Organ doses in pediatric patients undergoing cardiac‐centered fluoroscopically guided interventions: Comparison of three methods for computational phantom alignment

Marshall, Emily L.; Borrego, David; Fudge, James C.; Rajderkar, Dhanashree; Bolch, Wesley E.

doi:10.1002/mp.13044

Cited by 7 publications

(7 citation statements)

References 32 publications

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“…6(a)), as proposed by Omar et al (2016) [69]. The same concept was labelled as the "Table alignment" method by Marshall et al (2018a) [105]. (b) Target-centric refers to a technique where the specific coordinates of the target organ (usually the mid-point of the organ) are measured and used as the reference point, as defined by Omar et al (2016) [69].…”

Section: Patient-phantom and Phantom-beam Alignmentmentioning

confidence: 99%

“…(b) Target-centric refers to a technique where the specific coordinates of the target organ (usually the mid-point of the organ) are measured and used as the reference point, as defined by Omar et al (2016) [69]. The same concept was labelled as "Geometric centre alignment" as Marshall et al (2018a) [105]. The main motivation of this technique is for procedures that are organcentred such as cardiac catheterisation.…”

Section: Patient-phantom and Phantom-beam Alignmentmentioning

confidence: 99%

“…This approach has demonstrated better agreement in alignment compared to the head-centric approach [69]. (c) Image-based reference alignment refers to a ground truth method where one or more landmarks in the clinical images (both anterior-posterior and lateral views) are manually located, matched with corresponding landmarks in the computational phantom, which enables accurate alignment of the phantom to the isocentre of the X-ray beam in the MC system [105].…”

Section: Patient-phantom and Phantom-beam Alignmentmentioning

confidence: 99%

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Monte Carlo-based patient internal dosimetry in fluoroscopy-guided interventional procedures: A review

2021

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This systematic review aims to understand the dose estimation approaches and their major challenges. Specifically, we focused on state-of-the-art Monte Carlo (MC) methods in fluoroscopy-guided interventional procedures. Methods: All relevant studies were identified through keyword searches in electronic databases from inception until September 2020. The searched publications were reviewed, categorised and analysed based on their respective methodology.Results: Hundred and one publications were identified which utilised existing MC-based applications/programs or customised MC simulations. Two outstanding challenges were identified that contribute to uncertainties in the virtual simulation reconstruction. The first challenge involves the use of anatomical models to represent individuals. Currently, phantom libraries best balance the needs of clinical practicality with those of specificity. However, mismatches of anatomical variations including body size and organ shape can create significant discrepancies in dose estimations. The second challenge is that the exact positioning of the patient relative to the beam is generally unknown. Most dose prediction models assume the patient is located centrally on the examination couch, which can lead to significant errors. Conclusion:The continuing rise of computing power suggests a near future where MC methods become practical for routine clinical dosimetry. Dynamic, deformable phantoms help to improve patient specificity, but at present are only limited to adjustment of gross body volume. Dynamic internal organ displacement or reshaping is likely the next logical frontier. Image-based alignment is probably the most promising solution to enable this, but it must be automated to be clinically practical.

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Section: Patient-phantom and Phantom-beam Alignmentmentioning

confidence: 99%

Section: Patient-phantom and Phantom-beam Alignmentmentioning

confidence: 99%

Section: Patient-phantom and Phantom-beam Alignmentmentioning

confidence: 99%

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Monte Carlo-based patient internal dosimetry in fluoroscopy-guided interventional procedures: A review

2021

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“…Panorama semelhante é encontrado nos EUA, que apresenta anualmente cerca de 40.000 recém-nascidos com defeitos cardíacos congênitos críticos, que requerem procedimentos intervencionistas no primeiro ano de vida. Estes procedimentos de tratamento normalmente podem evitar que as crianças sejam submetidas à cirurgia convencional (MARSHALL et al, 2018).…”

Section: Capítulo 1: Introdução E Objetivosunclassified

“…Vários estudos foram realizados para investigar as doses para pacientes e, principalmente, para os profissionais que realizam rotineiramente procedimentos de RI. Além dos estudos experimentais, na literatura existe um grande número de artigos envolvendo simulação de Monte Carlo, abordando apenas a dosimetria de pacientes adultos (KIM et al, 2012;SANTOS et al, 2015;SANTOS et al, 2018;WILDGRUBER et al, 2018;HUO et al, 2018;BORREGO et al, 2017;SZUMSKA et al, 2016;KOUKORAVA et al, 2014;JOHNSON et al, 2009;BOZKURT e BOR, 2007;SIISKONEN et al, 2007) e, poucos estudos envolvendo pacientes pediátricos (SAYED et al, 2012;MARSHALL et al, 2018;HARBRON et al, 2016;YAKOUMAKIS et al, 2013;SCHULTZ et al, 2013). Diferentemente desses estudos descritos na literatura, no presente trabalho foram avaliadas as exposições ocupacionais e médicas, considerando o médico e o paciente pediátrico no cenário computacional, usando a simulação de Monte Carlo.…”

