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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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LV Systolic Point-Cloud Model to Quantify Accuracy of CT Derived Regional Strain

LV Systolic Point-Cloud Model to Quantify Accuracy of CT Der...

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

作者： Manohar, Ashish Colvert, Gabrielle Schluchter, Andrew Contijoch, Francisco McVeigh, Elliot R. Univ Calif San Diego Dept Mech & Aerosp Engn La Jolla CA 92093 USA Univ Calif San Diego Dept Bioengn La Jolla CA 92093 USA Univ Calif San Diego Dept Radiol La Jolla CA 92093 USA Univ Calif San Diego Dept Med Div Cardiol La Jolla CA 92093 USA

ISBN: (纸本)9781510625501

We present an analytical LV systolic model derived from human CT data to serve as the ground truth for optimization and validation of a previously published CT-derived regional strain metric called SQUEEZ. Physiologically-accurate strains were applied to each vertex of a clinically derived end-diastolic LV mesh to create analytical end-systolic poses exhibiting normal function as well as regional hypokinesia of four sizes (17.5mm, 14mm, 10.5mm, and 7mm in diameter), each with a programmed severe, medium, and subtle dysfunction. Regional strain estimates were obtained by registering the end-diastolic mesh to each end-systolic mesh condition using a non-rigid registration algorithm. Ground-truth models of normal function and of severe hypokinesia were used to identify the optimal parameters in the registration algorithm, and to measure the accuracy of detecting regional dysfunction of varying sizes and severities. We found that for normal LV systolic contraction, SQUEEZ values in all 16 AHA segments of the LV were accurately measured (within +/- 0.05). For cases with regional dysfunction, the errors in SQUEEZ in the region around the dysfunction increased with decreasing size of regional dysfunction. The mean SQUEEZ values of the 17.5mm and 14mm diameter dysfunctional regions, which we hypothesize are the most clinically relevant, were accurate to within +/- 0.05.

关键词： X-ray computed tomography Cardiac imaging Regional cardiac function Ischemic heart disease Cardiac modeling Cardiac registration

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Conformal Radiofrequency Ablation to Validate Ultrasound Thermometry

Conformal Radiofrequency Ablation to Validate Ultrasound The...

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

作者： Audigier, Chloe Kim, Younsu Ziegle, Jens Friebe, Michael Boctor, Emad M. Johns Hopkins Univ Dept Comp Sci Baltimore MD 21218 USA Johns Hopkins Univ Dept Radiol Baltimore MD 21218 USA Otto Von Guericke Univ Dept Med Engn Magdeburg Germany

ISBN: (纸本)9781510625501

Thermal ablation is a clinical procedure that aims at destroying pathological tissue minimally invasively through temperature changes. Temperature monitoring during the treatment is instrumental to achieve a precise and successful ablation procedure: ensuring a complete target ablation while preserving as much healthy tissue as possible. Ultrasound (US) is a promising low cost and portable modality, that could provide real-time temperature monitoring. However, the validation of such a technique is challenging. It is usually done with thermometers. They provide temperature measurements with good temporal resolution but only at a few local points. Magnetic Resonance Imaging (MRI) is the gold standard in term of temperature monitoring nowadays. It could also be used for validation of other thermometry techniques with a more accurate spatial resolution, but it requires MR-compatible devices. In this paper, we propose to leverage the use of a novel bipolar radiofrequency (RF) ablation device that provides 10 different ablation shapes to validate an ultrasound-based temperature monitoring method. The monitoring method relies on an external ultrasound element integrated with the bipolar RF ablation probe. This element send through the ablated tissues ultrasound waves that carry time-of-flight information. The ultrasound waves are collected by a clinical diagnostic ultrasound probe and can be related to the changes in temperature due to the ablation since ultrasound propagation velocity in biological tissue changes as temperature increases. We use this ultrasound-based method to monitor temperature during RF ablation. First on simulation data and then on two ex-vivo porcine liver experiments. Those dataset are used to show that we can validate the proposed temperature reconstruction method using the novel conformal radiofrequency ablation device by generating different ablation shapes.

关键词： Radiofrequency Ablation (RFA) Thermal Ablation Computational modeling Ultrasound thermal monitoring Validation

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Association between hemodynamic modifications and clinical outcome of intracranial aneurysms treated using flow diverters

Association between hemodynamic modifications and clinical o...

