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1. Evaluation of Interventional Planning Software Features for MR-guided Transrectal Prostate Biopsies

   https://doi.org/10.1109/BIBE50027.2020.00161  

   Velazco-Garcia, Jose D. ; Navkar, Nikhil V. ; Balakrishnan, Shidin ; Abinahed, Julien ; Al-Ansari, Abdulla ; Darweesh, Adham ; Al-Rumaihi, Khalid ; Christoforou, Eftychios G. ; Leiss, Ernst L. ; Karkoub, Mansour ; et al ( October 2020 , IEEE 20th International Conference on Bioinformatics and Bioengineering (BIBE))

   This work presents an interventional planning software to be used in conjunction with a robotic manipulator to perform transrectal MR guided prostate biopsies. The interventional software was designed taking in consideration a generic manipulator used under the two modes of operation: side-firing and end-firing of the biopsy needle. Studies were conducted with urologists using the software to plan virtual biopsies. The results show features of software relevant for operating efficiently under the two modes of operation.
2. A Prototype Holographic Augmented Reality Interface for Image-Guided Prostate Cancer Interventions

   https://doi.org/10.2312/vcbm.20181225  

   Morales Mojica, Cristina M. ( January 2018 , Eurographics Workshop on Visual Computing for Biomedicine)

   Puig Puig, Anna and (Ed.)

   Motivated by the potential of holographic augmented reality (AR) to offer an immersive 3D appreciation of morphology and anatomy, the purpose of this work is to develop and assess an interface for image-based planning of prostate interventions with a head-mounted display (HMD). The computational system is a data and command pipeline that links a magnetic resonance imaging (MRI) scanner/data and the operator, that includes modules dedicated to image processing and segmentation, structure rendering, trajectory planning and spatial co-registration. The interface was developed with the Unity3D Engine (C#) and deployed and tested on a HoloLens HMD. For ergonomics in the surgical suite, the system was endowed with hands-free interactive manipulation of images and the holographic scene via hand gestures and voice commands. The system was tested in silico using MRI and ultrasound datasets of prostate phantoms. The holographic AR scene rendered by the HoloLens HMD was subjectively found superior to desktop-based volume or 3D rendering with regard to structure detection and appreciation of spatial relationships, planning access paths and manual co-registration of MRI and Ultrasound. By inspecting the virtual trajectory superimposed to rendered structures and MR images, the operator observes collisions of the needle path with vital structures (e.g. urethra) andmore »adjusts accordingly. Holographic AR interfacing with wireless HMD endowed with hands-free gesture and voice control is a promising technology. Studies need to systematically assess the clinical merit of such systems and needed functionalities.« less
3. Prostate Cancer Risk Stratification via Nondestructive 3D Pathology with Deep Learning–Assisted Gland Analysis

   https://doi.org/10.1158/0008-5472.CAN-21-2843  

   Xie, Weisi ; Reder, Nicholas P. ; Koyuncu, Can ; Leo, Patrick ; Hawley, Sarah ; Huang, Hongyi ; Mao, Chenyi ; Postupna, Nadia ; Kang, Soyoung ; Serafin, Robert ; et al ( December 2021 , Cancer Research)

   An end-to-end pipeline for deep learning–assisted computational 3D histology analysis of whole prostate biopsies shows that nondestructive 3D pathology has the potential to enable superior prognostic stratification of patients with prostate cancer.

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4. A Novel Perceptive Robotic Cane with Haptic Navigation for Enabling Vision-Independent Participation in the Social Dynamics of Seat Choice

   Agrawal, Shivendra ; West, Mary Etta ; Hayes, Bradley ( October 2022 , Proceedings of the IEEERSJ International Conference on Intelligent Robots and Systems)

   Goal-based navigation in public places is critical for independent mobility and for breaking barriers that exist for blind or visually impaired (BVI) people in a sight-centric society. Through this work we present a proof-of-concept system that autonomously leverages goal-based navigation assistance and perception to identify socially preferred seats and safely guide its user towards them in unknown indoor environments. The robotic system includes a camera, an IMU, vibrational motors, and a white cane, powered via a backpack-mounted laptop. The system combines techniques from computer vision, robotics, and motion planning with insights from psychology to perform 1) SLAM and object localization, 2) goal disambiguation and scoring, and 3) path planning and guidance. We introduce a novel 2-motor haptic feedback system on the cane’s grip for navigation assistance. Through a pilot user study we show that the system is successful in classifying and providing haptic navigation guidance to socially preferred seats, while optimizing for users’ convenience, privacy, and intimacy in addition to increasing their confidence in independent navigation. The implications are encouraging as this technology, with careful design guided by the BVI community, can be adopted and further developed to be used with medical devices enabling the BVI population to better independentlymore »engage in socially dynamic situations like seat choice.« less
5. Scalable Optimization Methods for Incorporating Spatiotemporal Fractionation into Intensity-Modulated Radiotherapy Planning

   https://doi.org/10.1287/ijoc.2021.1070  

   Adibi, Ali ; Salari, Ehsan ( March 2022 , INFORMS Journal on Computing)

   It has been recently shown that an additional therapeutic gain may be achieved if a radiotherapy plan is altered over the treatment course using a new treatment paradigm referred to in the literature as spatiotemporal fractionation. Because of the nonconvex and large-scale nature of the corresponding treatment plan optimization problem, the extent of the potential therapeutic gain that may be achieved from spatiotemporal fractionation has been investigated using stylized cancer cases to circumvent the arising computational challenges. This research aims at developing scalable optimization methods to obtain high-quality spatiotemporally fractionated plans with optimality bounds for clinical cancer cases. In particular, the treatment-planning problem is formulated as a quadratically constrained quadratic program and is solved to local optimality using a constraint-generation approach, in which each subproblem is solved using sequential linear/quadratic programming methods. To obtain optimality bounds, cutting-plane and column-generation methods are combined to solve the Lagrangian relaxation of the formulation. The performance of the developed methods are tested on deidentified clinical liver and prostate cancer cases. Results show that the proposed method is capable of achieving local-optimal spatiotemporally fractionated plans with an optimality gap of around 10%–12% for cancer cases tested in this study. Summary of Contribution: The design ofmore »spatiotemporally fractionated radiotherapy plans for clinical cancer cases gives rise to a class of nonconvex and large-scale quadratically constrained quadratic programming (QCQP) problems, the solution of which requires the development of efficient models and solution methods. To address the computational challenges posed by the large-scale and nonconvex nature of the problem, we employ large-scale optimization techniques to develop scalable solution methods that find local-optimal solutions along with optimality bounds. We test the performance of the proposed methods on deidentified clinical cancer cases. The proposed methods in this study can, in principle, be applied to solve other QCQP formulations, which commonly arise in several application domains, including graph theory, power systems, and signal processing.« less