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Background: The Australian Imaging, Biomarkers and Lifestyle (AIBL) Study commenced in 2006 as a prospective study of 1,112 individuals (768 cognitively normal (CN), 133 with mild cognitive impairment (MCI), and 211 with Alzheimer’s disease dementia (AD)) as an ‘Inception cohort’ who underwent detailed ssessments every 18 months. Over the past decade, an additional 1247 subjects have been added as an ‘Enrichment cohort’ (as of 10 April 2019). Objective: Here we provide an overview of these Inception and Enrichment cohorts of more than 8,500 person-years of investigation. Methods: Participants underwent reassessment every 18 months including comprehensive cognitive testing, neuroimaging (magnetic resonance imaging, MRI; positron emission tomography, PET), biofluid biomarkers and lifestyle evaluations. Results: AIBL has made major contributions to the understanding of the natural history of AD, with cognitive and biological definitions of its three major stages: preclinical, prodromal and clinical. Early deployment of Aβ-amyloid and tau molecular PET imaging and the development of more sensitive and specific blood tests have facilitated the assessment of genetic and environmental factors which affect age at onset and rates of progression. Conclusion: This fifteen-year study provides a large database of highly characterized individuals with longitudinal cognitive, imaging and lifestyle data and biofluid collections, to aid in the development of interventions to delay onset, prevent or treat AD. Harmonization with similar large longitudinal cohort studies is underway to further these aims.

Oxidative stress has been implicated in Alzheimer’s disease, and it is potentially driven by the depletion of primary antioxidant, glutathione, as well as elevation of the pro-oxidant, iron. Present study evaluates glutathione level by magnetic resonance spectroscopy, iron deposition by quantitative susceptibility mapping in left hippocampus, as well as the neuropsychological scores of healthy old participants (N = 25), mild cognitive impairment (N = 16) and Alzheimer’s disease patients (N = 31). Glutathione was found to be significantly depleted in mild cognitive impaired (P

Dysfunction of the cholinergic basal forebrain (BF) neurotransmitter system, including cholinergic axon denervation of the cortex, plays an important role in cognitive decline and dementia. A validated method to directly quantify cortical cholinergic terminal integrity enables exploration of the involvement of this system in diverse cognitive profiles associated with dementia, particularly at a prodromal stage. In this study, we used the radiotracer [

Background: One of the spine deformities is scoliosis, and Cobb angle is generally used to assess it. Objectives: In this study, a computer-aided measurement system (CAMS) was presented as a new repeatable and reproducible approach to assess the Cobb angle in idiopathic scoliosis patients. Methods: Python libraries, including OpenCV and Numpy were used for image processing and design of the software. To assess the repeatability and reproducibility of the CAMS, a series of 98 anterior-posterior radiographs from patients with idiopathic scoliosis were used. Assessments were done by five independent observers. Each radiograph was assessed by each observer three times with a minimum break of two weeks among assessment. The single measure intraclass correlation coefficient (ICC), the mean absolute difference (MAD), and the standard error measurement (SEM) values were used for intra- and inter-observer reliability. Results: The inter-observer analysis indicated that the ICCs ranged from 0.94 - 0.99, and the MAD between manual and CAMS were less than 3°. For intra-observer measurements, the combined SEM between all observers for the manual and CAMS was 1.79° and 1.27°, respectively. An ICC value of 0.97 with 95% confidence interval (CI) was excellent in CAMS for inter-observer reliability. The MAD of CAMS was 2.18 ± 2.01 degrees. Conclusions: The CAMS is an effective and reliable approach for assessing scoliotic curvature in the standing radiographs of thoraco-lumbar. Moreover, CAMS can accelerate clinical visits, and its calculation results are reliable.