Section: Capítulo 1: Introdução E Objetivosunclassified

Avaliação da exposição médica e ocupacional em procedimento cardíaco de radiologia intervencionista pediátrica utilizando modelagem computacional

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Agradeço a Deus pelo dom da vida, por me permitir sonhar e por estar sempre comigo dando discernimento e sabedoria para realizar meus sonhos.Agradeço a minha orientadora, Prof. Dra. Ana Paula Perini, pela oportunidade de realizar este trabalho, pelos ensinamentos, paciência, orientação e, sobretudo, por ser uma mulher inspiradora na vida e na ciência.Ao professor e amigo Prof. Dr. Lucio Pereira Neves por suas valiosas contribuições e partilha de conhecimento.Á Universidade Federal de Uberlândia e ao PPGEB pelo o apoio técnico. Aos professores do PPGEB, especialmente aos professores Dr. Alcimar Barbosa Soares e a Dra. Selma Terezinha Milagre pela oportunidade de tê-los como porta para aprimorar e adquirir mais conhecimento na minha carreira científica.A Edson, secretário do PPGEB, pelos favores prestados. Aos meus pais Maria de Lourdes e Gracino Pereira in memorian por todo amor, por serem meus primeiros professores, e por serem minha base, fonte de inspiração e motivação para seguir em direção aos meus objetivos.Aos meus filhos Luís Marcelo e Luísa Sophia, pela compreensão em todos os momentos, pelo o nosso amor recíproco e incondicional e, sobretudo, por serem minha dose diária de motivação e foco na construção do nosso futuro.Agradeço a minha filha de 4 patas Yunni, por sempre me trazer paz e companhia. Aos meus irmãos Lázaro Tauan, Eliana, Elieide e Elizete por fazerem parte da construção da minha história e por estarem sempre na torcida por meu sucesso.Aos meus cunhados Marcos, Cássia, Norma, Manoel Lemos, Eduardo e Tatiana pelo companheirismo e incentivo.Aos meus sobrinhos Cleondson,

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Automatic localization of anatomical landmarks in head cine fluoroscopy images via deep learning

Fum,

Md Shah,

Raja Aman

et al. 2024

Medical Physics

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BackgroundFluoroscopy guided interventions (FGIs) pose a risk of prolonged radiation exposure; personalized patient dosimetry is necessary to improve patient safety during these procedures. However, current FGIs systems do not capture the precise exposure regions of the patient, making it challenging to perform patient‐procedure‐specific dosimetry. Thus, there is a pressing need to develop approaches to extract and use this information to enable personalized radiation dosimetry for interventional procedures.PurposeTo propose a deep learning (DL) approach for the automatic localization of 3D anatomical landmarks on randomly collimated and magnified 2D head fluoroscopy images.Materials and methodsThe model was developed with datasets comprising 800 000 pseudo 2D synthetic images (mixture of vessel‐enhanced and non‐enhancement), each with 55 annotated anatomical landmarks (two are landmarks for eye lenses), generated from 135 retrospectively collected head computed tomography (CT) volumetric data. Before training, dynamic random cropping was performed to mimic the varied field‐size collimation in FGI procedures. Gaussian‐distributed additive noise was applied to each individual image to enhance the robustness of the DL model in handling image degradation that may occur during clinical image acquisition in a clinical environment. The model was trained with 629 370 synthetic images for approximately 275 000 iterations and evaluated against a synthetic image test set and a clinical fluoroscopy test set.ResultsThe model shows good performance in estimating in‐ and out‐of‐image landmark positions and shows feasibility to instantiate the skull shape. The model successfully detected 96.4% and 92.5% 2D and 3D landmarks, respectively, within a 10 mm error on synthetic test images. It demonstrated an average of 3.6 ± 2.3 mm mean radial error and successfully detected 96.8% 2D landmarks within 10 mm error on clinical fluoroscopy images.ConclusionOur deep‐learning model successfully localizes anatomical landmarks and estimates the gross shape of skull structures from collimated 2D projection views. This method may help identify the exposure region required for patient‐specific organ dosimetry in FGIs procedures.

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Organ doses in pediatric patients undergoing cardiac‐centered fluoroscopically guided interventions: Comparison of three methods for computational phantom alignment

Cited by 7 publications

References 32 publications

Monte Carlo-based patient internal dosimetry in fluoroscopy-guided interventional procedures: A review

Monte Carlo-based patient internal dosimetry in fluoroscopy-guided interventional procedures: A review

Avaliação da exposição médica e ocupacional em procedimento cardíaco de radiologia intervencionista pediátrica utilizando modelagem computacional

Automatic localization of anatomical landmarks in head cine fluoroscopy images via deep learning

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