Brain-shift compensation by non-rigid registration of intra-operative ultrasound images with preoperative MR images based on residual complexity

Farnia, Parastoo; Ahmadian, Alireza; Shabanian, Touraj; Serej, Nasim Dadashi; Alirezaie, Javad

doi:10.1007/s11548-014-1098-5

Cited by 26 publications

(15 citation statements)

References 35 publications

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“…Registration algorithms in this field of ultrasound and MRI fusion can be categorized in non-deformable (Coupe et al, 2012; Prada et al, 2015a; Presles et al, 2014; Schneider et al, 2012) or deformable (Farnia et al, 2015; Laurence et al, 2013; Reinertsen et al, 2014; Rivaz et al, 2015; Rivaz et al, 2014b; Rivaz et al, 2014a; Rivaz and Collins, 2015a, Rivaz and Collins, 2015b) approaches that further split into feature-based and intensity-based methods. Feature-based methods find corresponding points or structures in both modalities and use correspondences to conclude the registration transformation (Modersitzki, 2003).…”

Section: Introductionmentioning

confidence: 99%

Automatic and efficient MRI-US segmentations for improving intraoperative image fusion in image-guided neurosurgery

Nitsch

Klein

Dammann

et al. 2019

NeuroImage: Clinical

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Knowledge of the exact tumor location and structures at risk in its vicinity are crucial for neurosurgical interventions. Neuronavigation systems support navigation within the patient's brain, based on preoperative MRI (preMRI). However, increasing tissue deformation during the course of tumor resection reduces navigation accuracy based on preMRI. Intraoperative ultrasound (iUS) is therefore used as real-time intraoperative imaging. Registration of preMRI and iUS remains a challenge due to different or varying contrasts in iUS and preMRI. Here, we present an automatic and efficient segmentation of B-mode US images to support the registration process. The falx cerebri and the tentorium cerebelli were identified as examples for central cerebral structures and their segmentations can serve as guiding frame for multi-modal image registration. Segmentations of the falx and tentorium were performed with an average Dice coefficient of 0.74 and an average Hausdorff distance of 12.2 mm. The subsequent registration incorporates these segmentations and increases accuracy, robustness and speed of the overall registration process compared to purely intensity-based registration. For validation an expert manually located corresponding landmarks. Our approach reduces the initial mean Target Registration Error from 16.9 mm to 3.8 mm using our intensity-based registration and to 2.2 mm with our combined segmentation and registration approach. The intensity-based registration reduced the maximum initial TRE from 19.4 mm to 5.6 mm, with the approach incorporating segmentations this is reduced to 3.0 mm. Mean volumetric intensity-based registration of preMRI and iUS took 40.5 s, including segmentations 12.0 s.

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Section: Introductionmentioning

confidence: 99%

Automatic and efficient MRI-US segmentations for improving intraoperative image fusion in image-guided neurosurgery

Nitsch

Klein

Dammann

et al. 2019

NeuroImage: Clinical

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“…Farnia et al have recently described brain shift compensation in a series of three articles ( 10 – 12 ) through matching of echogenic structures, specifically sulci, and optimization of the residual complexity value in the wavelet domain, a strategy to balance between feature and intensity-based registration approach advantages in multi-modal registration. With the introduction of the method in 2015, they validated the novel approach on both phantom and the BITE datasets, demonstrating a noted robustness to noise which is commonly encountered in iUS imaging.…”

Section: Resultsmentioning

confidence: 99%

Brain Shift in Neuronavigation of Brain Tumors: An Updated Review of Intra-Operative Ultrasound Applications