Quantitative susceptibility mapping (QSM) is an MRI post-processing technique that produces spatially resolved magnetic susceptibility maps from phase data. However, the traditional QSM reconstruction pipeline involves multiple non-trivial steps, including phase unwrapping, background field removal, and dipole inversion. These intermediate steps not only increase the reconstruction time but accumulates errors. This study aims to overcome existing limitations by developing a Laplacian-of-Trigonometric-functions (LoT) enhanced deep neural network for near-instant quantitative field and susceptibility mapping (i.e., iQFM and iQSM) from raw MRI phase data. The proposed iQFM and iQSM methods were compared with established reconstruction pipelines on simulated and in vivo datasets. In addition, experiments on patients with intracranial hemorrhage and multiple sclerosis were also performed to test the generalization of the proposed neural networks. The proposed iQFM and iQSM methods in healthy subjects yielded comparable results to those involving the intermediate steps while dramatically improving reconstruction accuracies on intracranial hemorrhages with large susceptibilities. High susceptibility contrast between multiple sclerosis lesions and healthy tissue was also achieved using the proposed methods. Comparative studies indicated that the most significant contributor to iQFM and iQSM over conventional multi-step methods was the elimination of traditional Laplacian unwrapping. The reconstruction time on the order of minutes for traditional approaches was shortened to around 0.1 s using the trained iQFM and iQSM neural networks.

β-Amyloid pathology is elevated in ~30% of cognitively normal people over 65, and is associated with accelerated neurodegeneration in the pre-clinical stages of Alzheimer's disease. Recent findings reveal that brain iron might also act to propel neurodegeneration in people with underlying amyloid pathology. Here, repeated PET scans of fluorodeoxyglucose (FDG) were used as a biomarker for brain hypometabolism and a downstream biomarker of neurodegeneration to investigate whether levels of ferritin in the cerebrospinal fluid (CSF; a reporter of brain iron load) are associated with prodromal disease progression of people with high β-amyloid pathology determined by established cut-off values in CSF t-tau/Aβ42 ratio. Nineteen cognitively normal participants with low t-tau/Aβ42, and 71 participants with high t-tau/Aβ42 who were cognitively normal or had mild cognitive impairment were included as participants from the Alzheimer's Disease Neuroimaging Initiative (ADNI) study. These subjects had repeated FDG-PET scans at 6-month intervals for 2 years, and yearly intervals for up to a further 3 years. In mixed-effects linear models of FDG signal, baseline CSF ferritin was associated with an accelerated decline in FDG PET in high t-tau/Aβ42 participants (β[SE] = -0.066 [0.017]; P = .0002), but not in people with low t-tau/Aβ42 (-0.029 [0.049]; P = .554). These data implicate iron as a contributing factor to neurodegeneration associated with β-amyloid pathology, and highlight CSF ferritin as a complementary prognostic biomarker to the t-tau/Aβ42 ratio that predicts near-term risk for disease progression.

Highlights • Detailed protocol of the Prospective Imaging Study of Ageing (PISA) Study. • Genetic risk prediction to identify those at differing risk of Alzheimer’s disease. • Longitudinal cohort for the study of precursors and lifestyle risk factors. • Use of online surveys and cognitive testing for large scale phenotyping. • Functional, structural and molecular neuroimaging with neurocognitive testing., This prospective cohort study, “Prospective Imaging Study of Ageing: Genes, Brain and Behaviour” (PISA) seeks to characterise the phenotype and natural history of healthy adult Australians at high future risk of Alzheimer’s disease (AD). In particular, we are recruiting midlife and older Australians with high and low genetic risk of dementia to discover biological markers of early neuropathology, identify modifiable risk factors, and establish the very earliest phenotypic and neuronal signs of disease onset. PISA utilises genetic prediction to recruit and enrich a prospective cohort and follow them longitudinally. Online surveys and cognitive testing are used to characterise an Australia-wide sample currently totalling over 3800 participants. Participants from a defined at-risk cohort and positive controls (clinical cohort of patients with mild cognitive impairment or early AD) are invited for onsite visits for detailed functional, structural and molecular neuroimaging, lifestyle monitoring, detailed neurocognitive testing, plus blood sample donation. This paper describes recruitment of the PISA cohort, study methodology and baseline demographics.

