Background: Cross-sectional studies have shown lower cerebral blood flow (CBF) in Alzheimer’s disease (AD), but longitudinal CBF changes in AD are still unknown. Objective: To reveal the longitudinal CBF changes in normal control (NC) and the AD continuum using arterial spin labeling perfusion magnetic resonance imaging (ASL MRI). Methods: CBF was calculated from two longitudinal ASL scans acquired 2.22±1.43 years apart from 140 subjects from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). At the baseline scan, the cohort contained 41 NC, 74 mild cognitive impairment patients (MCI), and 25 AD patients. 21 NC converted into MCI and 17 MCI converted into AD at the follow-up. Longitudinal CBF changes were assessed using paired-t test for non-converters and converters separately at each voxel and in the meta-ROI. Age and sex were used as covariates. Results: CBF reductions were observed in all subjects. Stable NC (n = 20) showed CBF reduction in the hippocampus and precuneus. Stable MCI patients (n = 57) showed spatially more extended CBF reduction patterns in hippocampus, middle temporal lobe, ventral striatum, prefrontal cortex, and cerebellum. NC-MCI converters showed CBF reduction in hippocampus and cerebellum and CBF increase in caudate. MCI-AD converters showed CBF reduction in hippocampus and prefrontal cortex. CBF changes were not related with longitudinal neurocognitive changes. Conclusion: Normal aging and AD continuum showed common longitudinal CBF reductions in hippocampus independent of disease and its conversion. Disease conversion independent longitudinal CBF reductions escalated in MCI subjects.
Arterial spin labeling (ASL) perfusion MRI has been increasingly used in Alzheimer's Disease (AD) research. Because ASL implementations differ greatly in signal preparations and data acquisition strategies, both resulting in a large difference of signal-to-noise ratio (SNR), a comparison of different sequences that are widely available in major MR vendors is vital. The purpose of this study was to compare three types of commercial ASL MRI methods in Siemens and GE scanners: 2D Pulsed ASL (PASL), 3D Background Suppressed (BS) PASL, and 3D BS Pseudo-Continuous ASL (PCASL). We used data from 100 healthy control (NC), 75 mild cognitive impairment (MCI), and 57 Alzheimer's disease (AD) subjects from the AD neuroimaging initiative (ADNI). Both cross-sectional perfusion difference and perfusion vs clinical assessment correlations were compared across the three types of data. Different perfusions change patterns were observed, being 3D + BS ASL more sensitive than 2D ASL and 3D PASL the most sensitive. Signi cant lower CBF were found in MCI compared with NC subjects in the left and right middle temporal gyrus, right cerebellum, and left postcentral gyrus; and signi cantly higher CBF in MCI compared to NC in the left and right precuneus, left middle temporal gyrus, and right calcarine gyrus.Signi cant lower CBF were found in AD compared with MCI in left and right cerebellum and right MCC, and signi cantly higher CBF in AD compared with MCI in left and right insula lobe, right thalamus, right calcarine gyrus, left and right thalamus, and right and left cerebellum.
