Development of a new Python-based cardiac phantom for myocardial SPECT imaging

Hanafy, Osama S; Khalil, Magdy M.; Khater, Ibrahim; Mohammed, Haitham S.

doi:10.1007/s12149-020-01534-y

Cited by 2 publications

(4 citation statements)

References 23 publications

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“…These are more easily adaptable for changes in the anatomy-pathology of the relevant structures, see Table 2 in Supplementary material. For image acquisition optimization purposes, the combination of computational human phantoms with the simulation of the imaging procedure can be a time- and cost-efficient tool [ 7 – 17 ].…”

Section: Introductionmentioning

confidence: 99%

“…Existing geometrical models of selected cardiac structures are based on a mathematical ellipsoidal model [ 17 ] or image segmentations [ 10 , 13 , 14 , 16 ]. Advanced models such as the XCAT phantom have been extended over the years so that the anatomical model covers the thorax and provides a detailed heart model.…”

Section: Introductionmentioning

confidence: 99%

“…Similar to motion simulation, image generation can be based on observations or physics-based simulation models. Hanafy et al used observations of image data in combination with a Poisson noise model for the simulation of cardiac SPECT images [ 17 ], and Gilbert et al employed a CycleGAN to generate synthetic echocardiographic images based on machine learning [ 16 ]. The observation-based approaches are very successful in the generation of realistic-looking images, which consider the typical artifacts and properties of real images.…”

Section: Introductionmentioning

confidence: 99%

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A simulation-based phantom model for generating synthetic mitral valve image data–application to MRI acquisition planning

Manini,

Nemchyna,

Akansel

et al. 2023

Int J CARS

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Purpose Numerical phantom methods are widely used in the development of medical imaging methods. They enable quantitative evaluation and direct comparison with controlled and known ground truth information. Cardiac magnetic resonance has the potential for a comprehensive evaluation of the mitral valve (MV). The goal of this work is the development of a numerical simulation framework that supports the investigation of MRI imaging strategies for the mitral valve. Methods We present a pipeline for synthetic image generation based on the combination of individual anatomical 3D models with a position-based dynamics simulation of the mitral valve closure. The corresponding images are generated using modality-specific intensity models and spatiotemporal sampling concepts. We test the applicability in the context of MRI imaging strategies for the assessment of the mitral valve. Synthetic images are generated with different strategies regarding image orientation (SAX and rLAX) and spatial sampling density. Results The suitability of the imaging strategy is evaluated by comparing MV segmentations against ground truth annotations. The generated synthetic images were compared to ones acquired with similar parameters, and the result is promising. The quantitative analysis of annotation results suggests that the rLAX sampling strategy is preferable for MV assessment, reaching accuracy values that are comparable to or even outperform literature values. Conclusion The proposed approach provides a valuable tool for the evaluation and optimization of cardiac valve image acquisition. Its application to the use case identifies the radial image sampling strategy as the most suitable for MV assessment through MRI.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A simulation-based phantom model for generating synthetic mitral valve image data–application to MRI acquisition planning

Manini,

Nemchyna,

Akansel

et al. 2023

Int J CARS

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“…The use of cardiac DPs, both static and dynamic, for the assessment of imaging modalities has accelerated over the last decade [6], along with similarly conceived cardiac atlases [7,8], to which we return below. Here, we present a static DP that our group has created for the assessment of derived haemodynamic modalities.…”

Section: Introductionmentioning

confidence: 99%

The Use of Digital Coronary Phantoms for the Validation of Arterial Geometry Reconstruction and Computation of Virtual FFR

Pederzani

Czechowicz

Ghorab

et al. 2022

Fluids

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We present computational fluid dynamics (CFD) results of virtual fractional flow reserve (vFFR) calculations, performed on reconstructed arterial geometries derived from a digital phantom (DP). The latter provides a convenient and parsimonious description of the main vessels of the left and right coronary arterial trees, which, crucially, is CFD-compatible. Using our DP, we investigate the reconstruction error in what we deem to be the most relevant way—by evaluating the change in the computed value of vFFR, which results from varying (within representative clinical bounds) the selection of the virtual angiogram pair (defined by their viewing angles) used to segment the artery, the eccentricity and severity of the stenosis, and thereby, the CFD simulation’s luminal boundary. The DP is used to quantify reconstruction and computed haemodynamic error within the VIRTUheartTM software suite. However, our method and the associated digital phantom tool are readily transferable to equivalent, clinically oriented workflows. While we are able to conclude that error within the VIRTUheartTM workflow is suitably controlled, the principal outcomes of the work reported here are the demonstration and provision of a practical tool along with an exemplar methodology for evaluating error in a coronary segmentation process.

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Development of a new Python-based cardiac phantom for myocardial SPECT imaging

Cited by 2 publications

References 23 publications

A simulation-based phantom model for generating synthetic mitral valve image data–application to MRI acquisition planning

A simulation-based phantom model for generating synthetic mitral valve image data–application to MRI acquisition planning

The Use of Digital Coronary Phantoms for the Validation of Arterial Geometry Reconstruction and Computation of Virtual FFR

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