Background Cortical iron accumulation has been reported as a pathological feature of Alzheimer's disease (AD). The cause of cortical iron elevation in AD is unknown but may be contributed by hemosiderin deposits in cerebral microbleeds that frequently occur in this disease. Objective To investigate the impact of cerebral microbleeds (which are more frequent in AD) on the magnetic susceptibility of the surrounding brain tissue. Methods 32 MRI scans from the Australian Imaging, Biomarker and Lifestyle (AIBL) study were found to have cerebral microbleeds by manual assessment of susceptibility weighted images. Quantitative susceptibility mapping (QSM; an MRI technique that is sensitive to iron) was used to estimate iron content in the tissue surrounding the microbleed in four concentric radii. Furthermore, the mirror regions on the contralateral hemisphere were also demarcated. A simulation analysis was conducted to investigate the effect of QSM imaging on cerebral microbleeds with varying sizes. Results 77 microbleeds were identified from the available scans. The immediate proximal region to the cerebral microbleeds had enhanced tissue susceptibility (∼0.02 PPM), but importantly, this did not extend beyond one voxel radius. This finding with in vivo data was also replicated in a simulation study. However, the presence of microbleeds could lead to over-estimation of tissue QSM in unsupervised quantification, therefore processing methods to avoid this artefact without the need for their manual identification are proposed. Conclusion The local changes in susceptibility due to microbleeds outside the focal lesion are restricted to 1 voxel and may be explained by partial voluming artefacts caused by limited imaging resolution. The susceptibly change induced by the microbleed is a relatively small proportion of tissue and could not account for regional iron changes observed in AD cortex.

This prospective cohort study, “Prospective Imaging Study of Ageing: Genes, Brain and Behaviour” (PISA) seeks to characterise the phenotype and natural history of healthy adult Australians at high future risk of Alzheimer’s disease (AD). In particular, we are recruiting mid-life Australians with high and low genetic risk of dementia to discover biological markers of early neuropathology, identify modifiable risk factors, and establish the very earliest phenotypic and neuronal signs of disease onset. PISA utilises genetic prediction to recruit and enrich a prospective cohort and follow them longitudinally. Online surveys and cognitive testing are used to characterise an Australia-wide sample currently totalling nearly 3,000 participants. Participants from a defined at-risk cohort and positive controls (clinical cohort of patients with mild cognitive impairment or early AD) are invited for onsite visits for lifestyle monitoring, detailed neurocognitive testing, blood sample donation, plus functional, structural and molecular neuroimaging. This paper describes recruitment of the PISA cohort, study methodology and baseline demographics.Author ApprovalAll authors have seen and approved this manuscript.

Background: Scoliosis is a three-dimensional deformity of the spine that is commonly assessed through measuring the Cobb angle. Objectives: In this study, a Cobb angle measurement decision support system (CaMDSS) was presented to provide a repeatable and reproducible procedure for Cobb angle measurement in idiopathic scoliosis patients. Methods: We used the OpenCV and the Numpy library for image processing and system design. A series of 98 anterior-posterior radiographs from patients diagnosed with idiopathic scoliosis were used to assess the repeatability and reproducibility of CaMDSS. Five independent observers performed the measurements, and each image was analyzed by each observer three times with a minimum interval of two weeks between measurements. Both the intra- and inter-observer reliability were obtained using the single measure intraclass correlation coefficient (ICC) value. The mean absolute difference (MAD) and the standard error measurement (SEM) were calculated for all corresponding intra- and inter-observer reliability estimates. Results: Statistical results for the inter-observer analysis showed that the MAD between manual and CaMDSS was less than 3o, and the ICCs ranged from 0.94 to 0.99. The combined SEM between all five observers for intra-observer measurements of the manual method and CaMDSS was 1.79o and 1.27o, respectively. The inter-observer reliability of CaMDSS was excellent as the ICC value of 0.97 with 95% CI was obtained. The CaMDSS mean absolute difference was 2.18 ± 2.01 degrees. Conclusion: Our study showed CaMDSS was an efficient and reliable method to assess the scoliotic curvature in Thoraco-Lumbar standing radiographs with the possibility of expediting clinic visits, ensuring the reliability of calculation, and decreasing the patient’s exposure to radiation.

Background and Objective Cerebral microbleeds (CMB) are important biomarkers of cerebrovascular diseases and cognitive dysfunctions. Susceptibility weighted imaging (SWI) is a common MRI sequence where CMB appear as small hypointense blobs. The prevalence of CMB in the population and in each scan is low, resulting in tedious and time-consuming visual assessment. Automated detection methods would be of value but are challenged by the CMB low prevalence, the presence of mimics such as blood vessels, and the difficulty to obtain sufficient ground truth for training and testing. In this paper, synthetic CMB (sCMB) generation using an analytical model is proposed for training and testing machine learning methods. The main aim is creating perfect synthetic ground truth as similar as reals, in high number, with a high diversity of shape, volume, intensity, and location to improve training of supervised methods. Method sCMB were modelled with a random Gaussian shape and added to healthy brain locations. We compared training on our synthetic data to standard augmentation techniques. We performed a validation experiment using sCMB and report result for whole brain detection using a 10-fold cross validation design with an ensemble of 10 neural networks. Results Performance was close to state of the art (~9 false positives per scan), when random forest was trained on synthetic only and tested on real lesion. Other experiments showed that top detection performance could be achieved when training on synthetic CMB only. Our dataset is made available, including a version with 37,000 synthetic lesions, that could be used for benchmarking and training. Conclusion Our proposed synthetic microbleeds model is a powerful data augmentation approach for CMB classification with and should be considered for training automated lesion detection system from MRI SWI.

