An adaptive motion regularization technique to support sliding motion in deformable image registration

Fu, Yabo; Shi, Lei; Li, H. Harold; Li, Hua; Yang, Deshan

doi:10.1002/mp.12734

Cited by 25 publications

(34 citation statements)

References 51 publications

(98 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Conventional DIR methods model the sliding motion by applying direction-dependent spatial filters repeatedly. 17,19,57 Since LungRegNet is a noniterative method, we plan to integrate biomechanical model into DVF regularization to model the sliding motion in the future. 61…”

Section: Discussionmentioning

confidence: 99%

“…On the contrary, many conventional intensity-based DIR methods have achieved such accuracy for many years. 17,19,[51][52][53] From this perspective, deep learning-based DIR methods have yet to outperform conventional DIR methods. Deep learning-based methods generally perform very well on classification tasks such as image classification and segmentation.…”

Section: Related Workmentioning

confidence: 99%

“…Kristin Higgins is consulting for Astra Zeneca, Varian, on advisory board for Genentech, and receiving research funding from RefleXion Medical. 57 Heinrich et al 51 Fu et al 19 Delmon et al 17 Vandemeulebroucke et al 16 Staring et al 52 Elastix+MI Gong et al 58 MIND Li et al 59…”

Section: Conflict Of Interestmentioning

confidence: 99%

“…Conventional methods usually apply spatial regularization repeatedly throughout the iteration process to smooth DVF, which may result in under-smoothed/oversmoothed DVF. 10,11,[15][16][17][18][19] Inaccurate DVF could result in unrealistically deformed images such as falsely deformed ribs or spine. 19 We have previously developed an unsupervised network for 4D-CT abdominal DIR.…”

Section: Introductionmentioning

confidence: 99%

“…10,11,[15][16][17][18][19] Inaccurate DVF could result in unrealistically deformed images such as falsely deformed ribs or spine. 19 We have previously developed an unsupervised network for 4D-CT abdominal DIR. 20 In this study, we have extended our previous work to 4D-CT lung DIR.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

LungRegNet: An unsupervised deformable image registration method for 4D‐CT lung

Lei

Wang

et al. 2020

Medical Physics

Self Cite

View full text Add to dashboard Cite

Purpose: To develop an accurate and fast deformable image registration (DIR) method for fourdimensional computed tomography (4D-CT) lung images. Deep learning-based methods have the potential to quickly predict the deformation vector field (DVF) in a few forward predictions. We have developed an unsupervised deep learning method for 4D-CT lung DIR with excellent performances in terms of registration accuracies, robustness, and computational speed. Methods: A fast and accurate 4D-CT lung DIR method, namely LungRegNet, was proposed using deep learning. LungRegNet consists of two subnetworks which are CoarseNet and FineNet. As the name suggests, CoarseNet predicts large lung motion on a coarse scale image while FineNet predicts local lung motion on a fine scale image. Both the CoarseNet and FineNet include a generator and a discriminator. The generator was trained to directly predict the DVF to deform the moving image. The discriminator was trained to distinguish the deformed images from the original images. Coarse-Net was first trained to deform the moving images. The deformed images were then used by the Fine-Net for FineNet training. To increase the registration accuracy of the LungRegNet, we generated vessel-enhanced images by generating pulmonary vasculature probability maps prior to the network prediction. Results: We performed fivefold cross validation on ten 4D-CT datasets from our department. To compare with other methods, we also tested our method using separate 10 DIRLAB datasets that provide 300 manual landmark pairs per case for target registration error (TRE) calculation. Our results suggested that LungRegNet has achieved better registration accuracy in terms of TRE than other deep learning-based methods available in the literature on DIRLAB datasets. Compared to conventional DIR methods, LungRegNet could generate comparable registration accuracy with TRE smaller than 2 mm. The integration of both the discriminator and pulmonary vessel enhancements into the network was crucial to obtain high registration accuracy for 4D-CT lung DIR. The mean and standard deviation of TRE were 1.00 AE 0.53 mm and 1.59 AE 1.58 mm on our datasets and DIRLAB datasets respectively. Conclusions: An unsupervised deep learning-based method has been developed to rapidly and accurately register 4D-CT lung images. LungRegNet has outperformed its deep-learning-based peers and achieved excellent registration accuracy in terms of TRE.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Conflict Of Interestmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

LungRegNet: An unsupervised deformable image registration method for 4D‐CT lung

Lei

Wang

et al. 2020

Medical Physics

Self Cite

View full text Add to dashboard Cite

show abstract

Enhanced super‐resolution reconstruction of T1w time‐resolved 4DMRI in low‐contrast tissue using 2‐step hybrid deformable image registration

