Prof. Cristian A. Linte Profile

Prof. Cristian A. Linte

Associate Professor at Rochester Institute of Technology

SPIE Involvement:

Conference Program Committee | Editorial Board Member: Journal of Medical Imaging | Author | Editor

Publications (54)

Proceedings Article | 29 March 2024 Poster + Paper

Comparative analysis of non-rigid registration techniques for liver surface registration

Bipasha Kundu, Zixin Yang, Richard Simon, Cristian Linte

Proceedings Volume 12928, 129282B (2024) https://doi.org/10.1117/12.3008594

KEYWORDS: Liver, Deformation, Image registration, Visibility, Navigation systems, Mathematical optimization, Finite element methods, Data modeling, Tissues

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SPIE Journal Paper | 29 August 2023

Inpainting surgical occlusion from laparoscopic video sequences for robot-assisted interventions

S. M. Kamrul Hasan, Richard A. Simon, Cristian A. Linte

JMI, Vol. 10, Issue 04, 045002, (August 2023) https://doi.org/10.1117/12.10.1117/1.JMI.10.4.045002

KEYWORDS: Image segmentation, Equipment, Video, Laparoscopy, Interpolation, Visualization, Surgery, Education and training, Convolution, Endoscopy

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PurposeMedical technology for minimally invasive surgery has undergone a paradigm shift with the introduction of robot-assisted surgery. However, it is very difficult to track the position of the surgical tools in a surgical scene, so it is crucial to accurately detect and identify surgical tools. This task can be aided by deep learning-based semantic segmentation of surgical video frames. Furthermore, due to the limited working and viewing areas of these surgical instruments, there is a higher chance of complications from tissue injuries (e.g., tissue scars and tears).ApproachWith the aid of digital inpainting algorithms, we present an application that uses image segmentation to remove surgical instruments from laparoscopic/endoscopic video. We employ a modified U-Net architecture (U-NetPlus) to segment the surgical instruments. It consists of a redesigned decoder and a pre-trained VGG11 or VGG16 encoder. The decoder was modified by substituting an up-sampling operation based on nearest-neighbor interpolation for the transposed convolution operation. Furthermore, these interpolation weights do not need to be learned to perform upsampling, which eliminates the artifacts generated by the transposed convolution. In addition, we use a very fast and adaptable data augmentation technique to further enhance performance. The instrument segmentation mask is filled in (i.e., inpainted) by the tool removal algorithms using the previously acquired tool segmentation masks and either previous instrument-containing frames or instrument-free reference frames.ResultsWe have shown the effectiveness of the proposed surgical tool segmentation/removal algorithms on a robotic instrument dataset from the MICCAI 2015 and 2017 EndoVis Challenge. We report a 90.20% DICE for binary segmentation, a 76.26% DICE for instrument part segmentation, and a 46.07% DICE for instrument type (i.e., all instruments) segmentation on the MICCAI 2017 challenge dataset using our U-NetPlus architecture, outperforming the results of earlier techniques used and tested on these data. In addition, we demonstrated the successful execution of the tool removal algorithm from surgical tool-free videos that contained moving surgical tools that were generated artificially.ConclusionsOur application successfully separates and eliminates the surgical tool to reveal a view of the background tissue that was otherwise hidden by the tool, producing results that are visually similar to the actual data.

SPIE Journal Paper | 14 July 2023

Disparity refinement framework for learning-based stereo matching methods in cross-domain setting for laparoscopic images

Zixin Yang, Richard Simon, Cristian Linte

JMI, Vol. 10, Issue 04, 045001, (July 2023) https://doi.org/10.1117/12.10.1117/1.JMI.10.4.045001

KEYWORDS: Endoscopy, Education and training, Laparoscopy, Light sources and illumination, Error analysis, Equipment, Surgery, Data modeling, Natural surfaces, Interpolation

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SPIE Journal Paper | 24 May 2023

Deep learning architecture for 3D image super-resolution of late gadolinium enhanced cardiac MRI

Roshan Reddy Upendra, Richard Simon, Cristian A. Linte

JMI, Vol. 10, Issue 05, 051808, (May 2023) https://doi.org/10.1117/12.10.1117/1.JMI.10.5.051808

KEYWORDS: Image segmentation, Super resolution, 3D modeling, Education and training, 3D image processing, Data modeling, 3D image enhancement, Interpolation, Image resolution, Deep learning

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PurposeHigh-resolution late gadolinium enhanced (LGE) cardiac magnetic resonance imaging (MRI) volumes are difficult to acquire due to the limitations of the maximal breath-hold time achievable by the patient. This results in anisotropic 3D volumes of the heart with high in-plane resolution, but low-through-plane resolution. Thus, we propose a 3D convolutional neural network (CNN) approach to improve the through-plane resolution of the cardiac LGE-MRI volumes.ApproachWe present a 3D CNN-based framework with two branches: a super-resolution branch to learn the mapping between low-resolution and high-resolution LGE-MRI volumes, and a gradient branch that learns the mapping between the gradient map of low-resolution LGE-MRI volumes and the gradient map of high-resolution LGE-MRI volumes. The gradient branch provides structural guidance to the CNN-based super-resolution framework. To assess the performance of the proposed CNN-based framework, we train two CNN models with and without gradient guidance, namely, dense deep back-projection network (DBPN) and enhanced deep super-resolution network. We train and evaluate our method on the 2018 atrial segmentation challenge dataset. Additionally, we also evaluate these trained models on the left atrial and scar quantification and segmentation challenge 2022 dataset to assess their generalization ability. Finally, we investigate the effect of the proposed CNN-based super-resolution framework on the 3D segmentation of the left atrium (LA) from these cardiac LGE-MRI image volumes.ResultsExperimental results demonstrate that our proposed CNN method with gradient guidance consistently outperforms bicubic interpolation and the CNN models without gradient guidance. Furthermore, the segmentation results, evaluated using Dice score, obtained using the super-resolved images generated by our proposed method are superior to the segmentation results obtained using the images generated by bicubic interpolation (p < 0.01) and the CNN models without gradient guidance (p < 0.05).ConclusionThe presented CNN-based super-resolution method with gradient guidance improves the through-plane resolution of the LGE-MRI volumes and the structure guidance provided by the gradient branch can be useful to aid the 3D segmentation of cardiac chambers, such as LA, from the 3D LGE-MRI images.

