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Machine Learning in Radio Imaging
Published in Punit Gupta, Dinesh Kumar Saini, Rohit Verma, Healthcare Solutions Using Machine Learning and Informatics, 2023
Nitesh Pradhan, Punit Gupta, Anita Shrotriya
Dual-energy X-ray absorptiometry (DXA) images used to diagnose osteoporosis provide a T-score. A T-score value between +1 and −1 indicates healthy bone. A value between −1 and −2.5 shows that the bone has become prone to osteoporosis [2]. This state is called osteopenia. A value below 2.5 is an indication of the poor quality of a bone and a sign of osteoporosis. The decrease in bone mineral density (BMD) characteristic of osteoporosis increases the risk of bone fracture. In Europe, 30% of women over the age of 50 years suffer from osteoporosis [3]. According to a 2000 report, 3.1 to 3.7 million cases of osteoporosis were recorded, with a direct treatment cost of 32 billion dollars, a cost that could rise to 76.8 billion dollars per year in 2050 if this trend continues [4].
X-ray Vision: Diagnostic X-rays and CT Scans
Published in Suzanne Amador Kane, Boris A. Gelman, Introduction to Physics in Modern Medicine, 2020
Suzanne Amador Kane, Boris A. Gelman
Dual energy x-ray absorptiometry (DEXA or DXA) is the present “gold standard” for osteoporosis screening. DEXA uses collimated x-rays from an x-ray tube source and a scintillation detector positioned so it intercepts x-rays transmitted through the body section of interest, such as the hip, spine, or wrist. At each position, a reading yields the sum of transmitted x-ray intensity (and hence x-ray absorption) along one line through the body. This includes the effect of x-ray absorption from both bone and the surrounding soft tissues; however, the absorption due to soft tissue can be compensated for by comparing different beam paths that do (path A) and do not (path B) include bone.
Radiogrammetry
Published in C M Langton, C F Njeh, The Physical Measurement of Bone, 2016
Jonathan A Thorpe, Christian M Langton
For the time being at least, diagnosis of osteoporosis typically requires confirmation by dual energy X-ray absorptiometry (DXA), and thus there is a strong urge for clinicians or researchers to convert or compare the results of radiogrammetry with DXA-derived BMD. In so doing, the conversion procedure has to make certain assumptions about bone structure which may not hold true in reality. The advantages to be gained from a close, if not exact, analogy with bone mineral density are considerable, however, provided such comparisons are not given exaggerated importance.
In-situ measurements of engine particulate filter ash deposits via X-ray computed tomography scanning
Published in Aerosol Science and Technology, 2021
Yujun Wang, Ben Wang, Carl J. Kamp, Leigh Rogoski, Connor Ryan, Michael J. Cunningham
X-ray computed tomography (CT) is a mature nondestructive imaging tool that has been widely applied in many fields (Du Plessis, Le Roux, and Guelpa 2016; Cnudde and Boone 2013; Perret, Al-Belushi, and Deadman 2007). Based on thousands of scans at different rotational angles, the CT computes each voxel’s attenuation coefficient in the three-dimensional (3 D) space. In addition to examining the sample 3 D geometry and localized microcracks, the voxel grayscale can be used to measure the sample local density, which is crucial to evaluate the part quality, contamination, and performance in many applications. In the medical examination, dual energy X-ray absorptiometry (DXA) is applied to measure the bone mineral density and mass body composition through 2 D images, which guides undernutrition or overnutrition treatment (International Atomic and Energy Agency 2013). In comparison, industrial CTs have sufficient freedoms in parameter settings, spacious room for large size samples, and high energy to penetrate through the dense parts. In the previous studies, the industrial CTs have been applied to measure the density of timber (Kadas 2016) and food (Kelkar, Boushey, and Okos 2015) and demonstrated significant advantages over the traditional mechanical methods. However, it should be noted that the industrial CTs have a few critical limitations such as the unknown spectrum of source energy and detector sensitivity, inaccessible attenuation coefficient data, and unapparent physical meaning of the image gray-scale. Thus, the methodology of using the CT attenuation coefficient data to calculate the sample density and mean atomic number (Heismann, Leppert, and Stierstorfer 2003) cannot be directly applied in the industrial CTs. A special approach needs to be carefully developed for industrial CTs in the density measurements.
Prediction of proximal femur fracture risk from DXA images based on novel fracture indexes
Published in Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 2021
Said Zellagui, Audrey Hivet, Marouane El Mouss, Ridha Hambli
The current tool for the diagnosis of osteoporosis and fracture risk assessment is dual-energy X-ray absorptiometry (DXA), which measures areal bone mineral density (aBMD reported in g/cm2). Several studies have shown that BMD is an important determinant of bone resistance and that there is a strong relationship between BMD and the probability of fracture risk (Hui et al. 1989; Cummings et al. 1990, 1993b; Marshal et al. 1996; Johnell et al. 2005). They have also shown that the bone loss is more pronounced at the level of the femoral neck (Chevalley et al. 1991) and that it is the most sensitive indicator of hip fracture risk of the proximal femur (Chevalley et al. 1991; Aloia et al. 1992; Cummings et al. 1993b). Nevertheless, it has been shown that BMD alone cannot predict with certainty who will have a future hip fracture (Riggs et al. 1982; Norimatsu et al. 1989). The risk of hip fracture depends not only on the BMD, but also on the mechanical properties, geometry, and architecture of the bone. It is the result of these combined effects (Nakamura et al. 1994). Wainwright et al. (2005) demonstrated that half of individuals suffering a hip fracture are non-osteoporotic according to BMD testing. Recent studies to estimate bone resistance and hence to prevent hip fracture risk are based on finite element (FE) models (Lotz et al. 1991a, 1991b; Ford and Keaveny 1996; Ford et al. 1996; Ota et al. 1999; Pietruszczak et al. 1999; Keyak and Falkinstein 2003; Taddei et al. 2006; Schileo et al. 2008; Dragomir-Daescu et al. 2011; Hambli et al. 2012; Hambli and Allaoui 2013; Hambli 2014) and hip structural analysis (HSA) (Gnudi et al. 1999; Nelson et al. 2000; Looker et al. 2001; Beck et al. 2001a, 2001b; Crabtree et al. 2002; Duan et al. 2003; Kaptoge et al. 2003b, 2003c; Pulkkinen et al. 2004; Melton et al. 2005; Szulc et al. 2006; Rivadeneira et al. 2007). FE analysis may not be the most effective and efficient technique for clinical routine, and is still limited due to the requirement of expensive computer hardware to achieve solutions of FE models within a clinically acceptable time. Several 2D and 3D FE models, based, respectively, on DXA and computer tomography (CT) scans of the proximal femur have been proposed in the literature (Lotz et al. 1991; Keyak et al. 1998; Cody et al. 1999). These approaches are accurate in estimating proximal femur strength, but their clinical applications to assess fracture risk are limited because of the higher radiation dose and the higher costs associated with CT scanning. In addition, the need for 3D segmentation and a meshing technique make FE modelling limited in terms of time and computer resources (Aspray et al. 2009).