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

作者： Paliwal, Nikhil Damiano, Robert J. Davies, Jason M. Siddiqui, Adnan H. Meng, Hui SUNY Buffalo Mech & Aerosp Engn Buffalo NY 14260 USA SUNY Buffalo Toshiba Stroke & Vasc Res Ctr Buffalo NY 14260 USA SUNY Buffalo Dept Neurosurg Buffalo NY 14260 USA SUNY Buffalo Dept Biomed Engn Buffalo NY 14260 USA

ISBN: (数字)9781510607163

ISBN: (纸本)9781510607156;9781510607163

Treatment of intracranial aneurysms (IAs) has been revolutionized by the advent of endovascular Flow Diverters (FDs), which disrupt blood flow within the aneurysm to induce pro-thrombotic conditions, and serves as a scaffold for endothelial ingrowth and arterial remodeling. Despite good clinical success of FDs, complications like incomplete occlusion and post-treatment rupture leading to subarachnoid hemorrhage have been reported. In silico computational fluid dynamic analysis of the pre- and post-treated geometries of IA patients can shed light on the contrasting blood hemodynamics associated with different clinical outcomes. In this study, we analyzed hemodynamic modifications in 15 IA patients treated using a single FD;10 IAs were completely occluded (successful) and 5 were partially occluded (unsuccessful) at 12-month follow-up. An in-house virtual stenting workflow was used to recapitulate the clinical intervention on these cases, followed by CFD to obtain pre- and post-treatment hemodynamics. Bulk hemodynamic parameters showed comparable reductions in both groups with average inflow rate and aneurysmal velocity reduction of 40.3% and 52.4% in successful cases, and 34.4% and 49.2% in unsuccessful cases. There was a substantial reduction in localized parameter like vortex coreline length and Energy Loss for successful cases, 38.2% and 42.9% compared to 10.1% and 10.5% for unsuccessful cases. This suggest that for successfully treated IAs, the localized complex blood flow is disrupted more prominently by the FD as compared to unsuccessful cases. These localized hemodynamic parameters can be potentially used in prediction of treatment outcome, thus aiding the clinicians in a priori assessment of different treatment strategies.

关键词： intracranial aneurysm computational fluid dynamics flow diverter virtual stenting device modeling virtual stenting endovascular intervention patient-specific

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4DCBCT-based motion modeling and 3D fluoroscopic image generation for lung cancer radiotherapy

4DCBCT-based motion modeling and 3D fluoroscopic image gener...

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

作者： Dhou, Salam Hurwitz, Martina Mishra, Pankaj Berbeco, Ross Lewis, John Brigham & Womens Hosp Dana Farber Canc Inst Boston MA 02115 USA Harvard Univ Sch Med Boston MA USA Varian Med Syst Palo Alto CA USA

ISBN: (纸本)9781628415056

A method is developed to build patient-specific motion models based on 4DCBCT images taken at treatment time and use them to generate 3D time-varying images (referred to as 3D fluoroscopic images). Motion models are built by applying Principal Component Analysis (PCA) on the displacement vector fields (DVFs) estimated by performing deformable image registration on each phase of 4DCBCT relative to a reference phase. The resulting PCA coefficients are optimized iteratively by comparing 2D projections captured at treatment time with projections estimated using the motion model. The optimized coefficients are used to generate 3D fluoroscopic images. The method is evaluated using anthropomorphic physical and digital phantoms reproducing real patient trajectories. For physical phantom datasets, the average tumor localization error (TLE) and (95th percentile) in two datasets were 0.95 (2.2) mm. For digital phantoms assuming superior image quality of 4DCT and no anatomic or positioning disparities between 4DCT and treatment time, the average TLE and the image intensity error (TIE) in six datasets were smaller using 4DCT-based motion models. When simulating positioning disparities and tumor baseline shifts at treatment time compared to planning 4DCT, the average TLE (95th percentile) and TIE were 4.2 (5.4) mm and 0.15 using 4DCT-based models, while they were 1.2 (2.2) mm and 0.10 using 4DCBCT-based ones, respectively. 4DCBCT-based models were shown to perform better when there are positioning and tumor baseline shift uncertainties at treatment time. Thus, generating 3D fluoroscopic images based on 4DCBCT-based motion models can capture both inter- and intra- fraction anatomical changes during treatment.