See Derry and Kent (doi:10.1093/awx167) for a scientific commentary on this article.The large variance in cognitive deterioration in subjects who test positive for amyloid-β by positron emission tomography indicates that convergent pathologies, such as iron accumulation, might combine with amyloid-β to accelerate Alzheimer's disease progression. Here, we applied quantitative susceptibility mapping, a relatively new magnetic resonance imaging method sensitive to tissue iron, to assess the relationship between iron, amyloid-β load, and cognitive decline in 117 subjects who underwent baseline magnetic resonance imaging and amyloid-β positron emission tomography from the Australian Imaging, Biomarkers and Lifestyle study (AIBL). Cognitive function data were collected every 18 months for up to 6 years from 100 volunteers who were either cognitively normal (n = 64) or diagnosed with mild cognitive impairment (n = 17) or Alzheimer's disease (n = 19). Among participants with amyloid pathology (n = 45), higher hippocampal quantitative susceptibility mapping levels predicted accelerated deterioration in composite cognition tests for episodic memory [β(standard error) = -0.169 (0.034), P = 9.2 × 10-7], executive function [β(standard error) = -0.139 (0.048), P = 0.004), and attention [β(standard error) = -0.074 (0.029), P = 0.012]. Deteriorating performance in a composite of language tests was predicted by higher quantitative susceptibility mapping levels in temporal lobe [β(standard error) = -0.104 (0.05), P = 0.036] and frontal lobe [β(standard error) = -0.154 (0.055), P = 0.006]. These findings indicate that brain iron might combine with amyloid-β to accelerate clinical progression and that quantitative susceptibility mapping could be used in combination with amyloid-β positron emission tomography to stratify individuals at risk of decline.

Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disease defined by the presence of upper and lower motor neuron dysfunction. Clinical presentation and disease progression are variable between patients with ALS, with psychosocial, neuropsychological, nutritional and genetic factors all related to ALS outcome. We present a detailed characterisation of a patient with familial ALS with a novel TBK1 mutation with coexistent brain atrophy, behavioural changes and metabolic dysfunction. This male patient was first seen at age 62 years with a 3-month history of gait unsteadiness, weakness of the right arm and emotional lability. There was a history of depression. His father had died of ALS at age 67 years. At first assessment, there was weakness of the right upper limb, ALS Functional Rating Scale-Revised (ALSFRS-R) was 39 and his weight was 72 kg. After 6 months, there was onset of dysphagia. He had tongue fasciculations, brisk jaw jerk, weakness, wasting, brisk reflexes and fasciculations of upper and lower limbs. The ALSFRS-R was 34 and weight had further declined to 65 kg. Three months later, he had deteriorated further with severe weakness, nausea, anorexia and depression. ALSFRS-R was 25 and weight was 54 kg. His respiratory function declined from FVC of 4.57 L to 1.05 L over 6 months. He died ~16 months after the onset of symptoms. The patient participated in a series of neuropsychological, imaging and metabolic studies of unselected consenting patients with ALS enrolled from our clinic.1 Neuropsychological assessment results for this patient are presented in online supplementary table 1. Comparison of his individual results with those of other patients with ALS is presented in online supplementary tables 2 to 4. Genetic assessment was done by massively parallel sequencing using a custom-designed neuromuscular gene capture panel that tests for 66 genes known to be associated …

