Towards Image-Guided Pediatric Atrial Septal Defect Repair

Congenital heart disease occurs in 107.6 out of 10,000 live births, with Atrial Septal Defects (ASD) accounting for 10\% of these conditions. Historically, ASDs were treated with open heart surgery using cardiopulmonary bypass, allowing a patch to be sewn over the defect. In 1976, King et al. demonstrated use of a transcatheter occlusion procedure, thus reducing the invasiveness of ASD repair. Localization during these catheter based procedures traditionally has relied on bi-plane fluoroscopy; more recently trans-esophageal echocardiography (TEE) and intra-cardiac echocardiography (ICE) have been used to navigate these procedures. Although there is a high success rate using the transcatheter occlusion procedure, fluoroscopy poses radiation dose risk to both patient and clinician. The impact of this dose to the patients is important as many of those undergoing this procedure are children, who have an increased risk associated with radiation exposure. Their longer life expectancy than adults provides a larger window of opportunity for expressing the damaging effects of ionizing radiation. In addition, epidemiologic studies of exposed populations have demonstrated that children are considerably more sensitive to the carcinogenic effects radiation. Image-guided surgery (IGS) uses pre-operative and intra-operative images to guide surgery or an interventional procedure. Central to every IGS system is a software application capable of processing and displaying patient images, registration between multiple coordinate systems, and interfacing with a tool tracking system. We have developed a novel image-guided surgery framework called Kit for Navigation by Image Focused Exploration (KNIFE). This software system serves as the core technology by which a system for reduction of radiation exposure to pediatric patients was developed. The bulk of the initial work in this research endevaour was the development of KNIFE which itself went through countless iterations before arriving at its current state as per the feature requirements established. Secondly, since this work involved the use of captured medical images and their use in an IGS software suite, a brief analysis of the physics behind the images was conducted. Through this aspect of the work, intrinsic parameters (principal point and focal point) of the fluoroscope were quantified using a 3D grid calibration phantom. A second grid phantom was traversed through the fluoroscopic imaging volume of II and flat panel based systems at 2 cm intervals building a scatter field of the volume to demonstrate pincushion and \u27S\u27 distortion in the images. Effects of projection distortion on the images was assessed by measuring the fiducial registration error (FRE) of each point used in two different registration techniques, where both methods utilized ordinary procrustes analysis but the second used a projection matrix built from the fluoroscopes calculated intrinsic parameters. A case study was performed to test whether the projection registration outperforms the rigid transform only. Using the knowledge generated were able to successfully design and complete mock clinical procedures using cardiac phantom models. These mock trials at the beginning of this work used a single point to represent catheter location but this was eventually replaced with a full shape model that offered numerous advantages. At the conclusion of this work a novel protocol for conducting IG ASD procedures was developed. Future work would involve the construction of novel EM tracked tools, phantom models for other vascular diseases and finally clinical integration and use

Radiation doses and associated risks from x-ray guided cardiac catheterization procedures in children and young adults

PhD ThesisCardiac catheterizations are an essential procedure in the management of patients with congenital and acquired heart conditions. However, associated radiation doses are often high, raising concerns over potentially increased cancer risks. Neither the radiation doses, nor the associated risks, have been adequately investigated in young people undergoing these procedures. A cohort was established of around 13,500 patients aged under 22 years who have undergone cardiac catheterizations in England. Organ doses were estimated based on a dosimetry system utilising data from Monte Carlo simulations. Doses were highest for the lungs (median: 17.6 millisieverts, mSv) and heart (13.6 mSv), while doses to bone marrow (2.6 mSv) and the thyroid (0.7 mSv) were relatively low. Radiation doses have fallen by a factor of up to ten during the study period. The results were compared to equivalent figures derived from physical measurements. Uncertainties in dose estimates were calculated. These were around ±30%, though were potentially much higher for breast dose. The risk of cancer in relation to estimated doses was calculated using BEIR VII risk models. For examinations conducted using modern equipment, these risks are around 1 in 1700. A small epidemiological analysis was performed, suggesting a nearly threefold increased risk of cancer in the cohort, compared to the general UK population. There are a number of reasons to suggest that this increase was primarily not related to radiation exposure, most notably the large impact of transplantation and likely associated immunosuppressant use. Despite the high cancer incidence, the overall survival in the cohort was high, at around 91% after 30 years. Conclusion: The study provides the first large scale estimation of organ doses from cardiac catheterizations among this age group. Rates of cancer among this patient group are high, although this is appears to be mostly due to factors other than radiation exposure.British Heart Foundatio

