Otorhinolaryngologic Diseases Commons^™

Semi-Automated Virtual Endoscopy Of The Frontal Recess, Ali Jafar, William Yao, Martin Citardi

Faculty, Staff and Student Publications

Introduction: Virtual endoscopy (VE) is the computer-based reprocessing of diagnostic imaging to simulate endoscopy of an anatomic region of interest. VE of the Frontal Sinus Outflow Tract (FSOT) may assist surgical planning and education.

Method: VE was performed on 16 normal sinus computed tomography (CT) scans for a total of 32 sides using the "path-to-target" tool on the TruDi surgical navigation system (ver. 2.3; Acclarent, Irving, CA, USA). To aid orientation during VE, planning points were placed on the middle turbinate, ethmoidal bulla, and skull base. The VE representation of anatomy and FSOT accuracy was manually confirmed by reviewing the …

Semi-Quantitative Assessment Of Surgical Navigation Accuracy During Endoscopic Sinus Surgery In A Real-World Environment, David Z Allen, Jason Talmadge, Martin J Citardi

Faculty, Staff and Student Publications

Introduction: Although surgical navigation is commonly used in rhinologic surgery, data on real world performance are sparse because of difficulties in collecting measurements for target registration error (TRE). Despite publications showing submillimeter TRE, surgeons do report TRE of >3 mm. We describe a novel method for assessing TRE during surgery and report findings with this technique.

Methods: The TruDi navigation system (Acclarent, Irving, CA) was registered using a contour-based protocol. The surgeon estimated target registration error (e-TRE) at up to 8 points (anatomic regions of interest [ROI]) during endoscopic sinus surgery (ESS). System logs were used to simulate the localization …

Archery: A Prospective Observational Study Of Artificial Intelligence-Based Radiotherapy Treatment Planning For Cervical, Head And Neck And Prostate Cancer – Study Protocol, Ajay Aggarwal, Laurence Edward Court, Peter Hoskin, Isabella Jacques, Mariana Kroiss, Sarbani Laskar, Yolande Lievens, Indranil Mallick, Rozita Abdul Malik, Elizabeth Miles, Issa Mohamad, Claire Murphy, Matthew Nankivell, Jeannette Parkes, Mahesh Parmar, Carol Roach, Hannah Simonds, Julie Torode, Barbara Vanderstraeten, Ruth Langley

Faculty, Staff and Student Publications

INTRODUCTION: Fifty per cent of patients with cancer require radiotherapy during their disease course, however, only 10%-40% of patients in low-income and middle-income countries (LMICs) have access to it. A shortfall in specialised workforce has been identified as the most significant barrier to expanding radiotherapy capacity. Artificial intelligence (AI)-based software has been developed to automate both the delineation of anatomical target structures and the definition of the position, size and shape of the radiation beams. Proposed advantages include improved treatment accuracy, as well as a reduction in the time (from weeks to minutes) and human resources needed to deliver radiotherapy. …

A Self-Configuring Deep Learning Network For Segmentation Of Temporal Bone Anatomy In Cone-Beam Ct Imaging, Andy S Ding, Alexander Lu, Zhaoshuo Li, Manish Sahu, Deepa Galaiya, Jeffrey H Siewerdsen, Mathias Unberath, Russell H Taylor, Francis X Creighton

Faculty, Staff and Student Publications

OBJECTIVE: Preoperative planning for otologic or neurotologic procedures often requires manual segmentation of relevant structures, which can be tedious and time-consuming. Automated methods for segmenting multiple geometrically complex structures can not only streamline preoperative planning but also augment minimally invasive and/or robot-assisted procedures in this space. This study evaluates a state-of-the-art deep learning pipeline for semantic segmentation of temporal bone anatomy.

STUDY DESIGN: A descriptive study of a segmentation network.

SETTING: Academic institution.

METHODS: A total of 15 high-resolution cone-beam temporal bone computed tomography (CT) data sets were included in this study. All images were co-registered, with relevant anatomical structures …

Automated Extraction Of Anatomical Measurements From Temporal Bone Ct Imaging, Andy S Ding, Alexander Lu, Zhaoshuo Li, Deepa Galaiya, Masaru Ishii, Jeffrey H Siewerdsen, Russell H Taylor, Francis X Creighton

Faculty, Staff and Student Publications

OBJECTIVE: Proposed methods of minimally invasive and robot-assisted procedures within the temporal bone require measurements of surgically relevant distances and angles, which often require time-consuming manual segmentation of preoperative imaging. This study aims to describe an automatic segmentation and measurement extraction pipeline of temporal bone cone-beam computed tomography (CT) scans.

STUDY DESIGN: Descriptive study of temporal bone measurements.

SETTING: Academic institution.

METHODS: A propagation template composed of 16 temporal bone CT scans was formed with relevant anatomical structures and landmarks manually segmented. Next, 52 temporal bone CT scans were autonomously segmented using deformable registration techniques from the Advanced Normalization Tools …

Knowledge-Based Planning For The Radiation Therapy Treatment Plan Quality Assurance For Patients With Head And Neck Cancer, Wenhua Cao, Mary Gronberg, Adenike Olanrewaju, Thomas Whitaker, Karen Hoffman, Carlos Cardenas, Adam Garden, Heath Skinner, Beth Beadle, Laurence Court

Faculty, Staff and Student Publications

This study aimed to investigate the feasibility of using a knowledge-based planning technique to detect poor quality VMAT plans for patients with head and neck cancer. We created two dose-volume histogram (DVH) prediction models using a commercial knowledge-based planning system (RapidPlan, Varian Medical Systems, Palo Alto, CA) from plans generated by manual planning (MP) and automated planning (AP) approaches. DVHs were predicted for evaluation cohort 1 (EC1) of 25 patients and compared with achieved DVHs of MP and AP plans to evaluate prediction accuracy. Additionally, we predicted DVHs for evaluation cohort 2 (EC2) of 25 patients for which we intentionally …