Proceedings Article | 10 April 2023 Presentation + Paper

A deep learning framework to estimate elastic modulus from ultrasound measured displacement fields

Utsav Ratna Tuladhar, Richard Simon, Cristian Linte, Michael Richards

Proceedings Volume 12470, 124700P (2023) https://doi.org/10.1117/12.2654675

KEYWORDS: Elastic modulus, Tissues, Deep learning, Elastography, Ultrasonography, Inverse problems, Elasticity, Computer simulations

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Proceedings Article | 3 April 2023 Presentation + Paper

Developing and evaluating the fidelity of patient specific kidney emulating phantoms for image-guided intervention applications

Kelly Merrell, Peter Jackson, Richard Simon, Cristian Linte

Proceedings Volume 12466, 124661C (2023) https://doi.org/10.1117/12.2655603

KEYWORDS: Gelatin, 3D modeling, Computed tomography, Kidney, Image registration, 3D printing, Manufacturing, 3D image processing, Tissues, Process modeling

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Proceedings Article | 3 April 2023 Presentation + Paper

A disparity refinement framework for learning-based stereo matching methods in cross-domain setting for laparoscopic images

Zixin Yang, Cristian Linte, Richard Simon

Proceedings Volume 12466, 1246607 (2023) https://doi.org/10.1117/12.2654804

KEYWORDS: Education and training, Laparoscopy, Visualization, Endoscopy, Light sources and illumination, Biomedical applications, Error analysis, Surgery, Cameras, Windows

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Proceedings Article | 3 April 2023 Poster + Paper

Investigating the impact of class-dependent label noise in medical image classification

Bidur Khanal, S.M. Kamrul Hasan, Bishesh Khanal, Cristian A. Linte

Proceedings Volume 12464, 1246437 (2023) https://doi.org/10.1117/12.2654420

KEYWORDS: Medical imaging, Image classification, Education and training, Visualization, Performance modeling, Visual process modeling, Image segmentation, Data modeling, Image resolution, Histopathology

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SPIE Journal Paper | 18 April 2022 Open Access

SPIE Medical Imaging 50th anniversary: historical review of the Image-Guided Procedures, Robotic Interventions, and Modeling conference

Jeffrey Siewerdsen, Cristian Linte

JMI, Vol. 9, Issue S1, 012206, (April 2022) https://doi.org/10.1117/12.10.1117/1.JMI.9.S1.012206

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Proceedings Article | 4 April 2022 Presentation + Paper

Pairwise versus transitive inverse consistent longitudinal rigid registration of magnetic resonance images of athletes with repetitive non-concussive head injuries: effects on regional distributions of diffusion measures

Kian Merchant-Borna, Jeffrey Bazarian, Cristian Linte, Nathan Cahill, Harshkumar Prajapati

Proceedings Volume 12036, 1203605 (2022) https://doi.org/10.1117/12.2613105

KEYWORDS: Diffusion, Rigid registration, Diffusion weighted imaging, Injuries, Head, Magnetism, Anisotropy, Brain, Magnetic resonance imaging, Diffusion tensor imaging

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Proceedings Article | 4 April 2022 Paper

A weakly supervised learning approach for surgical instrument segmentation from laparoscopic video sequences

Cristian Linte, Richard Simon, Zixin Yang

Proceedings Volume 12034, 120341U (2022) https://doi.org/10.1117/12.2610778

KEYWORDS: Image segmentation, Video, Machine learning, Laparoscopy, Binary data, Video processing, Robotics, Neural networks, Computer programming, RGB color model

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Proceedings Article | 4 April 2022 Presentation + Paper

Calibration of cine MRI segmentation probability for uncertainty estimation using a multi-task cross-task learning architecture

Cristian Linte, S. M. Kamrul Hasan

Proceedings Volume 12034, 120340T (2022) https://doi.org/10.1117/12.2612269

KEYWORDS: Uncertainty analysis, Image segmentation, Magnetic resonance imaging, Calibration, Medical imaging, Data modeling, Neural networks, Error analysis

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Proceedings Article | 4 April 2022 Poster + Paper

Assessing and reducing registration and targeting uncertainty in video-based image-guided renal navigation: an in vitro study featuring virtual targets

Peter Jackson, Kelly Merrell, Richard Simon, Cristian Linte

Proceedings Volume 12034, 120342W (2022) https://doi.org/10.1117/12.2613457

KEYWORDS: Cameras, Image registration, Kidney, Video, Navigation systems, Imaging systems, Laparoscopy, Virtual reality

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Proceedings Article | 15 February 2021 Presentation

Welcome and Introduction to SPIE Conference 11598

Cristian Linte, Jeffrey Siewerdsen

Proceedings Volume 11598, 1159802 (2021) https://doi.org/10.1117/12.2592823

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Proceedings Article | 15 February 2021 Presentation + Paper

Effect of uncertainty on target registration error in image-guided renal interventions: from simulation to in-vitro assessment

Richard Simon, Cristian Linte, Peter Jackson

Proceedings Volume 11598, 115980I (2021) https://doi.org/10.1117/12.2581854

KEYWORDS: Image registration, In vitro testing, Navigation systems, Optical tracking, Image-guided intervention, Image restoration, Error analysis, Calibration, Kidney, 3D acquisition

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It is imperative that image-guided intervention (IGI) systems provide accurate and precise navigation information to enable the user to trust the system and not place unwarranted confidence in the guidance capabilities of the system. Unfortunately, the actual error associated with the overall targeting capabilities of an IGI system is not readily known. Here we are primarily interested in the application of image-guided surgery in the context of renal interventions. We built a simulation pipeline to study the uncertainty propagation through an optically tracked IGI system to gain insight into the overall accuracy of the system. Our simulation pipeline models several stages, including stylus calibration, tool tracking, patient tracking, and image to patient registration. In the effort to realistically estimate tracking noise and user-associated fiducial localization error (FLE), we conducted several experiments using the optical tracking system. Our simulation suggested that a wider cone angle results in a more accurate tool calibration, which improves further with the collection of additional samples. Furthermore, our simulations also suggested that the image-to-patient registration was the most significant contributor to navigation uncertainty, followed by the fiducial localization error. Lastly, we also observed a 0.72 correlation between the Target Registration Error (TRE) estimated at target fiducials and the distance between the the centroids of the registration and target fiducial landmarks. To validate the simulation predictions, we also conducted several in vitro experiments using a 3D printed patient specific kidney phantom and compared the simulation-based registration predictions with those observed experimentally in vitro. The experiments confirmed the registration metrics (Fiducial Registration Error and TRE) predicted by the simulations, given several specific combinations of fiducial landmarks used to perform the image to patient registration.