Advancements in Medical Imaging and Diagnostics with Deep Learning Technologies

Medical imaging has long been a cornerstone in diagnostic medicine, providing clinicians with a non-invasive method to visualize internal structures and processes. However, traditional imaging techniques have faced challenges in resolution, safety concerns related to radiation exposure, and the need for invasive procedures for clearer visualization. With the advent of deep learning technologies, significant advancements have been made in the field of medical imaging, addressing many of these challenges and introducing new capabilities. This research seeks into the integration of deep learning in enhancing image resolution, leading to clearer and more detailed visualizations. Furthermore, the ability to reconstruct three-dimensional images from traditional two-dimensional scans offers a more comprehensive view of the area under examination. Automated analysis powered by deep learning algorithms not only speeds up the diagnostic process but also detects anomalies that might be overlooked by the human eye. Predictive analysis, based on these enhanced images, can forecast the likelihood of diseases, and real-time analysis during surgeries ensures immediate feedback, enhancing the precision of medical procedures. Safety in medical imaging has also seen improvements. Techniques powered by deep learning require reduced radiation, minimizing risks to patients. Additionally, the enhanced clarity and detail in images reduce the need for invasive procedures, further ensuring patient safety. The integration of imaging data with Electronic Health Records (EHR) has paved the way for personalized care recommendations, tailoring treatments based on individual patient history and current diagnostics. Lastly, the role of deep learning extends to medical education, where it aids in creating realistic simulations and models, equipping medical professionals with better training tools

Automatic image analysis of C-arm Computed Tomography images for ankle joint surgeries

Open reduction and internal fixation is a standard procedure in ankle surgery for treating a fractured fibula. Since fibula fractures are often accompanied by an injury of the syndesmosis complex, it is essential to restore the correct relative pose of the fibula relative to the adjoining tibia for the ligaments to heal. Otherwise, the patient might experience instability of the ankle leading to arthritis and ankle pain and ultimately revision surgery. Incorrect positioning referred to as malreduction of the fibula is assumed to be one of the major causes of unsuccessful ankle surgery. 3D C-arm imaging is the current standard procedure for revealing malreduction of fractures in the operating room. However, intra-operative visual inspection of the reduction result is complicated due to high inter-individual variation of the ankle anatomy and rather based on the subjective experience of the surgeon. A contralateral side comparison with the patient’s uninjured ankle is recommended but has not been integrated into clinical routine due to the high level of radiation exposure it incurs. This thesis presents the first approach towards a computer-assisted intra-operative contralateral side comparison of the ankle joint. The focus of this thesis was the design, development and validation of a software-based prototype for a fully automatic intra-operative assistance system for orthopedic surgeons. The implementation does not require an additional 3D C-arm scan of the uninjured ankle, thus reducing time consumption and cumulative radiation dose. A 3D statistical shape model (SSM) is used to reconstruct a 3D surface model from three 2D fluoroscopic projections representing the uninjured ankle. To this end, a 3D SSM segmentation is performed on the 3D image of the injured ankle to gain prior knowledge of the ankle. A 3D convolutional neural network (CNN) based initialization method was developed and its outcome was incorporated into the SSM adaption step. Segmentation quality was shown to be improved in terms of accuracy and robustness compared to the pure intensity-based SSM. This allows us to overcome the limitations of the previously proposed methods, namely inaccuracy due to metal artifacts and the lack of device-to-patient orientation of the C-arm. A 2D-CNN is employed to extract semantic knowledge from all fluoroscopic projection images. This step of the pipeline both creates features for the subsequent reconstruction and also helps to pre-initialize the 3D-SSM without user interaction. A 2D-3D multi-bone reconstruction method has been developed which uses distance maps of the 2D features for fast and accurate correspondence optimization and SSM adaption. This is the central and most crucial component of the workflow. This is the first time that a bone reconstruction method has been applied to the complex ankle joint and the first reconstruction method using CNN based segmentations as features. The reconstructed 3D-SSM of the uninjured ankle can be back-projected and visualized in a workflow-oriented manner to procure clear visualization of the region of interest, which is essential for the evaluation of the reduction result. The surgeon can thus directly compare an overlay of the contralateral ankle with the injured ankle. The developed methods were evaluated individually using data sets acquired during a cadaver study and representative clinical data acquired during fibular reduction. A hierarchical evaluation was designed to assess the inaccuracies of the system on different levels and to identify major sources of error. The overall evaluation performed on eleven challenging clinical datasets acquired for manual contralateral side comparison showed that the system is capable of accurately reconstructing 3D surface models of the uninjured ankle solely using three projection images. A mean Hausdorff distance of 1.72 mm was measured when comparing the reconstruction result to the ground truth segmentation and almost achieved the high required clinical accuracy of 1-2 mm. The overall error of the pipeline was mainly attributed to inaccuracies in the 2D-CNN segmentation. The consistency of these results requires further validation on a larger dataset. The workflow proposed in this thesis establishes the first approach to enable automatic computer-assisted contralateral side comparison in ankle surgery. The feasibility of the proposed approach was proven on a limited amount of clinical cases and has already yielded good results. The next important step is to alleviate the identified bottlenecks in the approach by providing more training data in order to further improve the accuracy. In conclusion, the new approach presented gives the chance to guide the surgeon during the reduction process, improve the surgical outcome while avoiding additional radiation exposure and reduce the number of revision surgeries in the long term

