats (n = 7) and age-matched lean Long-Evans Tokushima Otsuka (LETO) control rats (n = 8) were scanned using a three-dimensional gradient-echo sequence in a 3 Tesla MRI to map QSM. The correlation analysis was performed to evaluate the relationship between the QSM value and the body weight and the fasting blood glucose value of the rats. QSM values in the OLETF group were significantly increased in hippocampi and thalami, as compared with those of the LETO group. The QSM values of the OLETF rats were negatively correlated with the body weight in putamen and were significantly correlated with the fasting blood glucose level in the left amygdala and right thalamus. With higher QSM values in diabetic rats than in control rats, QSM values in the diabetic group were significantly correlated with the body weight and the fasting blood glucose level, indicating that the QSM technique should be helpful to noninvasively investigate iron overload in the diabetic brain.

Complex regional pain syndrome (CRPS) is one of the most challenging chronic pain conditions and is characterized by burning pain, allodynia, hyperalgesia, autonomic changes, trophic changes, edema, and functional loss involving mainly the extremities. Until recently, very few reports have been published concerning CRPS involving the orofacial area. We report on a 50-year-old female patient who presented with unbearable pain in all of her teeth and hypersensitivity of the facial skin. She also reported intractable pain in both extremities accompanied by temperature changes and orofacial pain that increased when the other pains were aggravated. In the case of CRPS with trigeminal neuropathic pain, protocols for proper diagnosis and prompt treatment have yet to be established in academia or in the clinical field. We performed functional magnetic resonance imaging for a thorough analysis of the cortical representation of the affected orofacial area immediately before and immediately after isolated light stimulus of the affected hand and foot and concluded that CRPS can be correlated with trigeminal neuropathy in the orofacial area. Furthermore, the patient was treated with carbamazepine administration and stellate ganglion block, which can result in a rapid improvement of pain in the trigeminal region.

The objective of this study was to evaluate susceptibility changes caused by iron accumulation in cognitive normal (CN) elderly, those with amnestic mild cognitive impairment (aMCI), and those with early state AD, and to compare the findings with gray matter volume (GMV) changes caused by neuronal loss. The participants included 19 elderly CN, 19 aMCI, and 19 AD subjects. The voxel-based quantitative susceptibility map (QSM) and GMV in the brain were calculated and the differences of those insides were compared among the three groups. The differences of the QSM data and GMVs among the three groups were investigated by voxel-based and region of interest (ROI)-based comparisons using a one-way analysis of covariance (ANCOVA) test with the gender and age as covariates. Finally, a receiver-operating-characteristic (ROC) curve analysis was performed. The voxel-based results showed that QSM demonstrated more areas with significant difference between the CN and AD groups compared to GMV. GMVs were decreased, but QSM values were increased in aMCI and AD groups compared with the CN group. QSM better differentiated aMCI from CN than GMV in the precuneus and allocortex regions. In the accumulation regions of iron and amyloid β, QSM can be used to differentiate between CN and aMCI groups, indicating a useful an auxiliary imaging for early diagnosis of AD., Highlights • The susceptibility difference was more sensitive than GMV change in known regions of iron and the amyloid β accumulations. • QSM values were increased in patients compared with normal elderly and better differentiated aMCI from CN than GMV values. • The QSM technology can be used as an auxiliary imaging for early diagnosis of AD.

The physical and technical development of chemical-exchange-saturation-transfer (CEST) magnetic resonance imaging (MRI) using clinical 3 T MRI was explored with the goal of mapping asparagine (Asn), gamma-aminobutyric acid (GABA), glutamate (Glu), glycine (Gly), and myoinositol (MI), which exist in the brain. Phantoms with nine different conditions at concentrations of 10, 30, and 50 mM and pH values of 5.6, 6.2, and 7.4 were prepared for the five target molecules to evaluate the dependence of the CEST effect in the concentration, the pH, and the amplitude of the applied radiofrequency field B1. CEST images in the offset frequency range of ±6 parts per million (ppm) were acquired using a pulsed radio-frequency saturation scheme with a clinical 3 T MRI system. A voxel-based main magnetic field B0 inhomogeneity correction, where B0 is the center frequency offset at zero ppm, was performed by using the spline interpolation method to fit the full Z-spectrum to estimate the center frequency. A voxel-based CEST asymmetry map was calculated to evaluate amide (-NH), amine (-NH2), and hydroxyl (-OH) groups for the five target molecules. The CEST effect for Glu, GABA, and Gly clearly increased with increasing concentrations. The CEST effect for MI was minimal, with no noticeable differences at different concentrations. The CEST effect for Glu and Gly increased with increasing acidity. The highest CEST asymmetry for GABA was observed at pH 6.2. The CEST effect for Glu, GABA, and Gly increased with increasing B1 amplitude. For all target molecules, the CEST effect for the human 3 T MRI system increased with increasing concentration and B1 amplitude, but varied with pH, depending on the characteristics of the molecules. The CEST effect for MI may be not suitable with clinical MRI systems. These results show that CEST imaging in the brain with the amine protons by using 3 T MRI is possible for several neuronal diseases.

A 37-year-old male presented to the neurology department after developing sudden headache, nausea, and visual disturbances. The patient had been diagnosed with hypertension 5 years previously, but was not currently taking appropriate medication; he also had a 20-pack/year history of smoking.

Purpose: Although a number of studies have focused on finding anatomical regions in which iron concentrations are high, no study has been conducted to examine the overall variations in susceptibility maps of Alzheimer’s disease (AD). The objective of this study, therefore, was to differentiate AD from cognitive normal (CN) and mild cognitive impairment (MCI) using a texture analysis of quantitative susceptibility maps (QSMs). Methods: The study was approved by the local institutional review board, and informed consent was obtained from all subjects. In each participant group—CN, MCI, and AD—18 elderly subjects were enrolled. A fully first-order flow-compensated 3D gradient-echo sequence was run to obtain axial magnitudes and phase images and to produce QSM data. Sagittal structural 3D T1-weighted (3DT1W) images were also obtained with the magnetization-prepared rapid acquisition of gradient-echo sequence to obtain brain tissue images. The first- and second-order texture parameters of the QSMs and 3DT1W images were obtained to evaluate group differences using a one-way analysis of covariance. Results: For the first-order QSM analysis, mean, standard deviation, and covariance of signal intensity separated the subject groups (F = 5.191, p = 0.009). For the second-order analysis, angular second moment, contrast, and correlation separated the subject groups (F = 6.896, p = 0.002). Finally, a receiver operating characteristic curve analysis differentiated MCI from CN in white matter on the QSMs (z = 3.092, p = 0.0020). Conclusions: This was the first study to evaluate the textures of QSM in AD, which overcame the limitations of voxel-based analyses. The QSM texture analysis successfully distinguished both AD and MCI from CN and outperformed the voxel-based analysis using 3DT1-weighed images in separating MCI from CN. The first-order textures were more efficient in differentiating MCI from CN than did the second-order.