Proceedings Article | 15 February 2021 Presentation + Paper

Joint deep learning framework for image registration and segmentation of late gadolinium enhanced MRI and cine cardiac MRI

Roshan Reddy Upendra, Richard Simon, Cristian Linte

Proceedings Volume 11598, 115980F (2021) https://doi.org/10.1117/12.2581386

KEYWORDS: Image segmentation, Cardiovascular magnetic resonance imaging, Image registration, Magnetic resonance imaging, Gadolinium, Image enhancement, Data modeling, Tissues, Performance modeling, Network architectures

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Late gadolinium enhanced (LGE) cardiac magnetic resonance (CMR) imaging, the current benchmark for assessment of myocardium viability, enables the identification and quantification of the compromised myocardial tissue regions, as they appear hyper-enhanced compared to the surrounding, healthy myocardium. However, in LGE CMR images, the reduced contrast between the left ventricle (LV) myocardium and LV blood-pool hampers the accurate delineation of the LV myocardium. On the other hand, the balanced-Steady State Free Precession (bSSFP) cine CMR imaging provides high resolution images ideal for accurate segmentation of the cardiac chambers. In the interest of generating patient-specific hybrid 3D and 4D anatomical models of the heart, to identify and quantify the compromised myocardial tissue regions for revascularization therapy planning, in our previous work, we presented a spatial transformer network (STN) based convolutional neural network (CNN) architecture for registration of LGE and bSSFP cine CMR image datasets made available through the 2019 Multi-Sequence Cardiac Magnetic Resonance Segmentation Challenge (MS-CMRSeg). We performed a supervised registration by leveraging the region of interest (RoI) information using the manual annotations of the LV blood-pool, LV myocardium and right ventricle (RV) blood-pool provided for both the LGE and the bSSFP cine CMR images. In order to reduce the reliance on the number of manual annotations for training such network, we propose a joint deep learning framework consisting of three branches: a STN based RoI guided CNN for registration of LGE and bSSFP cine CMR images, an U-Net model for segmentation of bSSFP cine CMR images, and an U-Net model for segmentation of LGE CMR images. This results in learning of a joint multi-scale feature encoder by optimizing all three branches of the network architecture simultaneously. Our experiments show that the registration results obtained by training 25 of the available 45 image datasets in a joint deep learning framework is comparable to the registration results obtained by stand-alone STN based CNN model by training 35 of the available 45 image datasets and also shows significant improvement in registration performance when compared to the results achieved by the stand-alone STN based CNN model by training 25 of the available 45 image datasets.

Proceedings Article | 15 February 2021 Presentation + Paper

Segmentation and removal of surgical instruments for background scene visualization from endoscopic/laparoscopic video

S. M. Kamrul Hasan, Richard Simon, Cristian Linte

Proceedings Volume 11598, 115980A (2021) https://doi.org/10.1117/12.2580668

KEYWORDS: Video, Visualization, Laparoscopy, Endoscopy, Tissues, Convolution, Surgery, Robotics, Injuries, Computer programming

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Proceedings Article | 16 March 2020 Paper

Automated segmentation of cardiac chambers from cine cardiac MRI using an adversarial network architecture

Roshan Reddy Upendra, Shusil Dangi, Cristian Linte

Proceedings Volume 11315, 113152Y (2020) https://doi.org/10.1117/12.2550656

KEYWORDS: Image segmentation, Network architectures, Cardiovascular magnetic resonance imaging, Magnetic resonance imaging, Medical imaging, Convolutional neural networks

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Proceedings Article | 16 March 2020 Presentation + Paper

CondenseUNet: a memory-efficient condensely-connected architecture for bi-ventricular blood pool and myocardium segmentation

Cristian Linte, S. M. Kamrul Hasan

Proceedings Volume 11315, 113151J (2020) https://doi.org/10.1117/12.2550640

KEYWORDS: Image segmentation, Convolution, Magnetic resonance imaging, Heart, Network architectures, Medical imaging

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Proceedings Article | 16 March 2020 Presentation + Paper

Combining statistical shape model and principal component analysis to estimate left ventricular volume and ejection fraction

Dawei Liu, Shusil Dangi, Karl Schwarz, Cristian Linte

Proceedings Volume 11319, 113190E (2020) https://doi.org/10.1117/12.2550650

KEYWORDS: Principal component analysis, Mahalanobis distance, 3D modeling, Statistical modeling, Shape analysis, Statistical analysis, Tomography, 3D image processing, Reconstruction algorithms

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Left ventricular ejection fraction (LVEF) assessment is instrumental for cardiac health diagnosis, patient management, and patient eligibility for participation in clinical studies. Due to its non-invasiveness and low operational cost, ultrasound (US) imaging is the most commonly used imaging modality to image the heart and assess LVEF. Even though 3D US imaging technology is becoming more available, cardiologists dominantly use 2D US imaging to visualize the LV blood pool and interpret its area changes between end-systole and end-diastole. Our previous work showed that LVEF estimates based on area changes are significantly lower than the true volume-based estimates by as much as 13%,1 which could lead to unnecessary and costly therapeutic decisions. Acquiring volumetric information about the LV blood pool necessitates either time-consuming 3D reconstruction or 3D US image acquisition. Here, we propose a method that leverages on a statistical shape model (SSM) constructed from 13 landmarks depicting the LV endocardial border to estimate a new patient’s LV volume and LVEF. Two methods to estimate the 3D LV geometry with and without size normalization were employed. The SSM was built using the 13 landmarks from 50 training patient image datasets. Subsequently, the Mahalanobis distance (with size normalization) or the vector distance (without size normalization) between an incoming patient’s LV landmarks and each shape in the SSM were used to determine the weights each training patient contributed to describing the new, incoming patient’s LV geometry and associated blood pool volume. We tested the pro- posed method to estimate the LV volumes and LVEF for 16 new test patients. The estimated LVEFs based on Mahalanobis distance and vector distance were within 2.9% and 1.1%, respectively, of the ground truth LVEFs calculated from the 3D reconstructed LV volumes. Furthermore, the viability of using fewer principal components (PCs) to estimate the LV volume was explored by reducing the number of PCs retained when projecting landmarks onto PCA space. LVEF estimated based on 3 PCs, 5 PCs, and 10 PCs are within 6.6%, 5.4%, and 3.3%, respectively, of LVEF estimates using the full set of 39 PCs.