ADVANCED MOTION MODELS FOR RIGID AND DEFORMABLE REGISTRATION IN IMAGE-GUIDED INTERVENTIONS

Image-guided surgery (IGS) has been a major area of interest in recent decades that continues to transform surgical interventions and enable safer, less invasive procedures. In the preoperative contexts, diagnostic imaging, including computed tomography (CT) and magnetic resonance (MR) imaging, offers a basis for surgical planning (e.g., definition of target, adjacent anatomy, and the surgical path or trajectory to the target). At the intraoperative stage, such preoperative images and the associated planning information are registered to intraoperative coordinates via a navigation system to enable visualization of (tracked) instrumentation relative to preoperative images. A major limitation to such an approach is that motions during surgery, either rigid motions of bones manipulated during orthopaedic surgery or brain soft-tissue deformation in neurosurgery, are not captured, diminishing the accuracy of navigation systems. This dissertation seeks to use intraoperative images (e.g., x-ray fluoroscopy and cone-beam CT) to provide more up-to-date anatomical context that properly reflects the state of the patient during interventions to improve the performance of IGS. Advanced motion models for inter-modality image registration are developed to improve the accuracy of both preoperative planning and intraoperative guidance for applications in orthopaedic pelvic trauma surgery and minimally invasive intracranial neurosurgery. Image registration algorithms are developed with increasing complexity of motion that can be accommodated (single-body rigid, multi-body rigid, and deformable) and increasing complexity of registration models (statistical models, physics-based models, and deep learning-based models). For orthopaedic pelvic trauma surgery, the dissertation includes work encompassing: (i) a series of statistical models to model shape and pose variations of one or more pelvic bones and an atlas of trajectory annotations; (ii) frameworks for automatic segmentation via registration of the statistical models to preoperative CT and planning of fixation trajectories and dislocation / fracture reduction; and (iii) 3D-2D guidance using intraoperative fluoroscopy. For intracranial neurosurgery, the dissertation includes three inter-modality deformable registrations using physic-based Demons and deep learning models for CT-guided and CBCT-guided procedures

Investigation of the Performance of Photon Counting Arrays Based on Polycrystalline Silicon Thin-Film Transistors

Projection x-ray imaging is commonly employed to visualize internal human anatomy and used to produce diagnostic images. Modern projection imaging is typically performed using an active matrix, flat panel imager that is comprised of a converter layer overlying a pixelated array. The images are formed by converting x-ray photons into electrical signals, and then integrating those signals over a frame time – a method referred to as fluence integration. Recently, imagers employing a second method for creating x-ray images – referred to as photon counting – have been developed and used to perform mammographic imaging (a form of projection imaging). Photon counting involves measuring the energy of each interacting x-ray photon and storing digital counts of the number of photons exceeding one or more energy thresholds. Because the imaging information is stored digitally, photon counting imagers are less susceptible to noise than fluence-integrating imagers – which improves image quality and/or decreases the amount of radiation required to acquire an image. Current photon counting mammographic imagers are based on crystalline silicon and are limited in detection area. In order to produce an image, the array is moved in a scanning motion across the object of interest. A photon counting imager with larger detection area would benefit other projection imaging modalities – such as radiography (which produces, for example, chest x-ray images) or fluoroscopy (which is used for non-invasively inserting stents and other medical devices). However, techniques to increase detection area, such as tiling multiple arrays, result in increased imager complexity or cost. For this reason, our group has been exploring the possibility of creating photon counting arrays using a different semiconductor material, referred to as polycrystalline silicon (poly-Si). This material is fabricated using a thin-film process, which allows the economic manufacture of monolithic, large-area arrays and has favorable material properties for creating complex, high speed circuits. Using poly-Si, a set of prototype arrays have been designed and fabricated. The design of the arrays consists of four components: an amplifier, a comparator, a clock generator, and a counter. Several circuit variations were created for each component, and circuit simulations were performed in order to determine energy resolution and count rate values for each variation of each component. For the amplifier component, all circuit variations were determined to have an energy resolution of ~10% when presented with a 70 keV input x-ray photon (a typical photon energy level used in diagnostic imaging). This energy resolution value is comparable to those reported for photon counting imagers fabricated using crystalline silicon. In addition, while count rate values for the amplifier component were roughly one order of magnitude too low for radiographic and fluoroscopic applications (which require count rates on the order of 1 million counts per second per square millimeter [cps/mm2]), a hypothetical amplifier circuit variation with count rate capabilities suitable for these applications (while preserving the same ~10% energy resolution) was designed. In addition, the count rate values for the various comparator, clock generator, and counter circuit variations ranged from 100 to 3000 kcps/mm2. Finally, due to improvements in the poly-Si fabrication process (driven largely by the display industry), future photon counting arrays employing this material can have pixel pitches as small as 250 um – a size approaching that suitable for radiographic and fluoroscopic imaging.PHDBiomedical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/144006/1/akliang_1.pd

