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检索条件"任意字段=Conference on SPIE Image-Guided Procedures, Robotic Interventions, and Modeling"

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Patient-specific electro-anatomical modeling of cochlear implants using deep neural networks

Patient-specific electro-anatomical modeling of cochlear imp...

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conference on Medical Imaging - image-guided procedures, robotic interventions, and modeling

作者： Liu, Ziteng Noble, Jack H. Vanderbilt Univ Dept Comp Sci Nashville TN 37235 USA Vanderbilt Univ Dept Elect & Comp Engn Nashville TN 37235 USA

ISBN: (纸本)9781510649446;9781510649439

Cochlear implants (CIs) are considered the standard-of-care treatment for profound sensory-based hearing loss. After CI surgery, an audiologist will adjust the CI processor settings for CI recipients to improve overall hearing performance. However, this programming procedure can be long and may lead to suboptimal outcomes due to the lack of objective information. In previous research, our group has developed methods that use patient-specific electrical characteristics to simulate the activation pattern of auditory nerves when they are stimulated by CI electrodes. However, estimating those electrical characteristics require extensive computation time and resources. In this paper, we proposed a deep-learning-based method to coarsely estimate the patient-specific electrical characteristics using a cycle-consistent network architecture. These estimates can then be further optimized using a limited range conventional searching strategy. Our network is trained with a dataset generated by solving physics-based models. The results show that our proposed method can generate high-quality predictions that can be used in the patient-specific model and largely improves the speed of constructing models.

关键词： Cochlear implant 3d neural networks nerve models

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Evaluation of a high-resolution patient-specific model of the electrically stimulated cochlea

Evaluation of a high-resolution patient-specific model of th...

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conference on Medical Imaging - image-guided procedures, robotic interventions, and modeling

作者： Cakir, Ahmet Dwyer, Robert T. Noble, Jack. H. Vanderbilt Univ Dept Elect Engn & Comp Sci 221 Kirkland Hall Nashville TN 37235 USA Vanderbilt Univ Med Ctr Dept Hearing & Speech Sci Nashville TN 37232 USA

ISBN: (数字)9781510607163

ISBN: (纸本)9781510607156;9781510607163

Cochlear implants (CIs) are surgically-implanted medical devices used to treat individuals with sensorineural hearing loss who do not receive benefit from appropriately fit amplification. Although these devices have been remarkably successful at restoring audibility, many patients experience poor outcomes. Our group has developed the first image-guided CI programming (IGCIP) technique where the positions of the electrodes are found in CT images and used to estimate neural activation patterns, which is unique information that audiologists can use to define patient-specific processor settings. In our current system, neural activation is estimated using only the distance from each electrode to the neural activation sites. This approach might be less accurate than using a high-resolution electro-anatomical model (EAM) of the electrically stimulated cochlea to perform physics-based estimation of neural activation. In this work, we propose a patient-customized EAM approach where the EAM is spatially and electrically adapted to a patient-specific configuration. Spatial adaptation is done through non-rigid registration of the model with the patient CT image. Electrical adaptation is done by adjusting tissue resistivity parameters so that the intra-cochlear voltage distributions predicted by the model best match those directly measured for the patient via their implant. We demonstrated our approach for N=7 patients. We found that our approach results in mean percent differences between direct and simulated measurements of voltage distributions of 11%. In addition, visual comparison shows the simulated and measured voltage distributions are qualitatively in good agreement. This represents a crucial step toward developing and validating the first in vivo patient-specific cochlea EAMs.

关键词： Cochlear implant modeling image-guided cochlear implant programming

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Surface-based Volumetric image Reconstruction for image-guided procedures Using a Data-Driven Framework

Surface-based Volumetric Image Reconstruction for Image-Guid...

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conference on Medical Imaging - image-guided procedures, robotic interventions, and modeling

作者： Chang, Chih-Wei Pan, Shaoyan Tian, Zhen Wang, Tonghe Axente, Marian Shelton, Joseph Liu, Tian Roper, Justin Yang, Xiaofeng Emory Univ Dept Radiat Oncol Atlanta GA 30308 USA Emory Univ Winship Canc Inst Atlanta GA 30308 USA Emory Univ Dept Biomed Informat Atlanta GA 30308 USA Univ Chicago Dept Radiat & Cellular Oncol Chicago IL 60637 USA Mem Sloan Kettering Canc Ctr Dept Med Phys New York NY 10065 USA Mt Sinai Med Ctr Dept Radiat Oncol New York NY 10029 USA

ISBN: (纸本)9781510671614;9781510671607

The advent of computed tomography significantly improves patients' health regarding diagnosis, prognosis, and treatment planning and image-guided radiotherapy. However, tomographic imaging cannot achieve real-time imaging and the imaging escalates concomitant radiation doses to patients, inducing potential secondary cancer by 4%. We demonstrate the feasibility of a data-driven approach to synthesize volumetric images using patients' surface images, which can be obtained from a zero-dose surface imaging system. This study includes 500 computed tomography (CT) image sets from 50 patients. Compared to the ground truth CT, the synthetic images result in the evaluation metric values of 26.9 +/- 4.1 Hounsfield units, 39.1 +/- 1.0 dB, and 0.97 +/- 0.01 regarding the mean absolute error, peak signal-to-noise ratio, and structural similarity index measure. This approach provides a data integration solution that can potentially enable real-time imaging, which is free of radiation-induced risk and could be applied to image-guided medical procedures.