关键词： Motion models 3D modeling Tumors 3D image processing image analysis Motion estimation Principal component analysis Error analysis Lawrencium Radiotherapy

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Towards quantitative quasi-static elastography with a gravity-induced deformation source

Towards quantitative quasi-static elastography with a gravit...

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

作者： Griesenauer, Rebekah H. Weis, Jared A. Arlinghaus, Lori R. Meszoely, Ingrid M. Miga, Michael I. Vanderbilt Univ Dept Biomed Engn Nashville TN 37235 USA Vanderbilt Univ Med Ctr Inst Imaging Sci Nashville TN 37235 USA Vanderbilt Univ Dept Surg Med Ctr Nashville TN 37240 USA Vanderbilt Univ Vanderbilt Inst Surg & Engn 221 Kirkland Hall Nashville TN 37235 USA Vanderbilt Univ Med Ctr Dept Radiol & Radiol Sci Nashville TN 37232 USA Vanderbilt Univ Med Ctr Dept Neurol Surg Nashville TN 37235 USA

ISBN: (数字)9781510607163

ISBN: (纸本)9781510607156;9781510607163

Biomechanical breast models have been employed for applications in image registration and analysis, breast augmentation simulation, and for surgical and biopsy guidance. Accurate applications of stress-strain relationships of tissue within the breast can improve the accuracy of biomechanical models that attempt to simulate breast movements. Reported stiffness values for adipose, glandular, and cancerous tissue types vary greatly. Variations in reported stiffness properties are mainly due to differences in testing methodologies and assumptions, measurement errors, and natural inter patient differences in tissue elasticity. Therefore, patient specific, in vivo determination of breast tissue properties is ideal for these procedural applications. Many in vivo elastography methods are not quantitative and/or do not measure material properties under deformation conditions that are representative of the procedure being simulated in the model. In this study, we developed an elasticity estimation method that is performed using deformations representative of supine therapeutic procedures. Reconstruction of material properties was performed by iteratively fitting two anatomical images before and after tissue stimulation. The method proposed is work flow friendly, quantitative, and uses a non-contact, gravity-induced deformation source. We tested this material property optimization procedure in a healthy volunteer and in simulation. In simulation, we show that the algorithm can reconstruct properties with errors below 1% for adipose and 5.6% for glandular tissue regardless of the starting stiffness values used as initial guesses. In clinical data, reconstruction errors are higher (3.6% and 24.2%) due to increased noise in the system. In a clinical context, the elastography method was shown to be promising for use in biomechanical model assisted supine procedures.

关键词： elastography MRI lumpectomy image guidance biomechanical modeling registration breast cancer

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Optimal strategies for modeling anatomy in a hybrid intelligence framework for auto-segmentation of organs

Optimal strategies for modeling anatomy in a hybrid intellig...

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

作者： Hao, You Udupa, Jayaram K. Tong, Yubing Liu, Tiange Wu, Caiyun Odhner, Dewey Torigian, Drew A. Univ Penn Dept Radiol Med Image Proc Grp 602 Goddard Bldg3710 Hamilton Walk Philadelphia PA 19104 USA Yanshan Univ Sch Informat Sci & Engn Qinhuangdao 066004 Hebei Peoples R China

ISBN: (纸本)9781510671614;9781510671607

Organ segmentation is a fundamental requirement in medical image analysis. Many methods have been proposed over the past 6 decades for segmentation. A unique feature of medical images is the anatomical information hidden within the image itself. To bring natural intelligence (NI) in the form of anatomical information accumulated over centuries into deep learning (DL) AI methods effectively, we have recently introduced the idea of hybrid intelligence (HI) that combines NI and AI and a system based on HI to perform medical image segmentation. This HI system has shown remarkable robustness to image artifacts, pathology, deformations, etc. in segmenting organs in the Thorax body region in a multicenter clinical study. The HI system utilizes an anatomy modeling strategy to encode NI and to identify a rough container region in the shape of each object via a non-DL-based approach so that DL training and execution are applied only to the fuzzy container region. In this paper, we introduce several advances related to modeling of the NI component so that it becomes substantially more efficient computationally, and at the same time, is well integrated with the DL portion (AI component) of the system. We demonstrate a 9-40 fold computational improvement in the auto-segmentation task for radiation therapy (RT) planning via clinical studies obtained from 4 different RT centers, while retaining state-of- the-art accuracy of the previous system in segmenting 11 objects in the Thorax body region.