On the investigation of a novel x-ray imaging techniques in radiation oncology

Radiation therapy is indicated for nearly 50% of cancer patients in Australia. Radiation therapy requires accurate delivery of ionising radiation to the neoplastic tissue and pre-treatment in situ x-ray imaging plays an important role in meeting treatment accuracy requirements. Four dimensional cone-beam computed tomography (4D CBCT) is one such pre-treatment imaging technique that can help to visualise tumour target motion due to breathing at the time of radiation treatment delivery. Measuring and characterising the target motion can help to ensure highly accurate therapeutic x-ray beam delivery. In this thesis, a novel pre-treatment x-ray imaging technique, called Respiratory Triggered 4D cone-beam Computed Tomography (RT 4D CBCT), is conceived and investigated. Specifically, the aim of this work is to progress the 4D CBCT imaging technology by investigating the use of a patient’s breathing signal to improve and optimise the use of imaging radiation in 4D CBCT to facilitate the accurate delivery of radiation therapy. These investigations are presented in three main studies: 1. Introduction to the concept of respiratory triggered four dimensional conebeam computed tomography. 2. A simulation study exploring the behaviour of RT 4D CBCT using patientmeasured respiratory data. 3. The experimental realisation of RT 4D CBCT working in a real-time acquisitions setting. The major finding from this work is that RT 4D CBCT can provide target motion information with a 50% reduction in the x-ray imaging dose applied to the patient

A biomechanical approach for real-time tracking of lung tumors during External Beam Radiation Therapy (EBRT)

Lung cancer is the most common cause of cancer related death in both men and women. Radiation therapy is widely used for lung cancer treatment. However, this method can be challenging due to respiratory motion. Motion modeling is a popular method for respiratory motion compensation, while biomechanics-based motion models are believed to be more robust and accurate as they are based on the physics of motion. In this study, we aim to develop a biomechanics-based lung tumor tracking algorithm which can be used during External Beam Radiation Therapy (EBRT). An accelerated lung biomechanical model can be used during EBRT only if its boundary conditions (BCs) are defined in a way that they can be updated in real-time. As such, we have developed a lung finite element (FE) model in conjunction with a Neural Networks (NNs) based method for predicting the BCs of the lung model from chest surface motion data. To develop the lung FE model for tumor motion prediction, thoracic 4D CT images of lung cancer patients were processed to capture the lung and diaphragm geometry, trans-pulmonary pressure, and diaphragm motion. Next, the chest surface motion was obtained through tracking the motion of the ribcage in 4D CT images. This was performed to simulate surface motion data that can be acquired using optical tracking systems. Finally, two feedforward NNs were developed, one for estimating the trans-pulmonary pressure and another for estimating the diaphragm motion from chest surface motion data. The algorithm development consists of four steps of: 1) Automatic segmentation of the lungs and diaphragm, 2) diaphragm motion modelling using Principal Component Analysis (PCA), 3) Developing the lung FE model, and 4) Using two NNs to estimate the trans-pulmonary pressure values and diaphragm motion from chest surface motion data. The results indicate that the Dice similarity coefficient between actual and simulated tumor volumes ranges from 0.76±0.04 to 0.91±0.01, which is favorable. As such, real-time lung tumor tracking during EBRT using the proposed algorithm is feasible. Hence, further clinical studies involving lung cancer patients to assess the algorithm performance are justified