Dr. Shusil Dangi Profile

Dr. Shusil Dangi

Senior Engineer

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Publications (8)

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

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 | 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 | 12 March 2018 Presentation + Paper

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

Shusil Dangi, Cristian Linte, Ziv Yaniv

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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Proceedings Article | 13 March 2017 Paper

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

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

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 | 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 | 18 March 2016 Paper

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

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

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 2015 Paper

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

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

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.

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