关键词： organ segmentation deep learning hybrid intelligence anatomic models radiation therapy planning computed tomography

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An adaptable model for estimating patient-specific electrical properties of the implanted cochlea

An adaptable model for estimating patient-specific electrica...

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

作者： Bratu, Erin L. Berg, Katelyn A. DeFreese, Andrea J. Gifford, Rene H. Noble, Jack H. Vanderbilt Univ Dept Elect & Comp Engn Nashville TN 37235 USA Vanderbilt Univ Dept Hearing & Speech Sci Med Ctr Nashville TN 37232 USA Vanderbilt Univ Dept Comp Sci Nashville TN 37235 USA

ISBN: (纸本)9781510671614;9781510671607

Hearing loss can significantly decrease the quality of life for individuals who do not receive adequate treatment, but fortunately, a variety of treatments exist depending on the nature of the loss. For individuals with severe-to-profound sensorineural hearing loss who have not achieved sufficient restoration of hearing with other treatments, the cochlear implant (CI) may be an option. The CI is a surgically-inserted neural prosthetic that converts sounds to electrical stimuli to directly stimulate auditory nerve fibers, bypassing the causes of dysfunction in the inner ear. While many recipients experience significant success with their implants, others receive little or no benefit. Multiple factors can affect hearing outcomes, including the quality of the program that controls the device. Prior research has endeavored to provide clinicians with objective information about a patient to assist them in identifying the optimal parameters for this program. Multiple comprehensive computational models that simulate electrical activity in the cochlea have been created for this task. However, these models are often not fully customizable or are highly customized to single sets of clinical measurements, requiring the model to be recomputed as these measurements change over time. Our overall goal is to create a new model of equal or better quality that is fully customizable and can adapt to changing clinical measurements without substantial recomputation. In this work, we present preliminary results of a methodology for one component of such a model, which uses non-patient-specific simulations of voltage spread in the cochlea to estimate patient-specific electric potentials.

关键词： cochlear implant computational modeling image-guided interventions

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A Quantitative Prediction of the Post-operative Lobectomy Lung Physiology using a GPU-based Linear Elastic Lung Biomechanics Model and a Constrained Generative Adversarial Learning Approach

A Quantitative Prediction of the Post-operative Lobectomy Lu...

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

作者： Santhanam, Anand Stiehl, Brad Lauria, Michael Barjaktarevic, Igor Goldin, Jonathan Yanagawa, Jane Low, Daniel Univ Calif Los Angeles Dept Radiat Oncol Los Angeles CA 90024 USA Univ Calif Los Angeles Div Pulm & Crit Care Los Angeles CA USA Univ Calif Los Angeles Div Radiol Los Angeles CA USA Univ Calif Los Angeles Div Thorac Surg Los Angeles CA USA

ISBN: (纸本)9781510640269

Lobectomy is a common and effective procedure for treating early-stage lung cancers. However, for patients with compromised pulmonary function (e.g. COPD) lobectomy can lead to major postoperative pulmonary complications. A technique for quantitatively predicting postoperative pulmonary function is needed to assist surgeons in assessing candidate's suitability for lobectomy. We present a framework for quantitatively predicting the postoperative lung physiology and function using a combination of lung biomechanical modeling and machine learning strategies. A set of 10 patients undergoing lobectomy was used for this purpose. The image input consists of pre- and post-operative breath hold CTs. An automated lobe segmentation algorithm and lobectomy simulation framework was developed using a Constrained Adversarial Generative Networks approach. Using the segmented lobes, a patient-specific GPU-based linear elastic biomechanical and airflow model and surgery simulation was then assembled that quantitatively predicted the lung deformation during the forced expiration maneuver. The lobe in context was then removed by simulating a volume reduction and computing the elastic stress on the surrounding residual lobes and the chest wall. Using the deformed lung anatomy that represents the post-operative lung geometry, the forced expiratory volume in 1 second (FEV1) (the amount of air exhaled by a patient in 1 second starting from maximum inhalation), and forced vital capacity (FVC) (the amount of air exhaled by force from maximum inhalation), were then modeled. Our results demonstrated that the proposed approach quantitatively predicted the postoperative lobe-wise lung function at the FEV1 and FEV/FVC.

关键词： Lobectomy Lung Dynamics Lung Segmentation Biomechanical modeling Machine Learning

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Tumor deformation correction for an image guidance system in breast conserving surgery

Tumor deformation correction for an image guidance system in...

