Cargando…

Dynamic cone-beam CT reconstruction using spatial and temporal implicit neural representation learning (STINR)

Objective. Dynamic cone-beam CT (CBCT) imaging is highly desired in image-guided radiation therapy to provide volumetric images with high spatial and temporal resolutions to enable applications including tumor motion tracking/prediction and intra-delivery dose calculation/accumulation. However, dyna...

Descripción completa

Detalles Bibliográficos
Autores principales: Zhang, You, Shao, Hua-Chieh, Pan, Tinsu, Mengke, Tielige
Formato: Online Artículo Texto
Lenguaje:English
Publicado: IOP Publishing 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10087494/
https://www.ncbi.nlm.nih.gov/pubmed/36638543
http://dx.doi.org/10.1088/1361-6560/acb30d
_version_ 1785022360632426496
author Zhang, You
Shao, Hua-Chieh
Pan, Tinsu
Mengke, Tielige
author_facet Zhang, You
Shao, Hua-Chieh
Pan, Tinsu
Mengke, Tielige
author_sort Zhang, You
collection PubMed
description Objective. Dynamic cone-beam CT (CBCT) imaging is highly desired in image-guided radiation therapy to provide volumetric images with high spatial and temporal resolutions to enable applications including tumor motion tracking/prediction and intra-delivery dose calculation/accumulation. However, dynamic CBCT reconstruction is a substantially challenging spatiotemporal inverse problem, due to the extremely limited projection sample available for each CBCT reconstruction (one projection for one CBCT volume). Approach. We developed a simultaneous spatial and temporal implicit neural representation (STINR) method for dynamic CBCT reconstruction. STINR mapped the unknown image and the evolution of its motion into spatial and temporal multi-layer perceptrons (MLPs), and iteratively optimized the neuron weightings of the MLPs via acquired projections to represent the dynamic CBCT series. In addition to the MLPs, we also introduced prior knowledge, in the form of principal component analysis (PCA)-based patient-specific motion models, to reduce the complexity of the temporal mapping to address the ill-conditioned dynamic CBCT reconstruction problem. We used the extended-cardiac-torso (XCAT) phantom and a patient 4D-CBCT dataset to simulate different lung motion scenarios to evaluate STINR. The scenarios contain motion variations including motion baseline shifts, motion amplitude/frequency variations, and motion non-periodicity. The XCAT scenarios also contain inter-scan anatomical variations including tumor shrinkage and tumor position change. Main results. STINR shows consistently higher image reconstruction and motion tracking accuracy than a traditional PCA-based method and a polynomial-fitting-based neural representation method. STINR tracks the lung target to an average center-of-mass error of 1–2 mm, with corresponding relative errors of reconstructed dynamic CBCTs around 10%. Significance. STINR offers a general framework allowing accurate dynamic CBCT reconstruction for image-guided radiotherapy. It is a one-shot learning method that does not rely on pre-training and is not susceptible to generalizability issues. It also allows natural super-resolution. It can be readily applied to other imaging modalities as well.
format Online
Article
Text
id pubmed-10087494
institution National Center for Biotechnology Information
language English
publishDate 2023
publisher IOP Publishing
record_format MEDLINE/PubMed
spelling pubmed-100874942023-08-16 Dynamic cone-beam CT reconstruction using spatial and temporal implicit neural representation learning (STINR) Zhang, You Shao, Hua-Chieh Pan, Tinsu Mengke, Tielige Phys Med Biol Paper Objective. Dynamic cone-beam CT (CBCT) imaging is highly desired in image-guided radiation therapy to provide volumetric images with high spatial and temporal resolutions to enable applications including tumor motion tracking/prediction and intra-delivery dose calculation/accumulation. However, dynamic CBCT reconstruction is a substantially challenging spatiotemporal inverse problem, due to the extremely limited projection sample available for each CBCT reconstruction (one projection for one CBCT volume). Approach. We developed a simultaneous spatial and temporal implicit neural representation (STINR) method for dynamic CBCT reconstruction. STINR mapped the unknown image and the evolution of its motion into spatial and temporal multi-layer perceptrons (MLPs), and iteratively optimized the neuron weightings of the MLPs via acquired projections to represent the dynamic CBCT series. In addition to the MLPs, we also introduced prior knowledge, in the form of principal component analysis (PCA)-based patient-specific motion models, to reduce the complexity of the temporal mapping to address the ill-conditioned dynamic CBCT reconstruction problem. We used the extended-cardiac-torso (XCAT) phantom and a patient 4D-CBCT dataset to simulate different lung motion scenarios to evaluate STINR. The scenarios contain motion variations including motion baseline shifts, motion amplitude/frequency variations, and motion non-periodicity. The XCAT scenarios also contain inter-scan anatomical variations including tumor shrinkage and tumor position change. Main results. STINR shows consistently higher image reconstruction and motion tracking accuracy than a traditional PCA-based method and a polynomial-fitting-based neural representation method. STINR tracks the lung target to an average center-of-mass error of 1–2 mm, with corresponding relative errors of reconstructed dynamic CBCTs around 10%. Significance. STINR offers a general framework allowing accurate dynamic CBCT reconstruction for image-guided radiotherapy. It is a one-shot learning method that does not rely on pre-training and is not susceptible to generalizability issues. It also allows natural super-resolution. It can be readily applied to other imaging modalities as well. IOP Publishing 2023-02-21 2023-02-06 /pmc/articles/PMC10087494/ /pubmed/36638543 http://dx.doi.org/10.1088/1361-6560/acb30d Text en © 2023 The Author(s). Published on behalf of Institute of Physics and Engineering in Medicine by IOP Publishing Ltd https://creativecommons.org/licenses/by/4.0/ Original content from this work may be used under the terms of the Creative Commons Attribution 4.0 licence (https://creativecommons.org/licenses/by/4.0/) . Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.
spellingShingle Paper
Zhang, You
Shao, Hua-Chieh
Pan, Tinsu
Mengke, Tielige
Dynamic cone-beam CT reconstruction using spatial and temporal implicit neural representation learning (STINR)
title Dynamic cone-beam CT reconstruction using spatial and temporal implicit neural representation learning (STINR)
title_full Dynamic cone-beam CT reconstruction using spatial and temporal implicit neural representation learning (STINR)
title_fullStr Dynamic cone-beam CT reconstruction using spatial and temporal implicit neural representation learning (STINR)
title_full_unstemmed Dynamic cone-beam CT reconstruction using spatial and temporal implicit neural representation learning (STINR)
title_short Dynamic cone-beam CT reconstruction using spatial and temporal implicit neural representation learning (STINR)
title_sort dynamic cone-beam ct reconstruction using spatial and temporal implicit neural representation learning (stinr)
topic Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10087494/
https://www.ncbi.nlm.nih.gov/pubmed/36638543
http://dx.doi.org/10.1088/1361-6560/acb30d
work_keys_str_mv AT zhangyou dynamicconebeamctreconstructionusingspatialandtemporalimplicitneuralrepresentationlearningstinr
AT shaohuachieh dynamicconebeamctreconstructionusingspatialandtemporalimplicitneuralrepresentationlearningstinr
AT pantinsu dynamicconebeamctreconstructionusingspatialandtemporalimplicitneuralrepresentationlearningstinr
AT mengketielige dynamicconebeamctreconstructionusingspatialandtemporalimplicitneuralrepresentationlearningstinr