Nie

Huang

Deasy

et al. 2020

J Applied Clin Med Phys

View full text Add to dashboard Cite

Purpose Deformable image registration (DIR) in low‐contrast tissues is often suboptimal because of low visibility of landmarks, low driving‐force to deform, and low penalty for misalignment. We aim to overcome the shortcomings for improved reconstruction of time‐resolved four‐dimensional magnetic resonance imaging (TR‐4DMRI). Methods and Materials Super‐resolution TR‐4DMRI reconstruction utilizes DIR to combine high‐resolution (highR:2x2x2mm 3 ) breath‐hold (BH) and low‐resolution (lowR:5x5x5mm 3 ) free‐breathing (FB) 3D cine (2Hz) images to achieve clinically acceptable spatiotemporal resolution. A 2‐step hybrid DIR approach was developed to segment low‐dynamic‐range (LDR) regions: low‐intensity lungs and high‐intensity “bodyshell” (=body‐lungs) for DIR refinement after conventional DIR. The intensity in LDR regions was renormalized to the full dynamic range (FDR) to enhance local tissue contrast. A T1‐mapped 4D XCAT digital phantom was created, and seven volunteers and five lung cancer patients were scanned with two BH and one 3D cine series per subject to compare the 1‐step conventional and 2‐step hybrid DIR using: (a) the ground truth in the phantom, (b) highR‐BH references, which were used to simulate 3D cine images by down‐sampling and Rayleigh‐noise‐adding, and (c) cross‐verification between two TR‐4DMRI images reconstructed from two BHs. To assess DIR improvement, 8‐17 blood vessel bifurcations were used in volunteers, and lung tumor position, size, and shape were used in phantom and patients, together with the voxel intensity correlation (VIC), structural similarity (SSIM), and cross‐consistency check (CCC). Results The 2‐step hybrid DIR improves contrast and DIR accuracy. In volunteers, it improves low‐contrast alignment from 6.5 ± 1.8 mm to 3.3 ± 1.0 mm. In phantom, it improves tumor center of mass alignment (COM = 1.3 ± 0.2 mm) and minimizes DIR directional difference. In patients, it produces almost‐identical tumor COM, size, and shape (dice> 0.85) as the reference. The VIC and SSIM are significantly increased and the number of CCC outliers are reduced by half. Conclusion The 2‐step hybrid DIR improves low‐contrast‐tissue alignment and increases lung tumor fidelity. It is recommended to adopt the 2‐step hybrid DIR for TR‐4DMRI reconstruction.

show abstract

Automatic large quantity landmark pairs detection in 4DCT lung images

Thomas

et al. 2019

Medical Physics

Self Cite

View full text Add to dashboard Cite

Purpose To automatically and precisely detect a large quantity of landmark pairs between two lung computed tomography (CT) images to support evaluation of deformable image registration (DIR). We expect that the generated landmark pairs will significantly augment the current lung CT benchmark datasets in both quantity and positional accuracy. Methods A large number of landmark pairs were detected within the lung between the end‐exhalation (EE) and end‐inhalation (EI) phases of the lung four‐dimensional computed tomography (4DCT) datasets. Thousands of landmarks were detected by applying the Harris‐Stephens corner detection algorithm on the probability maps of the lung vasculature tree. A parametric image registration method (pTVreg) was used to establish initial landmark correspondence by registering the images at EE and EI phases. A multi‐stream pseudo‐siamese (MSPS) network was then developed to further improve the landmark pair positional accuracy by directly predicting three‐dimensional (3D) shifts to optimally align the landmarks in EE to their counterparts in EI. Positional accuracies of the detected landmark pairs were evaluated using both digital phantoms and publicly available landmark pairs. Results Dense sets of landmark pairs were detected for 10 4DCT lung datasets, with an average of 1886 landmark pairs per case. The mean and standard deviation of target registration error (TRE) were 0.47 ± 0.45 mm with 98% of landmark pairs having a TRE smaller than 2 mm for 10 digital phantom cases. Tests using 300 manually labeled landmark pairs in 10 lung 4DCT benchmark datasets (DIRLAB) produced TRE results of 0.73 ± 0.53 mm with 97% of landmark pairs having a TRE smaller than 2 mm. Conclusion A new method was developed to automatically and precisely detect a large quantity of landmark pairs between lung CT image pairs. The detected landmark pairs could be used as benchmark datasets for more accurate and informative quantitative evaluation of DIR algorithms.

show abstract

An adaptive motion regularization technique to support sliding motion in deformable image registration

Cited by 25 publications

References 51 publications

LungRegNet: An unsupervised deformable image registration method for 4D‐CT lung

LungRegNet: An unsupervised deformable image registration method for 4D‐CT lung

Enhanced super‐resolution reconstruction of T1w time‐resolved 4DMRI in low‐contrast tissue using 2‐step hybrid deformable image registration

Automatic large quantity landmark pairs detection in 4DCT lung images

Contact Info

Product

Resources

About