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R.D. van den Berg
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Cryogenic electron tomography (cryo-ET) is currently the golden standard for imaging cellular tissue at nanometer resolution. The current workflow from cultured cells to final image is however very time-consuming, labor-intensive, expensive, and has a low yield. Moreover, many of the steps in this workflow require practice. Even after practice, experienced users often lose samples during the cumbersome cryo-ET workflow.
Clipping of the Autogrid is one of the manual steps in the current procedure that is known to have a low yield. During this procedure, the fragile 20 µm thick sample carriers (TEM-grids) can get damaged or contaminated causing the loss of valuable samples. After clipping, the Autogrid increases the stiffness of the sample carrier, enabling automatic handling of the samples. Clipping of the Autogrid can therefore be considered as one of the missing links for full automation of the cryo-ET workflow. This thesis was aimed at automating the clipping procedure, thereby improving the yield, reducing the time required for practice, and reducing the amount of manual handling steps of the current procedure.
A problem analysis on the current procedure for clipping the Autogrid was performed, based on which multiple solutions were designed and tested experimentally. Designed gripper fingers were used with a six-axis industrial robot arm to automatically handle sample carriers and Autogrids. Such procedures are done manually using tweezers in the current procedure. Damage induced on the sample carriers during automatic handling was quantified experimentally.
Automating the clipping procedure allows for adjusting the orientation of sample carriers during the procedure, which is currently (almost) impossible. For this purpose, a machine learning-based marker detection algorithm was used to automatically detect markers that are present at the bottom of an Autogrid. This detection algorithm was used with a stepper motor and a designed mechanism to automatically obtain a specified orientation of the Autogrid. Finally, recommendations were given on how the proposed designs could be used in a final automated solution. ...
Clipping of the Autogrid is one of the manual steps in the current procedure that is known to have a low yield. During this procedure, the fragile 20 µm thick sample carriers (TEM-grids) can get damaged or contaminated causing the loss of valuable samples. After clipping, the Autogrid increases the stiffness of the sample carrier, enabling automatic handling of the samples. Clipping of the Autogrid can therefore be considered as one of the missing links for full automation of the cryo-ET workflow. This thesis was aimed at automating the clipping procedure, thereby improving the yield, reducing the time required for practice, and reducing the amount of manual handling steps of the current procedure.
A problem analysis on the current procedure for clipping the Autogrid was performed, based on which multiple solutions were designed and tested experimentally. Designed gripper fingers were used with a six-axis industrial robot arm to automatically handle sample carriers and Autogrids. Such procedures are done manually using tweezers in the current procedure. Damage induced on the sample carriers during automatic handling was quantified experimentally.
Automating the clipping procedure allows for adjusting the orientation of sample carriers during the procedure, which is currently (almost) impossible. For this purpose, a machine learning-based marker detection algorithm was used to automatically detect markers that are present at the bottom of an Autogrid. This detection algorithm was used with a stepper motor and a designed mechanism to automatically obtain a specified orientation of the Autogrid. Finally, recommendations were given on how the proposed designs could be used in a final automated solution. ...
Cryogenic electron tomography (cryo-ET) is currently the golden standard for imaging cellular tissue at nanometer resolution. The current workflow from cultured cells to final image is however very time-consuming, labor-intensive, expensive, and has a low yield. Moreover, many of the steps in this workflow require practice. Even after practice, experienced users often lose samples during the cumbersome cryo-ET workflow.
Clipping of the Autogrid is one of the manual steps in the current procedure that is known to have a low yield. During this procedure, the fragile 20 µm thick sample carriers (TEM-grids) can get damaged or contaminated causing the loss of valuable samples. After clipping, the Autogrid increases the stiffness of the sample carrier, enabling automatic handling of the samples. Clipping of the Autogrid can therefore be considered as one of the missing links for full automation of the cryo-ET workflow. This thesis was aimed at automating the clipping procedure, thereby improving the yield, reducing the time required for practice, and reducing the amount of manual handling steps of the current procedure.
A problem analysis on the current procedure for clipping the Autogrid was performed, based on which multiple solutions were designed and tested experimentally. Designed gripper fingers were used with a six-axis industrial robot arm to automatically handle sample carriers and Autogrids. Such procedures are done manually using tweezers in the current procedure. Damage induced on the sample carriers during automatic handling was quantified experimentally.
Automating the clipping procedure allows for adjusting the orientation of sample carriers during the procedure, which is currently (almost) impossible. For this purpose, a machine learning-based marker detection algorithm was used to automatically detect markers that are present at the bottom of an Autogrid. This detection algorithm was used with a stepper motor and a designed mechanism to automatically obtain a specified orientation of the Autogrid. Finally, recommendations were given on how the proposed designs could be used in a final automated solution.
