Optimal Port Placement And Automated Robotic Positioning For Instrumented Laparoscopic Biosensors

OPTIMAL SURGICAL PORT PLACEMENT AND AUTOMATED ROBOTIC POSITIONING FOR RAMAN AND OTHER BIOSENSORS by BRADY KING January 2011 Advisors: Dr. Abhilash Pandya, Dr. Darin Ellis, Dr. Le Yi Wang, and Dr. Greg Auner Major: Computer Engineering Degree: Doctor of Philosophy Medical biosensors can provide new information during minimally invasive and robotic surgical procedures. However, these biosensors have significant physical limitations that make it difficult to find optimal port locations and place them in vivo. This dissertation explores the application of robotics and virtual/augmented reality to biosensors to enable their optimal use in vivo. In the first study, human performance in the task of port placement was evaluated to determine if computer intervention and assistance was needed. Using a virtual surgical environment, we present a number of targets on one or more tissue surfaces. A human factors study was conducted with 20 subjects that analyzed the subject\u27s placement of a port with the goal of scanning as many targets as possible with a biosensor. The study showed performance to be less than optimal with significant degradation in several specific scenarios. In the second study, an automated intelligent port placement system for biosensor use was developed. Patient data was displayed in an environment in which a surgeon could indicate areas of interest. The system utilized biosensor physical limitations and provided the best port location from which the biosensor could reach the targets on a collision-free path. The study showed that it is possible to find an optimal port location for proper biosensor data capture. In the final study, a surgical robot was investigated for potential use in holding and positioning a biosensor in vivo. A full control suite was developed for an AESOP 1000, enabling the positioning of the biosensor without hand manipulation. It was found that the robot lacks the accuracy needed for proper biosensor utilization. Specific causes for the inaccuracies were identified for analysis and consideration in future robotic platforms. Overall, the results show that the application of medical robotics and virtual/augmented reality is able to overcome of the significant physical limitations inherent to biosensor design that currently limit their use in surgery. We conjecture that a complete system, with a more accurate robot, could be used in vivo. We believe that results taken from the individual studies will result in improvements to pre-operative port placement and robotic design

A Patient-Specific Infrared Imaging Technique for Adjunctive Breast Cancer Screening: A Clinical and Simulation - Based Approach

Breast cancer is currently the most prevalent form of cancer in women with over 266,000 new diagnoses every year. The various methods used for breast cancer screening range in accuracy and cost, however there is no easily reproducible, reliable, low-cost screening method currently available for detecting cancer in breasts, especially with dense tissue. Steady-state Infrared Imaging (IRI) is unaffected by tissue density and has the potential to detect tumors in the breast by measuring and capturing the thermal profile on the breast surface induced by increased blood perfusion and metabolic activity in a rapidly growing malignant tumor. The current work presents a better understanding of IRI as an accurate breast cancer detection modality. A detailed study utilizing IRI-MRI approach with clinical design and validation of an elaborate IRI-Mammo study are presented by considering patient population, clinical study design, image interpretation, and recommended future path. Clinical IRI images are obtained in this study and an ANSYS-based modeling process developed earlier at RIT is used to localize and detect tumor in seven patients without subjective human interpretation. Further, the unique thermal characteristics of tumors that make their signatures distinct from benign conditions are identified. This work is part of an ongoing multidisciplinary collaboration between a team of thermal engineers and numerical modelers at the Rochester Institute of Technology and a team of clinicians at the Rochester General Hospital. The following components were developed to ensure valid experimentation while considering ethical considerations: IRB documentation, patient protocols, an image acquisition system (camera setup and screening table), and the necessary tools needed for image analysis without human interpretation. IRI images in the prone position were obtained and were used in accurately detecting the presence of a cancerous tumor in seven subjects. The size and location of tumor was also confirmed within 7 mm as compared to biopsy-proven pathology information. The study indicates that the IRI-Mammo approach has potential to be a highly effective adjunctive screening tool that can improve the breast cancer detection rates especially for subjects with dense breast tissue. This method is low cost, no-touch, radiation-free and highly portable, making it an attractive candidate as a breast cancer detection modality. Further, the developed method provided insight into infrared features corresponding to other biological images, pathology reports and patient history