Quantitative susceptibility mapping (QSM) reveals pathological changes in widespread diseases such as Parkinson’s disease, Multiple Sclerosis, or hepatic iron overload. QSM requires multiple processing steps after the acquisition of magnetic resonance imaging (MRI) phase measurements such as unwrapping, background field removal and the solution of an ill-posed field-to-source-inversion. Current techniques utilize iterative optimization procedures to solve the inversion and background field correction, which are computationally expensive and lead to suboptimal or over-regularized solutions requiring a careful choice of parameters that make a clinical application of QSM challenging. We have previously demonstrated that a deep convolutional neural network can invert the magnetic dipole kernel with a very efficient feed forward multiplication not requiring iterative optimization or the choice of regularization parameters. In this work, we extended this approach to remove background fields in QSM. The prototype method, called SHARQnet, was trained on simulated background fields and tested on 3T and 7T brain datasets. We show that SHARQnet outperforms current background field removal procedures and generalizes to a wide range of input data without requiring any parameter adjustments. In summary, we demonstrate that the solution of ill-posed problems in QSM can be achieved by learning the underlying physics causing the artifacts and removing them in an efficient and reliable manner and thereby will help to bring QSM towards clinical applications.

BACKGROUND: Arterial spin labeling (ASL) is an emerging MRI technique for noninvasive measurement of cerebral blood flow (CBF) that has been used to show hemodynamic changes in the brains of people with Alzheimer's disease (AD). CBF changes have been measured using positron emission tomography (PET) across the AD spectrum, but ASL showed limited success in measuring CBF variations in the preclinical phase of AD, where amyloid β (Aβ) plaques accumulate in the decades prior to symptom onset. PURPOSE: To investigate the relationship between CBF measured by multiphase-pseudocontinuous-ASL (MP-PCASL) and Aβ burden as measured by 11 C-PiB PET imaging in a study of cognitively normal (CN) subjects age over 65. STUDY TYPE: Cross-sectional. POPULATION: Forty-six CN subjects including 33 with low levels of Aβ burden and 13 with high levels of Aβ. FIELD STRENGTH/SEQUENCE: 3T/3D MP-PCASL. ASSESSMENT: The MP-PCASL method was chosen because it has a high signal-to-noise ratio. Furthermore, the data were analyzed using an efficient processing pipeline consisting of motion correction, ASL motion correction imprecision removal, temporal and spatial filtering, and partial volume effect correction. STATISTICAL TESTS: General Linear Model. RESULTS: In CN subjects positive for Aβ burden (n = 13), we observed a positive correlation between CBF and Aβ burden in the hippocampus, amygdala, caudate (P

Biomarkers are not well understood in depression, partly because there is no golden rule of what is abnormal in which patients and how neurobiological information can be used to improve diagnosis. The heterogeneity of depression suggests that diverse circuit-level abnormalities in individuals lead to various symptoms. Investigating heterogeneous depression is crucial to understand disease mechanisms and provide personalised medicine. Dynamical functional connectivity (dFC), consisting of spatial-temporal characteristics of brain activity, has been shown to be effective in characterizing the circuit-level abnormalities in depression. However, most of the current studies on dFC are based on one-step mapping while ignoring hierarchical spatial-temporal information, which may leverage the power of diagnosis in depression subtypes. In this study, we propose Brain Network Gated Recurrent Units (BrainNetGRU) to discover hierarchical resting-state dFC features for the diagnosis of depression subtypes, using data from 770 depressive adults from the UK Biobank. Particularly, we devise diffusion convolutional filters and recurrent units to effectively learn distinctive dynamic brain connectivity for depression subtypes. Experimental results show that BrainNetGRU can identify three types of depression with an accuracy of 72.05%. In addition, BrainNetGRU shows that resting-state functional connections in default mode network (DMN), cingulo-opercular network (CON) and fronto-parietal network (FPN) are important in the diagnosis of depression subtypes.

Purpose: The feasibility of radiation therapy treatment planning using substitute computed tomography (sCT) generated from magnetic resonance images (MRIs) has been demonstrated by a number of research groups. One challenge with an MRI-alone workflow is the accurate identification of intraprostatic gold fiducial markers, which are frequently used for prostate localization prior to each dose delivery fraction. This paper investigates a template-matching approach for the detection of these seeds in MRI. Methods: Two different gradient echo T1 and T2* weighted MRI sequences were acquired from fifteen prostate cancer patients and evaluated for seed detection. For training, seed templates from manual contours were selected in a spectralclusteringmanifold learning framework. This aids in clustering “similar” gold fiducial markers together. The marker with the minimum distance to a cluster centroid was selected as the representative template of that cluster during training. During testing, Gaussian mixture modeling followed by a Markovian model was used in automatic detection of the probable candidates. The probable candidates were rigidly registered to the templates identified from spectralclustering, and a similarity metric is computed for ranking and detection. Results: A fiducial detection accuracy of 95% was obtained compared to manual observations. Expert radiation therapist observers were able to correctly identify all three implanted seeds on 11 of the 15 scans (the proposed method correctly identified all seeds on 10 of the 15). Conclusions: An novel automatic framework for gold fiducial marker detection in MRI is proposed and evaluated with detection accuracies comparable to manual detection. When radiation therapists are unable to determine the seed location in MRI, they refer back to the planning CT (only available in the existing clinical framework); similarly, an automatic quality control is built into the automatic software to ensure that all gold seeds are either correctly detected or a warning is raised for further manual intervention.