Proceedings Article | 5 March 2020 Paper

Noise reduction in the inverse solution for one-dimensional cardiac active stress reconstruction

Minyao Li, Niels Otani, Vignesh Venkataramani, Cristian Linte

Proceedings Volume 11317, 113172E (2020) https://doi.org/10.1117/12.2550600

KEYWORDS: Inverse problems, Interference (communication), Signal to noise ratio, Denoising, Action potentials, Tissues, Magnetic resonance imaging, Data modeling, Wave propagation

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Proceedings Article | 5 March 2020 Presentation + Paper

An implementation of data assimilation techniques for transmural visualization of action potential propagation in cardiac tissue

Christopher Beam, Cristian Linte, Niels Otani

Proceedings Volume 11317, 113171L (2020) https://doi.org/10.1117/12.2550467

KEYWORDS: Inverse problems, Tissues, Filtering (signal processing), Action potentials, Error analysis, Data modeling, Visualization, Motion measurement, Wave propagation, Systems modeling

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Proceedings Article | 28 February 2020 Presentation + Paper

A machine leaning approach for abdominal aortic aneurysm severity assessment using geometric, biomechanical, and patient-specific historical clinical features

Golnaz Jalalahmadi, María Helguera, Cristian Linte

Proceedings Volume 11317, 1131713 (2020) https://doi.org/10.1117/12.2549277

KEYWORDS: Machine learning, Feature selection, Data modeling, Aneurysms, Performance modeling, 3D modeling, Feature extraction, Surgery, Finite element methods

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Proceedings Article | 15 March 2019 Presentation + Paper

Left ventricular ejection fraction assessment: unraveling the bias between area- and volume-based estimates

Dawei Liu, Isabelle Peck, Shusil Dangi, Karl Schwarz, Cristian Linte

Proceedings Volume 10955, 109550T (2019) https://doi.org/10.1117/12.2514388

KEYWORDS: Data modeling, 3D modeling, Mathematical modeling, Visualization, 3D image reconstruction, 3D image processing, Ultrasonography, Heart

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Calculating left ventricular ejection fraction (LVEF) accurately is crucial for the clinical diagnosis of cardiac disease, patient management, or other therapeutic treatment decisions. The measure of a patient's LVEF often affects their candidacy for cardiovascular intervention. Ultrasound (US) is one of the imaging modalities used to non-invasively assess LVEF, and it is the most common and least expensive. Despite the advances in 3D US transducer technology, only limited US machines are equipped with such transducer to enable true 3D US image acquisition. Thus, 2D US images remain to be widely used by cardiologists to image the heart and their interpretation is inherently based on two dimensional information immediately available in the US images. Past knowledge indicates that visual estimation of the LVEF based on the area changes of the left ventricle blood pool between systole and diastole (as depicted in 2D ultrasound images) may significantly underestimate the ejection fraction, rendering some patients as suitable candidates for potentially unnecessary interventions or implantation of assistive devices. True LVEF should be calculated based on changes in LV volumes, but equipment and time constraint limit the current technique to assess 3D LV geometry. The estimation of the systolic and diastolic blood pool volumes requires additional work beyond a simple visual assessment of the blood pool area changed in the 2D US images. Specifically, following the manual segmentation of the endocardial LV border, 3D volume would be assessed by reconstructing a LV volume from multiple tomographic views. In this work, we leverage on two idealized mathematical models of the left ventricle | a truncated prolate spheroid (TPS) and a paraboloid geometric model to characterize the LV shape according to the range of possible dimensions gathered from our patient-specific multi-plane US imaging data. The objective of this work is to reveal the necessity of calculating LVEFs based on volumes by showing that LVEF estimated using area changes underestimate the LVEF computed using volume changes. Additionally, we present a method to reconstruct the LV volume from 2D blood pool representations identified in the multi-plane 2D US images and use the reconstructed 3D volume throughout the cardiac cycle to estimate the LVEF. Our preliminary results show that the area-based LVEF significantly underestimates the true volume-based LVEF across both the theoretical simulations using idealized geometric models of the LV shape, as well as the patient-specific US imaging data. Specifically, both the TPS and paraboloid model showed an area-based LVEF of 41:3±4:7% and a volume-based LVEF of 55:4±5:7%, while the US image data showed an area-based LVEF of 34:7 ± 11:9% and a volume-based LVEF of 48:0 ± 14:0%. In summary, the area-based LVEF estimations using both the idealized TPS and paraboloid models was 14.1% lower than volume- based LVEF calculations using corresponding models. Furthermore, the area-based LVEF based on reconstructed LV volumes are 13.3% lower than volume-based estimates. Evidently, there is a need to further investigate a method to enable practical volume-based LVEF calculations to avoid the need for clinicians to estimate LVEF based on visual, holistic assessment of the blood pool area changes that improperly infer volumetric blood pool changes.

Proceedings Article | 13 March 2018 Paper

Technical note: on cardiac ablation lesion visualization for image-guided therapy monitoring

Cristian Linte, Jon Camp, Maryam Rettmann, Dieter Haemmerich, Mehmet Aktas, David Huang, Douglas Packer, David Holmes

Proceedings Volume 10576, 105760N (2018) https://doi.org/10.1117/12.2322523

KEYWORDS: Tissues, Visualization, Visual process modeling, Performance modeling, Injuries, Temperature metrology, Thermal modeling, Electrodes, Systems modeling, Statistical modeling

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Proceedings Article | 12 March 2018 Presentation + Paper

Toward modeling the effects of regional material properties on the wall stress distribution of abdominal aortic aneurysms

Golnaz Jalalahmadi, María Helguera, Cristian Linte, Doran Mix

Proceedings Volume 10578, 105780I (2018) https://doi.org/10.1117/12.2294558

KEYWORDS: 3D modeling, Aneurysms, Computed tomography, Surgery, Finite element methods, Image segmentation, Aorta, 3D image processing

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Proceedings Article | 12 March 2018 Presentation + Paper

Cine cardiac MRI slice misalignment correction towards full 3D left ventricle segmentation

Shusil Dangi, Ziv Yaniv, Cristian Linte

Proceedings Volume 10576, 1057607 (2018) https://doi.org/10.1117/12.2294936

KEYWORDS: Image segmentation, Magnetic resonance imaging, 3D modeling, Cardiovascular magnetic resonance imaging, Image registration, Image processing algorithms and systems, Medical imaging

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SPIE Journal Paper | 1 March 2018

Lesion modeling, characterization, and visualization for image-guided cardiac ablation therapy monitoring

Cristian Linte, Jon Camp, Maryam Rettmann, Dieter Haemmerich, Mehmet Aktas, David Huang, Douglas Packer, David Holmes

JMI, Vol. 5, Issue 02, 021218, (March 2018) https://doi.org/10.1117/12.10.1117/1.JMI.5.2.021218

KEYWORDS: Tissues, Electrodes, Visualization, Visual process modeling, Temperature metrology, Natural surfaces, Injuries, Performance modeling, Statistical modeling, Thermal modeling

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Proceedings Article | 13 March 2017 Paper

