Model-based PSF and MTF estimation and validation from skeletal clinical CT images

Pakdel, Amirreza; Mainprize, James G.; Robert, Normand; Fialkov, Jeffrey A.; Whyne, Cari

doi:10.1118/1.4835515

Cited by 16 publications

(17 citation statements)

References 34 publications

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“…Different modelling approaches will be likely necessary to further improve the results. Among them, in the authors' perspective, some promising ones are multiscale models to determine bone properties (Vaughan et al, 2012), and imageprocessing algorithms that de-blur CT images (Treece et al, 2012;Pakdel et al, 2014) or identify bone anisotropy (Larsson et al, 2014). Nonetheless, this shortcoming of our results does not impair the application of our models to clinical studies, as already done by Keyak et al (2011) with a model based on Keyak et al (2005) that did not reach a X¼Y relationship.…”

Section: Discussioncontrasting

confidence: 47%

To what extent can linear finite element models of human femora predict failure under stance and fall loading configurations?

Schileo

Balistreri

Grassi

et al. 2014

Journal of Biomechanics

131

110

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Section: Discussioncontrasting

confidence: 47%

To what extent can linear finite element models of human femora predict failure under stance and fall loading configurations?

Schileo

Balistreri

Grassi

et al. 2014

Journal of Biomechanics

131

110

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“…The 3D PSF were determined for each of the CT data sets and utilized within an iterative deconvolution algorithm to deblur the images in order to restore the geometrical details and intensity of high-contrast structures (bone) (Pakdel et al, 2012(Pakdel et al, , 2014. This process has been demonstrated to improve both geometry and CT intensity in the CMFS.…”

Section: Deblurring Of Ct Images With Point-spread Function (Psf)-estmentioning

confidence: 99%

“…Recent work has described the use of deblurring algorithms to reconstruct geometry and intensity values in CT data of skeletal structures (Pakdel et al, 2012(Pakdel et al, , 2014. Deconvolution using a point spread function (PSF) has been shown to yield significant improvements in 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66…”

Section: Introductionmentioning

confidence: 99%

High resolution bone material property assignment yields robust subject specific finite element models of complex thin bone structures

Pakdel

Fialkov

Whyne

2016

Journal of Biomechanics

Self Cite

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Accurate finite element (FE) modeling of complex skeletal anatomy requires high resolution in both meshing and the heterogeneous mapping of material properties onto the generated mesh. This study introduces Node-based elastic Modulus Assignment with Partial-volume correction (NMAP) as a new approach for FE material property assignment to thin bone structures. The NMAP approach incorporates point spread function based deblurring of CT images, partial-volume correction of CT image voxel intensities and anisotropic interpolation and mapping of CT intensity assignment to FE mesh nodes. The NMAP procedure combined with a derived craniomaxillo-facial skeleton (CMFS) specific density-isotropic elastic modulus relationship was applied to produce specimen-specific FE models of 6 cadaveric heads. The NMAP procedure successfully generated models of the complex thin bone structures with surface elastic moduli reflective of cortical bone material properties. The specimen-specific CMFS FE models were able to accurately predict experimental strains measured under in vitro temporalis and masseter muscle loading (r=0.93, slope=1.01, n=5). The strength of this correlation represents a robust validation for CMFS FE modeling that can be used to better understand load transfer in this complex musculoskeletal system. The developed methodology offers a systematic process-flow able to address the complexity of the CMFS that can be further applied to create high-fidelity models of any musculoskeletal anatomy.

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“…Further, such images have different spatial resolution conditions. Because, the PSF and SSP are dependent on the scanner itself and selected scan/reconstruction parameters [34]. Therefore, PSF-based method which used images of the same scanner [17] [24] is more appropriate to assess the performance dependence on size and density of a lung cancer CT screening CAD system.…”

Section: Discussionmentioning

confidence: 99%

Feasible Method to Assess the Performance of a Lung Cancer CT Screening CAD System in Clinical Practice: Dependence on Nodule Size and Density

Marasinghe¹,

Ohkubo²,

Kobayashi³

et al. 2014

IJMPCERO

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Detection of small pulmonary nodules is the goal of lung cancer screening. Computer-aided detection (CAD) systems are recommended to use in lung cancer computed tomography (CT) screening to increase the accuracy of nodule detection. Size and density of lung nodules are primary factors in determining the risk of malignancy. Therefore, purpose of this study is to apply computer-simulated virtual nodules based on the point spread function (PSF) measured in same scanner (maintaining spatial resolution condition) to assess the CAD system performance dependence on nodule size and density. Virtual nodules with density differences between lung background and nodule density (∆CT) values (200, 300 and 400 HU) and different sizes (4 to 8 mm) were generated and fused on clinical images. CAD detection was performed and free-response receiver operating characteristic (FROC) curves were obtained. Results show that both density and size of virtual nodules can affect detection efficiency. Detailed results are possible to use for quantitative analysis of a CAD system performance. This study suggests that PSF-based virtual nodules could be effectively used to assess the lung cancer CT screening CAD system performance dependence on nodule size and density.

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Model-based PSF and MTF estimation and validation from skeletal clinical CT images

Cited by 16 publications

References 34 publications

To what extent can linear finite element models of human femora predict failure under stance and fall loading configurations?

To what extent can linear finite element models of human femora predict failure under stance and fall loading configurations?

High resolution bone material property assignment yields robust subject specific finite element models of complex thin bone structures

Feasible Method to Assess the Performance of a Lung Cancer CT Screening CAD System in Clinical Practice: Dependence on Nodule Size and Density

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