Intra-operative Ultrasound to MRI Fusion with a Public Multimodal Discrete Registration Tool

Heinrich, Mattias P.

doi:10.1007/978-3-030-01045-4_19

Cited by 16 publications

(15 citation statements)

References 12 publications

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“…Nevertheless, aligning preoperative MRI and iUS for brain shift correction is still a challenging problem due to the different characteristics of each modality and the type of information they provide. Consequently, only a few studies have applied deep learning on registering preoperative MRI and iUS for brain shift correction [9][10][11]. In this paper, a fast and robust deep learning-based method for automatic preoperative MRI and interventional US registration is presented to assist the neurosurgeons by correcting brain shift intraoperatively.…”

Section: Introductionmentioning

confidence: 99%

Towards automated correction of brain shift using deep deformable magnetic resonance imaging-intraoperative ultrasound (MRI-iUS) registration

Zeineldin

Karar

Coburger

et al. 2020

Current Directions in Biomedical Engineering

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Intraoperative brain deformation, so-called brain shift, affects the applicability of preoperative magnetic resonance imaging (MRI) data to assist the procedures of intraoperative ultrasound (iUS) guidance during neurosurgery. This paper proposes a deep learning-based approach for fast and accurate deformable registration of preoperative MRI to iUS images to correct brain shift. Based on the architecture of 3D convolutional neural networks, the proposed deep MRI-iUS registration method has been successfully tested and evaluated on the retrospective evaluation of cerebral tumors (RESECT) dataset. This study showed that our proposed method outperforms other registration methods in previous studies with an average mean squared error (MSE) of 85. Moreover, this method can register three 3D MRI-US pair in less than a second, improving the expected outcomes of brain surgery.

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Section: Introductionmentioning

confidence: 99%

Towards automated correction of brain shift using deep deformable magnetic resonance imaging-intraoperative ultrasound (MRI-iUS) registration

Zeineldin

Karar

Coburger

et al. 2020

Current Directions in Biomedical Engineering

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“…First, we inherit the use of multi-scale and multi-orientation attributes in the original DRAMMS framework. Single-scale and single-orientation attributes may not always generalize well to other datasets (Wein, 2018) (Heinrich, 2018). Second, we skip ad-hoc pre-processing.…”

Section: Introductionmentioning

confidence: 99%

“…A third contribution of our work is thorough evaluation. Most existing methods have been evaluated with only single-site data (Farnia et al, 2014) (Farnia et al, 2016) (Lindseth et al, 2003) (Farnia, Ahmadian, Shabanian, Serej, & Alirezaie, 2015), or up to two datasets but resulted in inconsistent levels of accuracy (Hong et al, 2018) (Shams, Boucher, & Kadoury, 2018) (Zhong et al, 2018), or used dataset-specific parameters (Wein, 2018) (Heinrich, 2018). We used three datasets, so far the most comprehensive multi-site data, to evaluate MR-US registration.…”

Section: Introductionmentioning

confidence: 99%

Deformable MRI-Ultrasound registration using correlation-based attribute matching for brain shift correction: Accuracy and generality in multi-site data

et al. 2019

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Deformable MRI-Ultrasound registration using correlation-based attribute matching for brain shift correction: Accuracy and generality in multi-site data The MIT Faculty has made this article openly available. Please share how this access benefits you. Your story matters. Citation Machado, Inês et al. "Deformable MRI-Ultrasound registration using correlation-based attribute matching for brain shift correction: Accuracy and generality in multi-site data." NeuroImage 202

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“…MRI has also shown success in slice registration . Real‐time US‐MRI fusion imaging was evaluated by Kaplan et al .…”

Section: Introductionmentioning

confidence: 99%

“…MRI has also shown success in slice registration. 48,[55][56][57] Real-time US-MRI fusion imaging was evaluated by Kaplan et al 58 for use in trans-rectal biopsies with the goal of increasing the yield of the biopsy procedure. Image fusion was performed using six common fiducial markers on both the US and MRI images.…”

Section: Introductionmentioning

confidence: 99%

Two‐dimensional ultrasound‐computed tomography image registration for monitoring percutaneous hepatic intervention

Pohlman

Turney

et al. 2019

Medical Physics

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Purpose Deformable registration of ultrasound (US) and contrast enhanced computed tomography (CECT) images are essential for quantitative comparison of ablation boundaries and dimensions determined using these modalities. This comparison is essential as stiffness‐based imaging using US has become popular and offers a nonionizing and cost‐effective imaging modality for monitoring minimally invasive microwave ablation procedures. A sensible manual registration method is presented that performs the required CT‐US image registration. Methods The two‐dimensional (2D) virtual CT image plane that corresponds to the clinical US B‐mode was obtained by “virtually slicing” the 3D CT volume along the plane containing non‐anatomical landmarks, namely points along the microwave ablation antenna. The initial slice plane was generated using the vector acquired by rotating the normal vector of the transverse (i.e., xz) plane along the angle subtended by the antenna. This plane was then further rotated along the ablation antenna and shifted along with the direction of normal vector to obtain similar anatomical structures, such as the liver surface and vasculature that is visualized on both the CT virtual slice and US B‐mode images on 20 patients. Finally, an affine transformation was estimated using anatomic and non‐anatomic landmarks to account for distortion between the colocated CT virtual slice and US B‐mode image resulting in a final registered CT virtual slice. Registration accuracy was measured by estimating the Euclidean distance between corresponding registered points on CT and US B‐mode images. Results Mean and SD of the affine transformed registration error was 1.85 ± 2.14 (mm), computed from 20 coregistered data sets. Conclusions Our results demonstrate the ability to obtain 2D virtual CT slices that are registered to clinical US B‐mode images. The use of both anatomical and non‐anatomical landmarks result in accurate registration useful for validating ablative margins and comparison to electrode displacement elastography based images.

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Intra-operative Ultrasound to MRI Fusion with a Public Multimodal Discrete Registration Tool

Cited by 16 publications

References 12 publications

Towards automated correction of brain shift using deep deformable magnetic resonance imaging-intraoperative ultrasound (MRI-iUS) registration

Towards automated correction of brain shift using deep deformable magnetic resonance imaging-intraoperative ultrasound (MRI-iUS) registration

Deformable MRI-Ultrasound registration using correlation-based attribute matching for brain shift correction: Accuracy and generality in multi-site data

Two‐dimensional ultrasound‐computed tomography image registration for monitoring percutaneous hepatic intervention

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