3D printing for orthopedic applications: from high resolution cone beam CT images to life size physical models

Cristian Linte, Amiee Jackson, Shusil Dangi, Yehuda Ben-Zikri, Lawrence Ray

Proceedings Volume 10138, 101380T (2017) https://doi.org/10.1117/12.2256181

KEYWORDS: 3D modeling, Computed tomography, 3D printing, Process modeling, Printing, Image resolution, Cones, Stereolithography, Data modeling, Imaging informatics, Bone, Fused deposition modeling, Image segmentation

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Proceedings Article | 13 March 2017 Paper

A numerical framework for studying the biomechanical behavior of abdominal aortic aneurysm

Golnaz Jalalahmadi, Cristian Linte, María Helguera

Proceedings Volume 10137, 101372A (2017) https://doi.org/10.1117/12.2254528

KEYWORDS: Magnetic resonance imaging, Tissues, Process modeling, Data modeling, Finite element methods, 3D modeling, Image segmentation, 3D image processing, Arteries, Computed tomography

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Proceedings Article | 13 March 2017 Presentation + Paper

Anatomical based registration of multi-sector x-ray images for panorama reconstruction

Yehuda Kfir Ben-Zikri, Stacy Mendez, Cristian Linte

Proceedings Volume 10137, 101370H (2017) https://doi.org/10.1117/12.2256047

KEYWORDS: Image registration, X-ray imaging, X-rays, Panoramic photography, Bone, Edge detection, Image processing, Feature extraction, Surgery, Digital x-ray imaging, Image segmentation, Image filtering, Data modeling, Transparency

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Accurate measurement of long limb alignment is an essential stage of the pre-operative planning of realignment surgery. This alignment is quantified according to the hip-knee-ankle (HKA) angle of the mechanical axis of the lower extremity and is measured based on a full-length weight-bearing X-ray or standard computed radiography (CR) image of the patient in standing position. Due to the limited field-of-view of the traditionally employed digital X-ray imaging systems, several sector images are required to capture the posture of a standing individual. These sector images need to then be stitched" together to reconstruct the standing posture. To eliminate user-induced variability and time constraints associated with the traditional manual "stitching" protocol, we have created an image processing application to automate the stitching process, when there are no reliable external markers available in the images, by only relying on the most reliable anatomical content of the image. The application starts with a rough segmentation of the tibia and the sector images are then registered by evaluating the DICE coefficient between the edges of these corresponding bones along the medial edge. The identified translations are then used to register the original sector images into the standing panorama image. To test the robustness of our method, we randomly selected 40 datasets from a variant database consisting of nearly 100 patient X-ray images acquired for patient screening as part of a multi-site clinical trial. The resulting horizontal and vertical translation values from the automated registration were compared to the homologous translations recorded during the manual panorama generation conducted by a knowledgeable X-ray imaging technician. The mean and standard deviation of the differences for the horizontal translation parameters was -0:27±1:14 mm and 0:31±1:86 mm for the left and right tibia, respectively. The vertical translation differences for the left and right tibia were 1:05±5:24 mm and 1:32±4:77 mm, respectively. For these differences, the expert radiologist reported no difference in the hip-knee-ankle angular assessment.

Proceedings Article | 10 March 2017 Paper

An interactive, stereoscopic virtual environment for medical imaging visualization, simulation and training

Erik Messier, Evan Krueger, Gabriel Diaz, Cristian Linte

Proceedings Volume 10136, 101361H (2017) https://doi.org/10.1117/12.2256081

KEYWORDS: Visualization, Medical imaging, 3D displays, 3D image processing, Virtual reality, Stereoscopic displays, Image visualization, 3D visualizations, 3D image reconstruction, 3D acquisition, 3D modeling, Glasses, Surgery, Control systems

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Recent advances in medical image acquisition allow for the reconstruction of anatomies with 3D, 4D, and 5D renderings. Nevertheless, standard anatomical and medical data visualization still relies heavily on the use of traditional 2D didactic tools (i.e., textbooks and slides), which restrict the presentation of image data to a 2D slice format. While these approaches have their merits beyond being cost effective and easy to disseminate, anatomy is inherently three-dimensional. By using 2D visualizations to illustrate more complex morphologies, important interactions between structures can be missed. In practice, such as in the planning and execution of surgical interventions, professionals require intricate knowledge of anatomical complexities, which can be more clearly communicated and understood through intuitive interaction with 3D volumetric datasets, such as those extracted from high-resolution CT or MRI scans. Open source, high quality, 3D medical imaging datasets are freely available, and with the emerging popularity of 3D display technologies, affordable and consistent 3D anatomical visualizations can be created. In this study we describe the design, implementation, and evaluation of one such interactive, stereoscopic visualization paradigm for human anatomy extracted from 3D medical images. A stereoscopic display was created by projecting the scene onto the lab floor using sequential frame stereo projection and viewed through active shutter glasses. By incorporating a PhaseSpace motion tracking system, a single viewer can navigate an augmented reality environment and directly manipulate virtual objects in 3D. While this paradigm is sufficiently versatile to enable a wide variety of applications in need of 3D visualization, we designed our study to work as an interactive game, which allows users to explore the anatomy of various organs and systems. In this study we describe the design, implementation, and evaluation of an interactive and stereoscopic visualization platform for exploring and understanding human anatomy. This system can present medical imaging data in three dimensions and allows for direct physical interaction and manipulation by the viewer. This should provide numerous benefits over traditional, 2D display and interaction modalities, and in our analysis, we aim to quantify and qualify users' visual and motor interactions with the virtual environment when employing this interactive display as a 3D didactic tool.