et al. 2021

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Neuronavigation using pre-operative imaging data for neurosurgical guidance is a ubiquitous tool for the planning and resection of oncologic brain disease. These systems are rendered unreliable when brain shift invalidates the patient-image registration. Our previous review in 2015, Brain shift in neuronavigation of brain tumours: A review offered a new taxonomy, classification system, and a historical perspective on the causes, measurement, and pre- and intra-operative compensation of this phenomenon. Here we present an updated review using the same taxonomy and framework, focused on the developments of intra-operative ultrasound-based brain shift research from 2015 to the present (2020). The review was performed using PubMed to identify articles since 2015 with the specific words and phrases: “Brain shift” AND “Ultrasound”. Since 2015, the rate of publication of intra-operative ultrasound based articles in the context of brain shift has increased from 2–3 per year to 8–10 per year. This efficient and low-cost technology and increasing comfort among clinicians and researchers have allowed unique avenues of development. Since 2015, there has been a trend towards more mathematical advancements in the field which is often validated on publicly available datasets from early intra-operative ultrasound research, and may not give a just representation to the intra-operative imaging landscape in modern image-guided neurosurgery. Focus on vessel-based registration and virtual and augmented reality paradigms have seen traction, offering new perspectives to overcome some of the different pitfalls of ultrasound based technologies. Unfortunately, clinical adaptation and evaluation has not seen as significant of a publication boost. Brain shift continues to be a highly prevalent pitfall in maintaining accuracy throughout oncologic neurosurgical intervention and continues to be an area of active research. Intra-operative ultrasound continues to show promise as an effective, efficient, and low-cost solution for intra-operative accuracy management. A major drawback of the current research landscape is that mathematical tool validation based on retrospective data outpaces prospective clinical evaluations decreasing the strength of the evidence. The need for newer and more publicly available clinical datasets will be instrumental in more reliable validation of these methods that reflect the modern intra-operative imaging in these procedures.

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“…One of the NL-means filter is Optimized Bayesian Nonlocal Means (OBNLM). According to Coupe et al (2009), this method efficiently removes the speckle component, while enhancing the edges and preserving the image structures as this method have been experimenting with the variety of ultrasound images such as ultrasound brain image (Eskildsen et al, 2012;Farnia et al, 2015) and ultrasound 3D liver images (Bakas et al, 2012). The optimal Bayesian estimator of a noisefree patch v opt (B) can be written as in Equation 6:…”

Section: Nonlocal Means Filter (Nl-means Filter)mentioning

confidence: 99%

Gonad Ultrasonography Image Preprocessing for Mahseer (Tor Tombroides)

Razak¹,

Madzin²,

Khalid³

et al. 2017

fisheries-aqua

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Abstract:In the context of breeding and seed production of Mahseer species, understanding and control of the Mahseer gonad maturation level have given strong interest for scientific and commercial purposes. Possible use of ultrasonography for monitoring of gonad maturation in Mahseer fish is investigated. From previous studies, ultrasonography image of gonad can be affected by many speckle noise. Subtle differences between speckle noise and the Mahseer eggs lead to difficulties in identifying the eggs in ultrasonography gonad's image. To eliminate these speckle noise, preprocessing image method is required. Filtering despeckling techniques is initially applied to remove the noise. There will be experimenting in comparing which despeckling technique is suitable for the gonad's ultrasonography image. From the result, the best despeckling technique will be chosen and a framework of preprocessing method will be introduced for identifying the eggs in the gonad ultrasonography image. This noninvasive tool can then obviously be utilized to improve and monitor the maturation level of Mahseer fish. Ultrasonography thus has great potential for use in Mahseer fish both for conservation and aquaculture field. To our knowledge, this is the first article on the preprocessing of ultrasonography image on Mahseer or any fish species.

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Brain-shift compensation by non-rigid registration of intra-operative ultrasound images with preoperative MR images based on residual complexity

Abstract: This improved objective function based on RC in the wavelet domain enables accurate non-rigid multi-modal (US and MRI) image registration which is robust to noise. This technology is promising for compensation of intra-operative brain shift in neurosurgery.

Cited by 26 publications

References 35 publications

Automatic and efficient MRI-US segmentations for improving intraoperative image fusion in image-guided neurosurgery

Automatic and efficient MRI-US segmentations for improving intraoperative image fusion in image-guided neurosurgery

Brain Shift in Neuronavigation of Brain Tumors: An Updated Review of Intra-Operative Ultrasound Applications

Gonad Ultrasonography Image Preprocessing for Mahseer (Tor Tombroides)

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