2020
DOI: 10.1002/mp.13976
|View full text |Cite
|
Sign up to set email alerts
|

Multimodality image registration in the head‐and‐neck using a deep learning‐derived synthetic CT as a bridge

Abstract: Purpose To develop and demonstrate the efficacy of a novel head‐and‐neck multimodality image registration technique using deep‐learning‐based cross‐modality synthesis. Methods and Materials Twenty‐five head‐and‐neck patients received magnetic resonance (MR) and computed tomography (CT) (CTaligned) scans on the same day with the same immobilization. Fivefold cross validation was used with all of the MR‐CT pairs to train a neural network to generate synthetic CTs from MR images. Twenty‐four of 25 patients also h… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1
1
1
1

Citation Types

0
28
0

Year Published

2020
2020
2024
2024

Publication Types

Select...
6

Relationship

1
5

Authors

Journals

citations
Cited by 33 publications
(31 citation statements)
references
References 41 publications
0
28
0
Order By: Relevance
“…Also, commercial solutions start to be evaluated for the generation of DL‐based sCT from MRI for lesion detection of suspected sacroiliitis 223 or to facilitate surgical planning of the spine 224 . An exciting application is also the generation of sCT to facilitate multimodal image registration, as proposed by Mckenzie et al 225 …”
Section: Discussionmentioning
confidence: 99%
See 1 more Smart Citation
“…Also, commercial solutions start to be evaluated for the generation of DL‐based sCT from MRI for lesion detection of suspected sacroiliitis 223 or to facilitate surgical planning of the spine 224 . An exciting application is also the generation of sCT to facilitate multimodal image registration, as proposed by Mckenzie et al 225 …”
Section: Discussionmentioning
confidence: 99%
“…or to facilitate surgical planning of the spine224 . An exciting application is also the generation of sCT to facilitate multi-modal image registration, as proposed by Mckenzie et al225 .All the techniques of category I could be directly applied to MR-guided high-intensity focused ultrasound, where otherwise an additional CT would be required to properly plan the treatment226 .Additionally, the methods here reviewed to generate sCT can be applied to translating other image modalities. Interesting examples in the RT realm are provided by Jiang et al227 , who investigated using MRI-to-CT translation to increase the segmentation's robustness.Kieselmann et al 228 generated synthetic MRI from CT to train segmentation networks that exploit the wealth of delineation on another modality.…”
mentioning
confidence: 99%
“…10,11,16,17,72 MR-CT image registration neck, reducing an inter-modality registration problem to an intramodality one. 80 As summarized in Table 3, they found that, using the same deformable registration algorithm, the average landmark error decreased from 9.8 ± 3.1 mm in direct MR-CT registration to 6.0 ± 2.1 mm using synthetic CT as a bridge. Similar results were also reported in the inverse CT-MR registration task.…”
Section: Pet Attenuation Correctionmentioning
confidence: 91%
“…Direct registration between CT and MR images is very challenging due to disparate image contrast and is even less reliable in deformable registration wherein significant geometric distortion is allowed. McKenzie et al proposed a CycleGAN‐based method to synthetize CT images and used the synthetic CT to replace MRI in MR‐CT registration in the head and neck, reducing an inter‐modality registration problem to an intra‐modality one 80 . As summarized in Table 3, they found that, using the same deformable registration algorithm, the average landmark error decreased from 9.8 ± 3.1 mm in direct MR‐CT registration to 6.0 ± 2.1 mm using synthetic CT as a bridge.…”
Section: Application Areasmentioning
confidence: 99%
“…5, and to provide a reference CT for CT based IGRT [138][139][140][141][142]. Further, synthetic CTs have been generated from MRI using a cycleGAN for the purposes of providing a 'bridge' between MR and CT for the purposes of image registration [143].…”
Section: Treatment Planningmentioning
confidence: 99%