Background Arterial spin labeling (ASL) perfusion magnetic resonance imaging (MRI) denoising through deep learning (DL) often faces insufficient training data from patients. One solution is to train DL models using healthy subjects' data which are more widely available and transfer them to patients' data. Purpose To evaluate the transferability of a DL‐based ASL MRI denoising method (DLASL). Study Type Retrospective. Subjects Four hundred and twenty‐eight subjects (189 females) from three cohorts. Field Strength/Sequence 3 T two‐dimensional (2D) echo‐planar imaging (EPI)‐based pseudo‐continuous ASL (PCASL) and 2D EPI‐based pulsed ASL (PASL) sequences. Assessment DLASL was trained using young healthy adults' PCASL data (Dataset 1: 250/30 subjects as training/validation set) and was directly transferred (DTF) to PCASL data from Dataset 2 (45 subjects test set) of normal controls (NC) and Alzheimer's disease (AD) groups. DLASL was fine‐tuned (DLASLFT) and tested on PASL data from Dataset 3 (103 subjects test set) of NC and AD. An existing non‐DL method (NonDL) was used for comparison. Cerebral blood flow (CBF) images from ASL MRI were compared between NC and AD to assess characteristic hypoperfusion (lower CBF) patterns in AD. CBF image quality and CBF map sensitivity for detecting hypoperfusion using peak t‐value and suprathreshold cluster size are outcome measures. Statistical Tests Paired t‐test, two‐sample t‐test, one‐way analysis of variance, and Tukey honestly significant difference, and linear mixed‐effects models were used. P < 0.05 was considered statistically significant. Results Mean contrast‐to‐noise ratio (CNR) of Dataset 2 showed that DTF outperformed NonDL (AD: 3.38 vs. 2.64, NC: 3.80 vs. 3.36). On Dataset 3, DLASLFT outperformed NonDL measured by mean CNR (AD: 2.45 vs. 1.87, NC: 2.54 vs. 2.17) and mean radiologic score (2.86 vs. 2.44). Image quality improvement was significant on both test sets. DTF and DLASLFT improved sensitivity for detecting AD‐related hypoperfusion patterns compared with NonDL. Data Conclusion We demonstrated the DLASL's transferability across different ASL sequences and different populations. Level of Evidence 3 Technical Efficacy Stage 2
Unmanned Aircraft Systems (UAS) have been widely applied for reconnaissance and surveillance by exploiting information collected from the digital imaging payload. The super-resolution (SR) mosaicing of low-resolution (LR) UAS surveillance video frames has become a critical requirement for UAS video processing and is important for further effective image understanding. In this paper we develop a novel super-resolution framework, which does not require the construction of sparse matrices. The proposed method implements image operations in the spatial domain and applies an iterated back-projection to construct super-resolution mosaics from the overlapping UAS surveillance video frames. The Steepest Descent method, the Conjugate Gradient method and the Levenberg-Marquardt algorithm are used to numerically solve the nonlinear optimization problem for estimating a super-resolution mosaic. A quantitative performance comparison in terms of computation time and visual quality of the superresolution mosaics through the three numerical techniques is presented.
Entropy indicates irregularity of a dynamic system with higher entropy indicating higher irregularity and more transit states. In the human brain, regional brain entropy (BEN) has been increasingly assessed using resting state fMRI (rsfMRI), while changes of regional BEN during task-based fMRI have been scarcely studied. The purpose of this study is to characterize task-induced regional BEN alterations using the large Human Connectome Project (HCP) data. To control the potential modulation by the block-design, BEN of task-fMRI was calculated from the fMRI images acquired during the task conditions only and then compared to BEN of rsfMRI. Moreover, BEN was separately calculated from the control blocks of the task-fMRI runs and compared to BEN of task conditions. Finally, BEN of control conditions was compared to rsfMRI-derived BEN to test for residual task effects in the control condition. With respect to resting state, task-performance unanimously induced BEN reduction in the peripheral cortical area, including both task-related regions and task non-specific regions, and BEN increase in the centric part of the sensorimotor and perception networks. Task control condition showed large residual task effects. After controlling the task non-specific effects using the control BEN vs task BEN comparison, regional BEN showed task specific effects in target regions.
Abstract. Unmanned Aircraft Systems (UAS) have been used in many military and civil applications, particularly surveillance. One of the best ways to use the capacity of a UAS imaging system is by constructing a mosaic of the recorded video. This paper presents a novel algorithm for the construction of superresolution mosaicking. The algorithm is based on the Levenberg Marquardt (LM) method. Hubert prior is used together with four different cliques to deal with the ill-conditioned inverse problem and to preserve edges. Furthermore, the Lagrange multiplier is compute without using sparse matrices. We present the results with synthetic and real UAS surveillance data, resulting in a great improvement of the visual resolution. For the case of synthetic images, we obtained a PSNR of 47.0 dB, as well as a significant increase in the details visible for the case of real UAS frames in only ten iterations.
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