关键词： Radiotherapy Computed tomography 3D image processing Anatomy image restoration 3D image reconstruction 3D modeling Deep learning image quality Machine learning

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Dynamic Tracking of a Deformable Tissue Based on 3D-2D MR-US image Registration

Dynamic Tracking of a Deformable Tissue Based on 3D-2D MR-US...

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conference on Medical Imaging - image-guided procedures, robotic interventions, and modeling

作者： Marami, Bahram Sirouspour, Shahin Fenster, Aaron Capson, David W. McMaster Univ Dept Elect & Comp Engn Hamilton ON L8S 4L8 Canada Robert Res Inst Imaging Res Lab London ON N6A 5B7 Canada Univ Western Ontario Biomed Engn Grad Program London ON N6A 3K7 Canada Univ Victoria Dept Elect & Comp Engn Victoria BC V8W 2Y2 Canada

ISBN: (纸本)9780819498298

Real-time registration of pre-operative magnetic resonance (MR) or computed tomography (CT) images with intra-operative Ultrasound (US) images can be a valuable tool in image-guided therapies and interventions. This paper presents an automatic method for dynamically tracking the deformation of a soft tissue based on registering pre-operative three-dimensional (3D) MR images to intra-operative two-dimensional (2D) US images. The registration algorithm is based on concepts in state estimation where a dynamic finite element (FE)-based linear elastic deformation model correlates the imaging data in the spatial and temporal domains. A Kalman-like filtering process estimates the unknown deformation states of the soft tissue using the deformation model and a measure of error between the predicted and the observed intra-operative imaging data. The error is computed based on an intensity-based distance metric, namely, modality independent neighborhood descriptor (MIND), and no segmentation or feature extraction from images is required. The performance of the proposed method is evaluated by dynamically deforming 3D pre-operative MR images of a breast phantom tissue based on real-time 2D images obtained from an US probe. Experimental results on different registration scenarios showed that deformation tracking converges in a few iterations. The average target registration error on the plane of 2D US images for manually selected fiducial points was between 0.3 and 1.5 mm depending on the size of deformation.

关键词： Multimodal image registration 3D-2D MR-US registration image-guided intervention dynamic finite element modeling state estimation

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Retractor-Induced Brain Shift Compensation in image-guided Neurosurgery

Retractor-Induced Brain Shift Compensation in Image-guided N...

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conference on Medical Imaging - image-guided procedures, robotic interventions, and modeling

作者： Fan, Xiaoyao Ji, Songbai Hartov, Alex Roberts, David Paulsen, Keith Dartmouth Coll Thayer Sch Engn Hanover NH 03755 USA Dartmouth Med Sch Hanover NH 03755 USA

ISBN: (纸本)9780819494450

In image-guided neurosurgery, intraoperative brain shift significantly degrades the accuracy of neuronavigation that is solely based on preoperative magnetic resonance images (pMR). To compensate for brain deformation and to maintain the accuracy in image guidance achieved at the start of surgery, biomechanical models have been developed to simulate brain deformation and to produce model-updated MR images (uMR) to compensate for brain shift. To-date, most studies have focused on shift compensation at early stages of surgery (i.e., updated images are only produced after craniotomy and durotomy). Simulating surgical events at later stages such as retraction and tissue resection are, perhaps, clinically more relevant because of the typically much larger magnitudes of brain deformation. However, these surgical events are substantially more complex in nature, thereby posing significant challenges in model-based brain shift compensation strategies. In this study, we present results from an initial investigation to simulate retractor-induced brain deformation through a biomechanical finite element (FE) model where whole-brain deformation assimilated from intraoperative data was used produce uMR for improved accuracy in image guidance. Specifically, intensity-encoded 3D surface profiles at the exposed cortical area were reconstructed from intraoperative stereovision (iSV) images before and after tissue retraction. Retractor-induced surface displacements were then derived by coregistering the surfaces and served as sparse displacement data to drive the FE model. With one patient case, we show that our technique is able to produce uMR that agrees well with the reconstructed iSV surface after retraction. The computational cost to simulate retractor-induced brain deformation was approximately 10 min. In addition, our approach introduces minimal interruption to the surgical workflow, suggesting the potential for its clinical application.