Clipping of the Autogrid is one of the manual steps in the current procedure that is known to have a low yield. During this procedure, the fragile 20 µm thick sample carriers (TEM-grids) can get damaged or contaminated causing the loss of valuable samples. After clipping, the Autogrid increases the stiffness of the sample carrier, enabling automatic handling of the samples. Clipping of the Autogrid can therefore be considered as one of the missing links for full automation of the cryo-ET workflow. This thesis was aimed at automating the clipping procedure, thereby improving the yield, reducing the time required for practice, and reducing the amount of manual handling steps of the current procedure.
A problem analysis on the current procedure for clipping the Autogrid was performed, based on which multiple solutions were designed and tested experimentally. Designed gripper fingers were used with a six-axis industrial robot arm to automatically handle sample carriers and Autogrids. Such procedures are done manually using tweezers in the current procedure. Damage induced on the sample carriers during automatic handling was quantified experimentally.
Automating the clipping procedure allows for adjusting the orientation of sample carriers during the procedure, which is currently (almost) impossible. For this purpose, a machine learning-based marker detection algorithm was used to automatically detect markers that are present at the bottom of an Autogrid. This detection algorithm was used with a stepper motor and a designed mechanism to automatically obtain a specified orientation of the Autogrid. Finally, recommendations were given on how the proposed designs could be used in a final automated solution.
Master thesis
(2019)
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Ronald van den Berg, Amir Zadpoor, Nima Tolou, Mohammad Mirzaali Mazandarani, A.C. Akyildiz
The majority of cardiovascular clinical events, which are the main causes of mortality and morbidity worldwide, are caused by atherosclerotic plaque rupture. This biomechanical event occurs when the local plaque stresses exceed its strength. The plaque stresses can be assessed by computational models to predict these events. Current approaches to obtaining the plaque material stiffness properties that these models require as input have large computational costs and are therefore far from being implemented for clinical use. This study aims to develop, validate, and apply for the first time, an approach to obtaining the material stiffness properties of atherosclerotic plaque tissue much faster by employing the virtual fields method (VFM). With this method, the virtual work principle is employed with boundary problem specific, kinematically admissible virtual fields to solve energy balance equations for the material stiffness parameters that are of interest. In this study a method is presented for obtaining the virtual fields for the specific application of intraluminally pressurised atherosclerotic plaque tissue. For the purpose of validation, full field displacement maps were computed at 100 mmHg using Finite Element (FE) models based on histological slides of atherosclerotic plaque tissue. To mimic a realistic situation, the resolution and noise levels of a clinical and high frequency ultrasound scanner were used. Although higher resolution deformation maps with smaller noise levels were shown to provide more accurate results, the VFM-based technique demonstrated good performance for both the high frequency and clinical ultrasound scanner settings tested. VFM was also used in a single case study to estimate the c1 material parameter for a Neo-Hookean incompressible material model in the case of an atherosclerotic human coronary artery. The estimated c1-values for this case were: 21.5 kPa for diseased intima, 13.3 kPa for lipid, and 23.6 kPa for wall tissue. These values were in good agreement with the reported values from literature. In this study, VFM was applied successfully for the material characterization of atherosclerotic plaques for the first time. It is more attractive than current approaches as it is computationally less expensive and has a great potential to be extended for material characterization of even more plaque components than employed in the current study.
...
The majority of cardiovascular clinical events, which are the main causes of mortality and morbidity worldwide, are caused by atherosclerotic plaque rupture. This biomechanical event occurs when the local plaque stresses exceed its strength. The plaque stresses can be assessed by computational models to predict these events. Current approaches to obtaining the plaque material stiffness properties that these models require as input have large computational costs and are therefore far from being implemented for clinical use. This study aims to develop, validate, and apply for the first time, an approach to obtaining the material stiffness properties of atherosclerotic plaque tissue much faster by employing the virtual fields method (VFM). With this method, the virtual work principle is employed with boundary problem specific, kinematically admissible virtual fields to solve energy balance equations for the material stiffness parameters that are of interest. In this study a method is presented for obtaining the virtual fields for the specific application of intraluminally pressurised atherosclerotic plaque tissue. For the purpose of validation, full field displacement maps were computed at 100 mmHg using Finite Element (FE) models based on histological slides of atherosclerotic plaque tissue. To mimic a realistic situation, the resolution and noise levels of a clinical and high frequency ultrasound scanner were used. Although higher resolution deformation maps with smaller noise levels were shown to provide more accurate results, the VFM-based technique demonstrated good performance for both the high frequency and clinical ultrasound scanner settings tested. VFM was also used in a single case study to estimate the c1 material parameter for a Neo-Hookean incompressible material model in the case of an atherosclerotic human coronary artery. The estimated c1-values for this case were: 21.5 kPa for diseased intima, 13.3 kPa for lipid, and 23.6 kPa for wall tissue. These values were in good agreement with the reported values from literature. In this study, VFM was applied successfully for the material characterization of atherosclerotic plaques for the first time. It is more attractive than current approaches as it is computationally less expensive and has a great potential to be extended for material characterization of even more plaque components than employed in the current study.