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

作者： Richey, Winona L. Heiselman, Jon Ringel, Morgan Meszoely, Ingrid M. Miga, Michael, I Vanderbilt Univ Dept Biomed Engn Nashville TN 37235 USA Vanderbilt Univ Div Surg Oncol Med Ctr Nashville TN USA Vanderbilt Univ Dept Radiol & Radiol Sci Nashville TN USA Vanderbilt Inst Surg & Engn Nashville TN USA Vanderbilt Univ Dept Neurol Surg Med Ctr Nashville TN USA

ISBN: (纸本)9781510649446;9781510649439

Breast cancer is the most common cancer in women, and surgical resection is standard of care for the majority of breast cancer patients. Unfortunately, current reoperation rates are 10-29%. Uncertainty in lesion localization is one of the main factors contributing to these high reoperation rates. This work uses the linearized iterative boundary reconstruction approach to model patient breast deformation due to abduction of the ipsilateral arm. A preoperative supine magnetic resonance (MR) image was obtained with the patient's arms down near the torso. A mock intraoperative breast shape was measured from a supine MR image obtained with the patient's arm up near the head. Sparse data was subsampled from the full volumetric image to represent realistic intraoperative data collection: surface fiducial points, the intra-fiducial skin surface, and the chest wall as measured with 7 tracked ultrasound images. The deformed preoperative arm-down data was compared to the ground truth arm-up data. From rigid registration to model correction the tumor centroid distance improves from 7.3 mm to 3.3 mm, average surface fiducial error across 9 synthetic fiducials and the nipple improves from 7.4. 2.2 to 1.3. 0.7, and average subsurface error across 14 corresponding features improves from 6.2 +/- 1.4 mm to 3.5 +/- 1.1 mm. Using preoperative supine MR imaging and sparse data in the deformed position, this modeling framework can correct for breast shape changes between imaging and surgery to more accurately predict intraoperative position of the tumor as well as 10 surface fiducials and 14 subsurface features.

关键词： breast conserving surgery image-guided surgery soft tissue deformation breast cancer lumpectomy registration biomechanical modeling

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A semiautomatic algorithm for three-dimensional segmentation of the prostate on CT images using shape and local texture characteristics

A semiautomatic algorithm for three-dimensional segmentation...

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

作者： Shahedi, Maysam Ma, Ling Halicek, Martin Guo, Rongrong Zhang, Guoyi Schuster, David M. Nieh, Peter Master, Viraj Fei, Baowei Emory Univ Dept Radiol & Imaging Sci Atlanta GA 30322 USA Georgia Inst Technol Wallace H Coulter Dept Biomed Engn Atlanta GA 30332 USA Emory Univ Atlanta GA 30322 USA Emory Univ Dept Urol Atlanta GA 30322 USA Emory Univ Winship Canc Inst Atlanta GA 30322 USA

ISBN: (数字)9781510616424

ISBN: (纸本)9781510616424

Prostate segmentation in computed tomography (CT) images is useful for planning and guidance of the diagnostic and therapeutic procedures. However, the low soft-tissue contrast of CT images makes the manual prostate segmentation a time-consuming task with high inter-observer variation. We developed a semi-automatic, three-dimensional (3D) prostate segmentation algorithm using shape and texture analysis and have evaluated the method against manual reference segmentations. In a training data set we defined an inter-subject correspondence between surface points in the spherical coordinate system. We applied this correspondence to model the globular and smoothly curved shape of the prostate with 86, well-distributed surface points using a point distribution model that captures prostate shape variation. We also studied the local texture difference between prostate and non-prostate tissues close to the prostate surface. For segmentation, we used the learned shape and texture characteristics of the prostate in CT images and we used a set of user inputs for prostate localization. We trained our algorithm using 23 CT images and tested it on 10 images. We evaluated the results compared with those of two experts' manual reference segmentations using different error metrics. The average measured Dice similarity coefficient (DSC) and mean absolute distance (MAD) were 88 +/- 2% and 1.9 +/- 0.5 mm, respectively. The averaged inter-expert difference measured on the same dataset was 91 +/- 4% (DSC) and 1.3 +/- 0.6 mm (MAD). With no prior intra-patient information, the proposed algorithm showed a fast, robust and accurate performance for 3D CT segmentation.

关键词： image segmentation Prostate Computed tomography image processing algorithms and systems 3D image processing 3D modeling Algorithm development Cancer Shape analysis

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