Proceedings Article | 3 March 2017 Presentation + Paper

Integrating atlas and graph cut methods for right ventricle blood-pool segmentation from cardiac cine MRI

Shusil Dangi, Cristian Linte

Proceedings Volume 10135, 1013519 (2017) https://doi.org/10.1117/12.2256013

KEYWORDS: Signal to noise ratio, Image processing algorithms and systems, Data modeling, Transform theory

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Proceedings Article | 1 April 2016 Paper

Experimental characterization, comparison and image quality assessment of two ultrasound contrast agents: Optison and Definity

Cristian Linte, Amy Hughes, Karl Schwarz, Steven Day

Proceedings Volume 9790, 97900H (2016) https://doi.org/10.1117/12.2217741

KEYWORDS: Ultrasonography, Tissues, Image quality, Blood, Visualization, Backscatter, Image acquisition, In vitro testing, Doppler effect, Signal detection, Sensors

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Proceedings Article | 29 March 2016 Paper

Automated reconstruction of standing posture panoramas from multi-sector long limb x-ray images

Cristian Linte, Linzey Miller, Caroline Trier, Yehuda Ben-Zikri

Proceedings Volume 9788, 97880B (2016) https://doi.org/10.1117/12.2217742

KEYWORDS: Image registration, Panoramic photography, Image segmentation, Image processing, Edge detection, Bone, Digital x-ray imaging, Image filtering, Binary data

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Proceedings Article | 18 March 2016 Paper

A comparison study of atlas-based 3D cardiac MRI segmentation: global versus global and local transformations

Cristian Linte, Aditya Daryanani, Shusil Dangi, Yehuda Kfir Ben-Zikri

Proceedings Volume 9786, 978624 (2016) https://doi.org/10.1117/12.2217883

KEYWORDS: Image segmentation, Cardiovascular magnetic resonance imaging, Image registration, Heart, Electronic filtering, Statistical analysis, 3D modeling

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Proceedings Article | 18 March 2016 Paper

A robust automated left ventricle region of interest localization technique using a cardiac cine MRI atlas

Cristian Linte, Yehuda Kfir Ben-Zikri

Proceedings Volume 9786, 97862T (2016) https://doi.org/10.1117/12.2217885

KEYWORDS: Image segmentation, Magnetic resonance imaging, Image registration, Medical imaging, Image analysis, Image processing, Computer aided diagnosis and therapy, Visualization, Image enhancement, Heart, Image quality, Expectation maximization algorithms

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Region of interest detection is a precursor to many medical image processing and analysis applications, including segmentation, registration and other image manipulation techniques. The optimal region of interest is often selected manually, based on empirical knowledge and features of the image dataset. However, if inconsistently identified, the selected region of interest may greatly affect the subsequent image analysis or interpretation steps, in turn leading to incomplete assessment during computer-aided diagnosis or incomplete visualization or identification of the surgical targets, if employed in the context of pre-procedural planning or image-guided interventions. Therefore, the need for robust, accurate and computationally efficient region of interest localization techniques is prevalent in many modern computer-assisted diagnosis and therapy applications. Here we propose a fully automated, robust, a priori learning-based approach that provides reliable estimates of the left and right ventricle features from cine cardiac MR images. The proposed approach leverages the temporal frame-to-frame motion extracted across a range of short axis left ventricle slice images with small training set generated from les than 10% of the population. This approach is based on histogram of oriented gradients features weighted by local intensities to first identify an initial region of interest depicting the left and right ventricles that exhibits the greatest extent of cardiac motion. This region is correlated with the homologous region that belongs to the training dataset that best matches the test image using feature vector correlation techniques. Lastly, the optimal left ventricle region of interest of the test image is identified based on the correlation of known ground truth segmentations associated with the training dataset deemed closest to the test image. The proposed approach was tested on a population of 100 patient datasets and was validated against the ground truth region of interest of the test images manually annotated by experts. This tool successfully identified a mask around the LV and RV and furthermore the minimal region of interest around the LV that fully enclosed the left ventricle from all testing datasets, yielding a 98% overlap with their corresponding ground truth. The achieved mean absolute distance error between the two contours that normalized by the radius of the ground truth is 0.20 ± 0.09.

Proceedings Article | 18 March 2015 Paper

The effect of elastic modulus on ablation catheter contact area

David Holmes, Jon Camp, Cristian Linte, Maryam Rettmann, Deyu Sun, Douglas Packer, Richard Robb

Proceedings Volume 9415, 941506 (2015) https://doi.org/10.1117/12.2083122

KEYWORDS: Tissues, Heart, Image quality, Ultrasonography, Imaging systems, Image segmentation, Real time imaging, Electrodes, Visualization, Magnetism

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Proceedings Article | 18 March 2015 Paper

Endocardial left ventricle feature tracking and reconstruction from tri-plane trans-esophageal echocardiography data

Cristian Linte, Shusil Dangi, Yehuda Ben-Zikri, Karl Schwarz, Nathan Cahill

Proceedings Volume 9415, 941505 (2015) https://doi.org/10.1117/12.2082541

KEYWORDS: Image segmentation, 3D image processing, 3D acquisition, Image enhancement, Image acquisition, Image analysis, Echocardiography, Real time imaging, Image registration, Image processing

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Two-dimensional (2D) ultrasound (US) has been the clinical standard for over two decades for monitoring and assessing cardiac function and providing support via intra-operative visualization and guidance for minimally invasive cardiac interventions. Developments in three-dimensional (3D) image acquisition and transducer design and technology have revolutionized echocardiography imaging enabling both real-time 3D trans-esophageal and intra-cardiac image acquisition. However, in most cases the clinicians do not access the entire 3D image volume when analyzing the data, rather they focus on several key views that render the cardiac anatomy of interest during the US imaging exam. This approach enables image acquisition at a much higher spatial and temporal resolution. Two such common approaches are the bi-plane and tri-plane data acquisition protocols; as their name states, the former comprises two orthogonal image views, while the latter depicts the cardiac anatomy based on three co-axially intersecting views spaced at 60⁰to one another. Since cardiac anatomy is continuously changing, the intra-operative anatomy depicted using real-time US imaging also needs to be updated by tracking the key features of interest and endocardial left ventricle (LV) boundaries. Therefore, rapid automatic feature tracking in US images is critical for three reasons: 1) to perform cardiac function assessment; 2) to identify location of surgical targets for accurate tool to target navigation and on-target instrument positioning; and 3) to enable pre- to intra-op image registration as a means to fuse pre-op CT or MR images used during planning with intra-operative images for enhanced guidance. In this paper we utilize monogenic filtering, graph-cut based segmentation and robust spline smoothing in a combined work flow to process the acquired tri-plane TEE time series US images and demonstrate robust and accurate tracking of the LV endocardial features. We reconstruct the endocardial LV geometry using the tri-plane contours and spline interpolation, and assess the accuracy of the proposed work flow against gold-standard results from the GE Echopac PC clinical software according to quantitative clinical LV characterization parameters, such as the length, circumference, area and volume. Our proposed combined work flow leads to consistent, rapid and automated identification of the LV endocardium, suitable for intra-operative applications and "on-the-fly" computer-assisted assessment of ejection fraction for cardiac function monitoring.Two-dimensional (2D) ultrasound (US) has been the clinical standard for over two decades for monitoring and assessing cardiac function and providing support via intra-operative visualization and guidance for minimally invasive cardiac interventions. Developments in three-dimensional (3D) image acquisition and transducer design and technology have revolutionized echocardiography imaging enabling both real-time 3D trans-esophageal and intra-cardiac image acquisition. However, in most cases the clinicians do not access the entire 3D image volume when analyzing the data, rather they focus on several key views that render the cardiac anatomy of interest during the US imaging exam. This approach enables image acquisition at a much higher spatial and temporal resolution. Two such common approaches are the bi-plane and tri-plane data acquisition protocols; as their name states, the former comprises two orthogonal image views, while the latter depicts the cardiac anatomy based on three co-axially intersecting views spaced at 600 to one another. Since cardiac anatomy is continuously changing, the intra-operative anatomy depicted using real-time US imaging also needs to be updated by tracking the key features of interest and endocardial left ventricle (LV) boundaries. Therefore, rapid automatic feature tracking in US images is critical for three reasons: 1) to perform cardiac function assessment; 2) to identify location of surgical targets for accurate tool to target navigation and on-target instrument positioning; and 3) to enable pre- to intra-op image registration as a means to fuse pre-op CT or MR images used during planning with intra-operative images for enhanced guidance. In this paper we utilize monogenic filtering, graph-cut based segmentation and robust spline smoothing in a combined work flow to process the acquired tri-plane TEE time series US images and demonstrate robust and accurate tracking of the LV endocardial features. We reconstruct the endocardial LV geometry using the tri-plane contours and spline interpolation, and assess the accuracy of the proposed work flow against gold-standard results from the GE Echopac PC clinical software according to quantitative clinical LV characterization parameters, such as the length, circumference, area and volume. Our proposed combined work flow leads to consistent, rapid and automated identification of the LV endocardium, suitable for intra-operative applications and on-the- y" computer-assisted assessment of ejection fraction for cardiac function monitoring.