The quantitative use of medical images often requires an intensity scaling with respect to the signal from a well-characterized anatomical region of interest. The choice of such a region often varies between studies which can substantially influence the quantification, resulting in study bias hampering objective findings which are detrimental to open science. This study outlines a list of criteria and a statistical ranking approach for identifying normalization region of interest. The proposed criteria include (i) associations between reference region and demographics such as age, (ii) diagnostic group differences in the reference region, (iii) correlation between reference and primary areas of interest, (iv) local variance in the reference region, and (v) longitudinal reproducibility of the target regions when normalized. The proposed approach has been used to establish an optimal normalization region of interest for the analysis of Quantitative Susceptibility Mapping (QSM) of Magnetic Resonance Imaging (MRI). This was achieved by using cross-sectional data from 119 subjects with normal cognition, mild cognitive impairment, and Alzheimer’s disease as well as and 19 healthy elderly individuals with longitudinal data. For the QSM application, we found that normalizing by the white matter regions not only satisfies the criteria but it also provides the best separation between clinical groups for deep brain nuclei target regions.

Arterial spin labeling (ASL) is an emerging MRI technique for non-invasive measurement of cerebral blood flow (CBF). Compared to invasive perfusion imaging modalities, ASL suffers from low sensitivity due to poor signal-to-noise ratio (SNR), susceptibility to motion artifacts and low spatial resolution, all of which limit its reliability. In this work, the effects of various state of the art image processing techniques for addressing these ASL limitations are investigated. A processing pipeline consisting of motion correction, ASL motion correction imprecision removal, temporal and spatial filtering, partial volume effect correction, and CBF quantification was developed and assessed. To further improve the SNR for pseudo-continuous ASL (PCASL) by accounting for errors in tagging efficiency, the data from multiphase (MP) acquisitions were analyzed using a novel weighted-averaging scheme. The performances of each step in terms of SNR and reproducibility were evaluated using test-retest ASL data acquired from 12 young healthy subjects. The proposed processing pipeline was shown to improve the within-subject coefficient of variation and regional reproducibility by 17% and 16%, respectively, compared to CBF maps computed following motion correction but without the other processing steps. The CBF measurements of MP-PCASL compared to PCASL had on average 23% and 10% higher SNR and reproducibility, respectively.

Quantitative medical imaging often utilizes intensity normalization based on a signal from a neighboring region. The choice of this region can substantially affect the quantification, and is potentially confounded by the presence of pathology or other limitations such as partial volume effect. In this paper we outline the desirable list of criteria for selecting a normalization region of interest, and utilize this approach for quantitative susceptibility mapping (QSM) MRI in a study of neurodegeneration. The proposed criteria includes (i) association between reference region and demographics such as age, (ii) diagnostic group separation effect in the reference region, (iii) correlation between reference and target regions, (iv) local variance in the reference region, and (v) reduced cross-sectional variance within the diagnostic groups using the reference region. The intensity normalization was then evaluated using 119 subjects with normal cognition, mild cognitive impairment and Alzheimer's disease. For QSM applications in ageing we found that normalizing by the white matter regions not only satisfies the criteria but it also provides the best separation between clinical groups in the brain nuclei regions.

This paper proposes the use of Ultra-Wide Band (UWB) technology to detect falls in smart home environment. A bi-static setup is proposed in which the UWB sensor including both transmitter and receiver is mounted over the ceiling to monitor and detect falls among other types of inertial movements such as slow/fast walking and lying down. The experimental results show that the proposed approach can monitor and detect falls efficiently through a real-time analyses of the streaming data generated by the UWB sensor. It also demonstrates that the ambient UWB sensor can be used as a promising fall detection solution in monitoring certain areas of high risks of falls in smart home environments.