A momentum-based diffeomorphic demons framework for deformable MR-CT image registration. (24th October 2018)

• Record Type: Journal Article
• Authors: Han, R; De Silva, T; Ketcha, M; Uneri, A; Siewerdsen, J H
• Is Part Of: Physics in medicine & biology. Volume 63:Number 21(2018:Nov.)
• Publication Date: 2018-10-24
• Subjects: image registration -- neurosurgery navigation -- deformable registration; Biophysics -- Periodicals; Medical physics -- Periodicals; 610.153
• DOI: 10.1088/1361-6560/aae66c
• Languages: English
• ISSNs: 0031-9155

Abstract

Abstract: Neuro-navigated procedures require a high degree of geometric accuracy but are subject to geometric error from complex deformation in the deep brain—e.g. regions about the ventricles due to egress of cerebrospinal fluid (CSF) upon neuroendoscopic approach or placement of a ventricular shunt. We report a multi-modality, diffeomorphic, deformable registration method using momentum-based acceleration of the Demons algorithm to solve the transformation relating preoperative MRI and intraoperative CT as a basis for high-precision guidance. The registration method (pMI-Demons) extends the mono-modality, diffeomorphic form of the Demons algorithm to multi-modality registration using pointwise mutual information (pMI) as a similarity metric. The method incorporates a preprocessing step to nonlinearly stretch CT image values and incorporates a momentum-based approach to accelerate convergence. Registration performance was evaluated in phantom and patient images: first, the sensitivity of performance to algorithm parameter selection (including update and displacement field smoothing, histogram stretch, and the momentum term) was analyzed in a phantom study over a range of simulated deformations; and second, the algorithm was applied to registration of MR and CT images for four patients undergoing minimally invasive neurosurgery. Performance was compared to two previously reported methods (free-form deformation using mutual information (MI-FFD) and symmetric normalization using mutual information (MI-SyN)) in terms of target registration error (TRE), Jacobian determinant ( J ), and runtime. The phantom study identified optimal or nominal settings of algorithm parameters for translation to clinical studies. In the phantom study, the pMI-Demons method achieved comparable registration accuracy to the reference methods and strongly reduced outliers in TRE ( p 0.001 in Kolmogorov–Smirnov test). Similarly, in the clinical study: median TRE = 1.54 mm (0.83–1.66 mm interquartile range, IQR) for pMI-Demons compared to 1.40 mm (1.02–1.67 mm IQR) for MI-FFD and 1.64 mm (0.90–1.92 mm IQR) for MI-SyN. The pMI-Demons and MI-SyN methods yielded diffeomorphic transformations ( J > 0) that preserved topology, whereas MI-FFD yielded unrealistic ( J < 0) deformations subject to tissue folding and tearing. Momentum-based acceleration gave a ~35% speedup of the pMI-Demons method, providing registration runtime of 10.5 min (reduced to 2.2 min on GPU), compared to 15.5 min for MI-FFD and 34.7 min for MI-SyN. The pMI-Demons method achieved registration accuracy comparable to MI-FFD and MI-SyN, maintained diffeomorphic transformation similar to MI-SyN, and accelerated runtime in a manner that facilitates translation to image-guided neurosurgery.