Proceedings Article | 17 March 2015 Paper

Augmenting real-time video with virtual models for enhanced visualization for simulation, teaching, training and guidance

Cristian Linte, Alexander Bensch, Michael Potter, Alexander Dawson-Elli

Proceedings Volume 9416, 941615 (2015) https://doi.org/10.1117/12.2082545

KEYWORDS: Cameras, Visualization, Video, Calibration, Virtual reality, Autoregressive models, Visual process modeling, Augmented reality, Computer simulations, Optical tracking

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Proceedings Article | 12 March 2014 Paper

Toward standardized mapping for left atrial analysis and cardiac ablation guidance

M. Rettmann, D. Holmes, C. Linte, D. Packer, R. Robb

Proceedings Volume 9036, 90361K (2014) https://doi.org/10.1117/12.2043191

KEYWORDS: Veins, Cameras, Image segmentation, Ray tracing, Associative arrays, Electrophysiology, Atrial fibrillation, Heart, Visualization, Standards development

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Proceedings Article | 12 March 2014 Paper

Virtual estimates of fastening strength for pedicle screw implantation procedures

Cristian Linte, Jon Camp, Kurt Augustine, Paul Huddleston, David Holmes, Richard Robb

Proceedings Volume 9036, 903617 (2014) https://doi.org/10.1117/12.2044097

KEYWORDS: Bone, Spine, Surgery, Minerals, Image segmentation, 3D image processing, Computed tomography, Image registration, Nerve, Rigid registration

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Traditional 2D images provide limited use for accurate planning of spine interventions, mainly due to the complex 3D anatomy of the spine and close proximity of nerve bundles and vascular structures that must be avoided during the procedure. Our previously developed clinician-friendly platform for spine surgery planning takes advantage of 3D pre-operative images, to enable oblique reformatting and 3D rendering of individual or multiple vertebrae, interactive templating, and placement of virtual pedicle implants. Here we extend the capabilities of the planning platform and demonstrate how the virtual templating approach not only assists with the selection of the optimal implant size and trajectory, but can also be augmented to provide surrogate estimates of the fastening strength of the implanted pedicle screws based on implant dimension and bone mineral density of the displaced bone substrate. According to the failure theories, each screw withstands a maximum holding power that is directly proportional to the screw diameter (D), the length of the in-bone segm,ent of the screw (L), and the density (i.e., bone mineral density) of the pedicle body. In this application, voxel intensity is used as a surrogate measure of the bone mineral density (BMD) of the pedicle body segment displaced by the screw. We conducted an initial assessment of the developed platform using retrospective pre- and post-operative clinical 3D CT data from four patients who underwent spine surgery, consisting of a total of 26 pedicle screws implanted in the lumbar spine. The Fastening Strength of the planned implants was directly assessed by estimating the intensity - area product across the pedicle volume displaced by the virtually implanted screw. For post-operative assessment, each vertebra was registered to its homologous counterpart in the pre-operative image using an intensity-based rigid registration followed by manual adjustment. Following registration, the Fastening Strength was computed for each displaced bone segment. According to our preliminary clinical study, a comparison between Fastening Strength, displaced bone volume and mean voxel intensity showed similar results (p < 0.1) between the virtually templated plans and the post-operative outcome following the traditional clinical approach. This study has demonstrated the feasibility of the platform in providing estimates the pedicle screw fastening strength via virtual implantation, given the intrinsic vertebral geometry and bone mineral density, enabling the selection of the optimal implant dimension adn trajectory for improved strength.

Proceedings Article | 12 March 2014 Paper

Updating a preoperative surface model with information from real-time tracked 2D ultrasound using a Poisson surface reconstruction algorithm

Maryam Rettmann, David Holmes, Cristian Linte, Douglas Packer, Richard Robb, Deyu Sun

Proceedings Volume 9036, 90361T (2014) https://doi.org/10.1117/12.2042510

KEYWORDS: Data modeling, 3D modeling, Ultrasonography, Clouds, Error analysis, Reconstruction algorithms, Computed tomography, Process modeling, Computer simulations, 3D image processing

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Proceedings Article | 15 March 2013 Paper

Segmentation of left atrial intracardiac ultrasound images for image guided cardiac ablation therapy

M. Rettmann, T. Stephens, D. Holmes, C. Linte, D. Packer, R. Robb

Proceedings Volume 8671, 86712D (2013) https://doi.org/10.1117/12.2008762

KEYWORDS: Image segmentation, Blood, Tissues, Ultrasonography, Statistical analysis, Data modeling, Error analysis, Heart, Echocardiography, Process modeling

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Proceedings Article | 8 March 2013 Paper

Image-based modeling and characterization of RF ablation lesions in cardiac arrhythmia therapy

Cristian Linte, Jon Camp, Maryam Rettmann, David Holmes, Richard Robb

Proceedings Volume 8671, 86710E (2013) https://doi.org/10.1117/12.2008529

KEYWORDS: Tissues, Temperature metrology, Electrodes, Natural surfaces, Visualization, 3D modeling, Fiber optics, Thermal modeling, Visual process modeling, Statistical modeling