Deep Learning in Cardiology

The medical field is creating large amount of data that physicians are unable to decipher and use efficiently. Moreover, rule-based expert systems are inefficient in solving complicated medical tasks or for creating insights using big data. Deep learning has emerged as a more accurate and effective technology in a wide range of medical problems such as diagnosis, prediction and intervention. Deep learning is a representation learning method that consists of layers that transform the data non-linearly, thus, revealing hierarchical relationships and structures. In this review we survey deep learning application papers that use structured data, signal and imaging modalities from cardiology. We discuss the advantages and limitations of applying deep learning in cardiology that also apply in medicine in general, while proposing certain directions as the most viable for clinical use.Comment: 27 pages, 2 figures, 10 table

Integrated navigation and visualisation for skull base surgery

Skull base surgery involves the management of tumours located on the underside of the brain and the base of the skull. Skull base tumours are intricately associated with several critical neurovascular structures making surgery challenging and high risk. Vestibular schwannoma (VS) is a benign nerve sheath tumour arising from one of the vestibular nerves and is the commonest pathology encountered in skull base surgery. The goal of modern VS surgery is maximal tumour removal whilst preserving neurological function and maintaining quality of life but despite advanced neurosurgical techniques, facial nerve paralysis remains a potentially devastating complication of this surgery. This thesis describes the development and integration of various advanced navigation and visualisation techniques to increase the precision and accuracy of skull base surgery. A novel Diffusion Magnetic Resonance Imaging (dMRI) acquisition and processing protocol for imaging the facial nerve in patients with VS was developed to improve delineation of facial nerve preoperatively. An automated Artificial Intelligence (AI)-based framework was developed to segment VS from MRI scans. A user-friendly navigation system capable of integrating dMRI and tractography of the facial nerve, 3D tumour segmentation and intraoperative 3D ultrasound was developed and validated using an anatomically-realistic acoustic phantom model of a head including the skull, brain and VS. The optical properties of five types of human brain tumour (meningioma, pituitary adenoma, schwannoma, low- and high-grade glioma) and nine different types of healthy brain tissue were examined across a wavelength spectrum of 400 nm to 800 nm in order to inform the development of an Intraoperative Hypserpectral Imaging (iHSI) system. Finally, functional and technical requirements of an iHSI were established and a prototype system was developed and tested in a first-in-patient study

Automatic analysis of medical images for change detection in prostate cancer

Prostate cancer is the most common cancer and second most common cause of cancer death in men in the UK. However, the patient risk from the cancer can vary considerably, and the widespread use of prostate-specific antigen (PSA) screening has led to over-diagnosis and over-treatment of low-grade tumours. It is therefore important to be able to differentiate high-grade prostate cancer from the slowly- growing, low-grade cancer. Many of these men with low-grade cancer are placed on active surveillance (AS), which involves constant monitoring and intervention for risk reclassification, relying increasingly on magnetic resonance imaging (MRI) to detect disease progression, in addition to TRUS-guided biopsies which are the routine clinical standard method to use. This results in a need for new tools to process these images. For this purpose, it is important to have a good TRUS-MR registration so corresponding anatomy can be located accurately between the two. Automatic segmentation of the prostate gland on both modalities reduces some of the challenges of the registration, such as patient motion, tissue deformation, and the time of the procedure. This thesis focuses on the use of deep learning methods, specifically convolutional neural networks (CNNs), for prostate cancer management. Chapters 4 and 5 investigated the use of CNNs for both TRUS and MRI prostate gland segmentation, and reported high segmentation accuracies for both, Dice Score Coefficients (DSC) of 0.89 for TRUS segmentations and DSCs between 0.84-0.89 for MRI prostate gland segmentation using a range of networks. Chapter 5 also investigated the impact of these segmentation scores on more clinically relevant measures, such as MRI-TRUS registration errors and volume measures, showing that a statistically significant difference in DSCs did not lead to a statistically significant difference in the clinical measures using these segmentations. The potential of these algorithms in commercial and clinical systems are summarised and the use of the MRI prostate gland segmentation in the application of radiological prostate cancer progression prediction for AS patients are investigated and discussed in Chapter 8, which shows statistically significant improvements in accuracy when using spatial priors in the form of prostate segmentations (0.63 ± 0.16 vs. 0.82 ± 0.18 when comparing whole prostate MRI vs. only prostate gland region, respectively)