关键词： image-guided neurosurgery brain deformation computational modeling stereovision surface registration

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Patient-specific 4D Monte Carlo Dose Accumulation using Correspondence Model Based Motion Prediction

Patient-specific 4D Monte Carlo Dose Accumulation using Corr...

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conference on Medical Imaging - image-guided procedures, robotic interventions, and modeling

作者： Sentker, Thilo Madesta, Frederic Werner, Rene Univ Med Ctr Hamburg Eppendorf Dept Computat Neurosci Hamburg Germany Univ Med Ctr Hamburg Eppendorf Dept Radiotherapy & Radiooncol Hamburg Germany

ISBN: (纸本)9781510625501

Quality assurance in current 4D radiotherapy workflows is of great importance to assure a positive treatment outcome, i.e. total tumor eradication. Especially for the treatment of lung and liver tumors, which are subject to high motion magnitudes due to patient breathing, it is crucial to verify the applied dose to the target volume. In this study, we present a new 4D Monte Carlo dose accumulation approach that accounts for internal patient motion during treatment and is therefore able to predict the actual 3D dose distribution delivered to the patient for quality assurance purposes. Monte Carlo simulations are conducted using the EGSnrc software toolkit, which models the propagation of photons, electrons and positrons. However, to consider dynamic beam parameters and the movement of internal patient geometry, we developed a method to compute the dose for each control point of the actual VMAT patient treatment plan to account for breathing induced internal patient motion. The internal motion during treatment is predicted using correspondence modeling, which correlates patient-specific DIR-based internal motion information and external breathing signals and is trained on 4D CT data of the patient. For each VMAT control point, a corresponding motion vector field is predicted and applied to the original patient CT to allow for dose computation on the patient geometry as it was irradiated during treatment. Thus, density changes while treatment due to patient breathing motion are taken into account during computation of the resulting dose distribution.

关键词： Monte Carlo simulation 4D dose accumulation correspondence modeling deformable image registration

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Simulated evaluation of an intraoperative surface modeling method for catheter ablation by a real phantom simulation experiment

Simulated evaluation of an intraoperative surface modeling m...

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conference on Medical Imaging - image-guided procedures, robotic interventions, and modeling

作者： Sun, Deyu Rettmann, Maryam E. Packer, Douglas Robb, Richard A. Holmes, David R., III Mayo Clin Biomed Imaging Resources Rochester MN 55905 USA Mayo Clin Div Cardiovasc Dis Rochester MN 55905 USA

ISBN: (纸本)9781628415056

In this work, we propose a phantom experiment method to quantitatively evaluate an intraoperative left-atrial modeling update method. In prior work, we proposed an update procedure which updates the preoperative surface model with information from real-time tracked 2D ultrasound. Prior studies did not evaluate the reconstruction using an anthropomorphic phantom. In this approach, a silicone heart phantom (based on a high resolution human atrial surface model reconstructed from CT images) was made as simulated atriums. A surface model of the left atrium of the phantom was deformed by a morphological operation simulating the shape difference caused by organ deformation between pre-operative scanning and intra-operative guidance. During the simulated procedure, a tracked ultrasound catheter was inserted into right atrial phantom scanning the left atrial phantom in a manner mimicking the cardiac ablation procedure. By merging the preoperative model and the intraoperative ultrasound images, an intraoperative left atrial model was reconstructed. According to results, the reconstruction error of the modeling method is smaller than the initial geometric difference caused by organ deformation. As the area of the left atrial phantom scanned by ultrasound increases, the reconstruction error of the intraoperative surface model decreases. The study validated the efficacy of the modeling method.

关键词： intraoperative left atrial surface reconstruction Poisson Equation Reconstruction intra-cardiac echocardiography catheter ablation phantom evaluation

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Pre- to post-operative CT image registration to estimate cortical shift for image updating in deep brain stimulation

Pre- to post-operative CT image registration to estimate cor...

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conference on Medical Imaging - image-guided procedures, robotic interventions, and modeling

作者： Li, Chen Fan, Xiaoyao Aronson, Joshua Paulsen, Keith D. Dartmouth Coll Thayer Sch Engn Hanover NH 03755 USA Dartmouth Coll Geisel Sch Med Hanover NH 03755 USA Norris Cotton Canc Ctr Lebanon NH 03756 USA Dartmouth Hitchcock Med Ctr Lebanon NH 03756 USA

