Adaptive Graph Diffusion Regularisation for Discontinuity Preserving Image Registration

Sandkühler, Robin; Jud, Christoph; Pezold, Simon; Cattin, Philippe C.

doi:10.1007/978-3-319-92258-4_3

Cited by 18 publications

(17 citation statements)

References 18 publications

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“…The error was quantified as mean deformation field after 2D registration was performed using the same registration method as in Sec. 2.1 [11]. The median error lies below 1 mm and the maximum error below 3 mm for all datasets and both methods.…”

Section: Training Detailsmentioning

confidence: 73%

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Respiratory Motion Modelling Using cGANs

Giger

Sandkühler

Jud

et al. 2018

Medical Image Computing and Computer Assisted Intervention – MICCAI 2018

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Respiratory motion models in radiotherapy are considered as one possible approach for tracking mobile tumours in the thorax and abdomen with the goal to ensure target coverage and dose conformation. We present a patient-specific motion modelling approach which combines navigator-based 4D MRI with recent developments in deformable image registration and deep neural networks. The proposed regression model based on conditional generative adversarial nets (cGANs) is trained to learn the relation between temporally related US and MR navigator images. Prior to treatment, simultaneous ultrasound (US) and 4D MRI data is acquired. During dose delivery, online US imaging is used as surrogate to predict complete 3D MR volumes of different respiration states ahead of time. Experimental validations on three volunteer lung datasets demonstrate the potential of the proposed model both in terms of qualitative and quantitative results, and computational time required.

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Section: Training Detailsmentioning

confidence: 73%

“…For further details on 4D MRI, we refer to [12]. Unlike [12], deformable image registration of the navigator slices is performed using the approach proposed in [11], which was specifically developed for mask-free lung image registration.…”

Section: Data Acquisition and Image Registrationmentioning

confidence: 99%

Respiratory Motion Modelling Using cGANs

Giger

Sandkühler

Jud

et al. 2018

Medical Image Computing and Computer Assisted Intervention – MICCAI 2018

Self Cite

View full text Add to dashboard Cite

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“…For the interphase robustness assessment, each resulting phase-specific FM was spatially aligned with the reference V NM image by first using affine registration 37 to align each 4DCT phase image (on which the FM was calculated) and the SPECT attenuation correction CT image. The resulting affine transformation was then applied to the FMs.…”

Section: Methodsmentioning

confidence: 99%

Respiratory Invariant Textures From Static Computed Tomography Scans for Explainable Lung Function Characterization

Huang

Teng

Zhang

et al. 2023

Journal of Thoracic Imaging

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Purpose: The inherent characteristics of lung tissue independent of breathing maneuvers may provide fundamental information for function assessment. This paper attempted to correlate textural signatures from computed tomography (CT) with pulmonary function measurements. Materials and Methods: Twenty-one lung cancer patients with thoracic 4-dimensional CT, DTPA-single-photon emission CT ventilation (V NM) scans, and available spirometry measurements (forced expiratory volume in 1 s, FEV1; forced vital capacity, FVC; and FEV1/FVC) were collected. In subregional feature discovery, function-correlated candidates were identified from 79 radiomic features based on the statistical strength to differentiate defected/nondefected lung regions. Feature maps (FMs) of selected candidates were generated on 4-dimensional CT phases for a voxel-wise feature distribution study. Quantitative metrics were applied for validations, including the Spearman correlation coefficient (SCC) and the Dice similarity coefficient for FM-V NM spatial agreement assessments, intraclass correlation coefficient for FM interphase robustness evaluations, and FM-spirometry comparisons. Results: At the subregion level, 8 function-correlated features were identified (effect size>0.330). The FMs of candidates yielded moderate-to-strong voxel-wise correlations with the reference V NM. The FMs of gray level dependence matrix dependence nonuniformity showed the highest robust (intraclass correlation coefficient=0.96 and P<0.0001) spatial correlation, with median SCCs ranging from 0.54 to 0.59 throughout the 10 breathing phases. Its phase-averaged FM achieved a median SCC of 0.60, a median Dice similarity coefficient of 0.60 (0.65) for high (low) functional lung volumes, and a correlation of 0.565 (0.646) between the spatially averaged feature values and FEV1 (FEV1/FVC). Conclusions: The results provide further insight into the underlying association of specific pulmonary textures with both local (V NM) and global (FEV1/FVC, FEV1) functions. Further validations of the FM generalizability and the standardization of implementation protocols are warranted before clinically relevant investigations.

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“…An algorithm that aligns lung structures while maintaining ventilation and perfusion signal modulations 27 was applied to register the 2D image time series to an image in the midrespiratory state (baseline image). Subsequently, fractional ventilation and relative perfusion maps were obtained from the registered time‐series using the MP method (Figure 1A,B ).…”

Section: Methodsmentioning

confidence: 99%