2021
DOI: 10.1002/mp.15272
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Slot‐scan dual‐energy bone densitometry using motorized X‐ray systems

Abstract: Purpose We investigate the feasibility of slot‐scan dual‐energy (DE) bone densitometry on motorized radiographic equipment. This approach will enable fast quantitative measurements of areal bone mineral density (aBMD) for opportunistic evaluation of osteoporosis. Methods We investigated DE slot‐scan protocols to obtain aBMD measurements at the lumbar spine (L‐spine) and hip using a motorized x‐ray platform capable of synchronized translation of the x‐ray source and flat‐panel detector (FPD). The slot dimension… Show more

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Cited by 5 publications
(5 citation statements)
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References 64 publications
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“…In PDD, precomputed lookup tables 21 (LUTs) were used to convert pairs of (LE, HE) detector pixel values into pairs of (Al, PE) line integrals. The LUTs were generated by applying a polyenergetic forward model to estimate LE and HE pixel intensities for Al path lengths ranging −10–100 mm and PE path lengths ranging −10–300 mm (negative line integrals were included to account for noise).…”
Section: Methodsmentioning
confidence: 99%
See 1 more Smart Citation
“…In PDD, precomputed lookup tables 21 (LUTs) were used to convert pairs of (LE, HE) detector pixel values into pairs of (Al, PE) line integrals. The LUTs were generated by applying a polyenergetic forward model to estimate LE and HE pixel intensities for Al path lengths ranging −10–100 mm and PE path lengths ranging −10–300 mm (negative line integrals were included to account for noise).…”
Section: Methodsmentioning
confidence: 99%
“…9,10 This work investigates whether the BME imaging capability of DE CT can be translated onto the emerging cone-beam CT (CBCT) systems for musculoskeletal (MSK) imaging 12-16-f or example, the dedicated extremity CBCT [17][18][19] or robotic x-ray devices such as Multitom Rax (Siemens Healthineers, Forchheim, Germany). 15,[20][21][22][23] MSK CBCT offers advanced 3D imaging capabilities for orthopedic settings that traditionally relied on 2D radiography, including novel diagnostic features like weight-bearing scans [24][25][26] and high-resolution evaluation of fine bone detail (e.g., trabecular bone 27 or subtle fractures). The addition of BME detection could help establish MSK CBCT as a platform for comprehensive bone health assessment.…”
Section: Introductionmentioning
confidence: 99%
“…Projection domain decomposition (PDD) solves equation (1) directly by precomputing intensities (or attenuations) for each material thickness, storing them in materialspecific intensity-to-thickness (or attenuation-to-thickness) look-up tables and using bilinear interpolation to estimate the material thicknesses. 2,7 Alternatively, an optimization-based approach derived from model-based material decomposition can be used. 8 Assuming a monoenergetic beam at the effective energy E ef f of the "detected" spectrum the forward model described by equation 1 can be simplified to…”
Section: Methodsmentioning
confidence: 99%
“…To obtain 𝑆 𝐶𝐵𝐶𝑇 , a kernel-based scatter estimation method was developed, based on Zhao et al 5 . To account for the distinct distribution and frequency properties of low-angle and high-angle scatter in the ultra-compact geometry of the stationary scanner, the total scatter was obtained as the weighted sum of two kernels with accounting for high-and low-angle scatter, following:…”
Section: Adaptive Kernel-based Scatter Estimation In Non-circular Sta...mentioning
confidence: 99%
“…Projection-based approaches to scatter compensation remove the need for a complete volume and generate scatter estimates directly from projection data, either by application of deep neural networks, as in deep scatter estimation (DSE) 4 , or via iterative convolution of a "scatter potential" kernel. 5,6 The derivation of convolution kernels or DSE parameters, however, invokes assumptions of stationarity of the source-detector geometry across projection views. Therefore, application to non-circular geometry with variable source-detector pose would require a source-and detector pixel-dependent kernel derivation, drastically increasing the problem dimensionality.…”
Section: Introductionmentioning
confidence: 99%