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Proceedings Article | 17 February 2012 Paper

Calibration and evaluation of a magnetically tracked ICE probe for guidance of left atrial ablation therapy

Mia Gunawan, Shivaram Arunachalam, Richard Robb, David Holmes, Douglas Packer, Maryam Rettmann, Cristian Linte, Ben Dilger

Proceedings Volume 8316, 83162P (2012) https://doi.org/10.1117/12.912869

KEYWORDS: Calibration, Visualization, Optical tracking, 3D acquisition, 3D modeling, Transducers, 3D image processing, Optical spheres, Prototyping, Imaging systems

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The novel prototype system for advanced visualization for image-guided left atrial ablation therapy developed in our laboratory permits ready integration of multiple imaging modalities, surgical instrument tracking, interventional devices and electro-physiologic data. This technology allows subject-specific procedure planning and guidance using 3D dynamic, patient-specific models of the patient's heart, augmented with real-time intracardiac echocardiography (ICE). In order for the 2D ICE images to provide intuitive visualization for accurate catheter to surgical target navigation, the transducer must be tracked, so that the acquired images can be appropriately presented with respect to the patient-specific anatomy. Here we present the implementation of a previously developed ultrasound calibration technique for a magnetically tracked ICE transducer, along with a series of evaluation methods to ensure accurate imaging and faithful representation of the imaged structures. Using an engineering-designed phantom, target localization accuracy is assessed by comparing known target locations with their transformed locations inferred from the tracked US images. In addition, the 3D volume reconstruction accuracy is also estimated by comparing a truth volume to that reconstructed from sequential 2D US images. Clinically emulating validation studies are conducted using a patient-specific left atrial phantom. Target localization error of clinically-relevant surgical targets represented by nylon fiducials implanted within the endocardial wall of the phantom was assessed. Our studies have demonstrated 2.4 ± 0.8 mm target localization error in the engineering-designed evaluation phantoms, 94.8 ± 4.6 % volume reconstruction accuracy, and 3.1 ± 1.2 mm target localization error in the left atrial-mimicking phantom. These results are consistent with those disseminated in the literature and also with the accuracy constraints imposed by the employed technology and the clinical application.

Proceedings Article | 26 March 2011 Open Access

Ultrasound image guidance of cardiac interventions

Douglas Jones, Gérard Guiraudon, Danielle Pace, Cristian Linte, Terry Peters, Pencilla Lang

Proceedings Volume 7968, 79680T (2011) https://doi.org/10.1117/12.879843

KEYWORDS: Ultrasonography, Heart, 3D image processing, Visualization, Image fusion, 3D acquisition, Transducers, Image segmentation, Tissues, Echocardiography

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Proceedings Article | 27 February 2010 Paper

Estimating heart shift and morphological changes during minimally invasive cardiac interventions

Cristian Linte, Bob Kiaii, John Moore, Terry Peters, Danielle Pace, Chris Wedlake, Daniel S. Cho, Daniel Bainbridge, Mathew Carias

Proceedings Volume 7625, 762509 (2010) https://doi.org/10.1117/12.845579

KEYWORDS: Heart, Process modeling, Image registration, Image acquisition, Chest, Image segmentation, Computed tomography, Transducers, Visualization, Electrocardiography

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Proceedings Article | 13 March 2009 Paper

In vitro cardiac catheter navigation via augmented reality surgical guidance

Andrew Wiles, Jennifer Lo, Terry Peters, Chris Wedlake, Cristian Linte, John Moore

Proceedings Volume 7261, 72610O (2009) https://doi.org/10.1117/12.813829

KEYWORDS: Heart, Endoscopy, In vitro testing, Autoregressive models, Visualization, Augmented reality, Data modeling, Magnetic tracking, Real time imaging, Transducers

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Proceedings Article | 31 March 2008 Paper

From pre-operative cardiac modeling to intra-operative virtual environments for surgical guidance: an in vivo study

Marcin Wierzbicki, Daniel Bainbridge, John Moore, Terry Peters, Douglas Jones, Cristian Linte, Christopher Wedlake, Andrew Wiles, Gérard Guiraudon

Proceedings Volume 6918, 69180D (2008) https://doi.org/10.1117/12.772028

KEYWORDS: Heart, 3D modeling, Surgery, 3D image processing, Image segmentation, Virtual reality, Visualization, Magnetic resonance imaging, Image registration, In vivo imaging

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Proceedings Article | 17 March 2008 Paper

Object identification accuracy under ultrasound enhanced virtual reality for minimally invasive cardiac surgery

Anis Ahmad, Cristian Linte, Terry Peters, John Moore, Andrew Wiles, Christopher Wedlake

Proceedings Volume 6918, 69180E (2008) https://doi.org/10.1117/12.773178

KEYWORDS: Ultrasonography, Calibration, Transducers, Optical spheres, Virtual reality, Surgery, Spherical lenses, Error analysis, Heart, Visualization

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Proceedings Article | 3 April 2007 Paper

Navigation accuracy for an intracardiac procedure using ultrasound enhanced virtual reality

Cristian Linte, Andrew Wiles, Daniel Bainbridge, Christopher Wedlake, Gerard Guiraudon, Terry Peters, Douglas Jones, John Moore

Proceedings Volume 6509, 65090W (2007) https://doi.org/10.1117/12.711532

KEYWORDS: Ultrasonography, Virtual reality, Heart, Surgery, Magnetic tracking, Image quality, Calibration, Accuracy assessment, Imaging systems, Transducers

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Proceedings Article | 21 March 2007 Paper

An augmented reality environment for image-guidance of off-pump mitral valve implantation

John Moore, Chris Wedlake, Doug Jones, Gerard Guiraudon, Terry Peters, Nick Hill, Andrew Wiles, Daniel Bainbridge, Christian Linte

Proceedings Volume 6509, 65090N (2007) https://doi.org/10.1117/12.711041

KEYWORDS: Heart, Surgery, Virtual reality, Visualization, Endoscopy, 3D acquisition, 3D image processing, Transducers, Echocardiography, Tissues

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Proceedings Article | 13 March 2006 Paper

A method for myocardial contraction force reconstruction for tissue viability assessment

Cristian Linte, Terry Peters, Abbas Samani

Proceedings Volume 6143, 61432O (2006) https://doi.org/10.1117/12.655421

KEYWORDS: Tissues, Reconstruction algorithms, Heart, Algorithm development, Error analysis, Magnetic resonance imaging, 3D modeling, Inverse problems, Physiology, Computer simulations

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