ISBN: (数字)9781510616424

ISBN: (纸本)9781510616424

The success of deep brain stimulations (DBS) heavily relies on the accurate placement of electrodes in the operating room (OR). However, the pre-operative images such as MRI and CT for surgical targeting are degraded by brain shift, a combination of brain movement and deformation. One way to compensate for this intra-operative brain shift is to utilize a nonlinear biomechanical brain model to estimate the whole brain deformation based on which an updated MR can be generated. Due to the variability of deformation in both magnitude and direction among different cases, partially sampled intraoperative data (e.g., O-arm, CT) of tissue motion is critical to guide the model estimation. In this paper, we present a method to extract the sparse data by matching brain surface features from pre- and post-operative CTs, followed by the reconstruction of the full 3d-displacement field based on the original spatial information of these 2d points. Specifically, the size and the location of the sparse data were determined based on the pneumocephalus in the post-operative CT. The 2D CT-encoded texture maps from both pre-and post-operative CTs were then registered using Demons algorithm. The final 3d-displacement field in our one-patient-example shows an average lateral shift of 1.42mm, and a shift of 10.11mm in the direction of gravity. The results presented in this work have shown the potential of assimilating the sparse data from intra-operative images into the pipeline of model-based image guidance for DBS in the future.

关键词： Deep Brain Stimulation brain shift image-guided neurosurgery sparse data biomechanical brain modeling

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A comparison of geometry- and feature-based sparse data extraction for model-based image updating in deep brain stimulation surgery

A comparison of geometry- and feature-based sparse data extr...

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conference on Medical Imaging - image-guided procedures, robotic interventions, and modeling

ISBN: (纸本)9781510625501

Deep brain stimulation (DBS) electrode placement is a burr-hole procedure for the treatment of patients with neuro-degenerative disease such as Parkinson's disease, essential tremor and dystonia. Accurate placement of electrodes is the key to optimal surgical outcome. However, the accuracy of pre-operative images used for surgical planning are often degraded by intraoperative brain shift. To compensate for intraoperative target deviation, we have developed a biomechanical model, driven by partially sampled displacements between pre- and postCT, to estimate a whole brain displacement field based on which updated CT (uCT) can be generated. The results of the finite element model depend on sparse data, as the model minimizes the difference between model estimates and sparse data. Existing approaches to extract sparse data from brain surface are typically geometry or feature-based. In this paper, we explore a geometry based iterative closest point (ICP) algorithm and a feature-based image registration algorithm, and drive the model with 1) geometry-based sparse data only, 2) feature-based sparse data only, and 3) combined data from 1) and 2). We assess the model performance in terms of model-data misfit, as well as target registration errors (TREs) at the anterior commissure (AC) and posterior commissure (PC). Results show that the model driven by the geometry-based sparse data reduced the TREs of preCT from 1.65mm to 1.26 mm and 1.88 mm to 1.58 mm at AC and PC, respectively by compensating majorly along the direction of gravity and the longitudinal axis, whereas feature-based sparse data contributed to the compensation along the lateral direction at PC.

关键词： Brain shift deep brain stimulation image-guided surgery sparse data computational modeling biomechanical model

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Statistical shape modeling based renal volume measurement using tracked ultrasound

Statistical shape modeling based renal volume measurement us...

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conference on Medical Imaging - image-guided procedures, robotic interventions, and modeling

作者： Raikar, Vipul Pai Kwartowitz, David M. Clemson Univ Dept Bioengn Clemson SC 29634 USA

ISBN: (数字)9781510607163

ISBN: (纸本)9781510607156;9781510607163

Autosomal dominant polycystic kidney disease (ADPKD) is the fourth most common cause of kidney transplant worldwide accounting for 7-10% of all cases. Although ADPKD usually progresses over many decades, accurate risk prediction is an important task.(1) Identifying patients with progressive disease is vital to providing new treatments being developed and enable them to enter clinical trials for new therapy. Among other factors, total kidney volume (TKV) is a major biomarker predicting the progression of ADPKD. Consortium for Radiologic Imaging Studies in Polycystic Kidney Disease (CRISP)2 have shown that TKV is an early, and accurate measure of cystic burden and likely growth rate. It is strongly associated with loss of renal function.(3) While ultrasound (US) has proven as an excellent tool for diagnosing the disease;monitoring short-term changes using ultrasound has been shown to not be accurate. This is attributed to high operator variability and reproducibility as compared to tomographic modalities such as CT and MR (Gold standard). Ultrasound has emerged as one of the standout modality for intra-procedural imaging and with methods for spatial localization has afforded us the ability to track 2D ultrasound in physical space which it is being used. In addition to this, the vast amount of recorded tomographic data can be used to generate statistical shape models that allow us to extract clinical value from archived image sets. In this work, we aim at improving the prognostic value of US in managing ADPKD by assessing the accuracy of using statistical shape model augmented US data, to predict TKV, with the end goal of monitoring short-term changes.

关键词： Statistical Shape modeling Tracked Ultrasound ADPKD

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