Focal Spot, Spring 2006

https://digitalcommons.wustl.edu/focal_spot_archives/1102/thumbnail.jp

Radiological Society of North America (RSNA) 3D printing Special Interest Group (SIG): Guidelines for medical 3D printing and appropriateness for clinical scenarios

Este número da revista Cadernos de Estudos Sociais estava em organização quando fomos colhidos pela morte do sociólogo Ernesto Laclau. Seu falecimento em 13 de abril de 2014 surpreendeu a todos, e particularmente ao editor Joanildo Burity, que foi seu orientando de doutorado na University of Essex, Inglaterra, e que recentemente o trouxe à Fundação Joaquim Nabuco para uma palestra, permitindo que muitos pudessem dialogar com um dos grandes intelectuais latinoamericanos contemporâneos. Assim, buscamos fazer uma homenagem ao sociólogo argentino publicando uma entrevista inédita concedida durante a sua passagem pelo Recife, em 2013, encerrando essa revista com uma sessão especial sobre a sua trajetória

LLUSD Articulator - Volume 29, Number 2

Contents: 4 | Good news for 2019 and beyond9 | New Center for Computer Aided Digital Design12 | Tord Lundgren’s multifaceted career16 | LLUSD’s 62nd conferring of degrees20 | Servants of service: the improbable journey of Yiming Li and Wu Zhang28 | Student Achievement Awards39 | NASDAD grateful for 75 years44 | Dental hygiene students publishedhttps://scholarsrepository.llu.edu/articulator/1015/thumbnail.jp

Air Force Institute of Technology Research Report 2011

This report summarizes the research activities of the Air Force Institute of Technology’s Graduate School of Engineering and Management. It describes research interests and faculty expertise; lists student theses/dissertations; identifies research sponsors and contributions; and outlines the procedures for contacting the school. Included in the report are: faculty publications, conference presentations, consultations, and funded research projects. Research was conducted in the areas of Aeronautical and Astronautical Engineering, Electrical Engineering and Electro-Optics, Computer Engineering and Computer Science, Systems and Engineering Management, Operational Sciences, Mathematics, Statistics and Engineering Physics

LLUSD Articulator - Volume 28, Number 1

Contents: 4 | Dean\u27s message7 | Careers in Dentistry Workshop8 | Marlene Schultz: first female dental student10 | Dean\u27s Circle13 | Norman Britton\u27s last chance fund16 | Beau Story\u27s story21 | First One Loma Linda Homecoming30 | Dental caries research revisited38 | Student research poster competition: LLU to CDA46 | NEWS50 | CDR acquires hi-res, 3D Micro-CT scannerhttps://scholarsrepository.llu.edu/articulator/1012/thumbnail.jp

CRANE: A Redundant, Multi-Degree-of-Freedom Computed Tomography Robot for Heightened Needle Dexterity within a Medical Imaging Bore

Computed Tomography (CT) image guidance enables accurate and safe minimally invasive treatment of diseases, including cancer and chronic pain, with needle-like tools via a percutaneous approach. The physician incrementally inserts and adjusts the needle with intermediate images due to the accuracy limitation of free-hand adjustment and patient physiological motion. Scanning frequency is limited to minimize ionizing radiation exposure for the patient and physician. Robots can provide high positional accuracy and compensate for physiological motion with fewer scans. To accomplish this, the robots must operate within the confined imaging bore while retaining sufficient dexterity to insert and manipulate the needle. This paper presents CRANE: CT Robotic Arm and Needle Emplacer, a CT-compatible robot with a design focused on system dexterity that enables physicians to manipulate and insert needles within the scanner bore as naturally as they would be able to by hand. We define abstract and measurable clinically motivated metrics for in-bore dexterity applicable to general-purpose intra-bore image-guided needle placement robots, develop an automatic robot planning and control method for intra-bore needle manipulation and device setup, and demonstrate the redundant linkage design provides dexterity across various human morphology and meets the clinical requirements for target accuracy during an in-situ evaluation.Comment: 20 pages, 13 figures, Transactions on Robotic

Using Support Vector Machines with Multiple Indices of Diffusion for Automated Classification of Mild Cognitive Impairment

Few studies have looked at the potential of using diffusion tensor imaging (DTI) in conjunction with machine learning algorithms in order to automate the classification of healthy older subjects and subjects with mild cognitive impairment (MCI). Here we apply DTI to 40 healthy older subjects and 33 MCI subjects in order to derive values for multiple indices of diffusion within the white matter voxels of each subject. DTI measures were then used together with support vector machines (SVMs) to classify control and MCI subjects. Greater than 90% sensitivity and specificity was achieved using this method, demonstrating the potential of a joint DTI and SVM pipeline for fast, objective classification of healthy older and MCI subjects. Such tools may be useful for large scale drug trials in Alzheimer's disease where the early identification of subjects with MCI is critical

Applications of artificial intelligence to prostate multiparametric MRI (mpMRI): Current and emerging trends

Prostate carcinoma is one of the most prevalent cancers worldwide. Multiparametric magnetic resonance imaging (mpMRI) is a non-invasive tool that can improve prostate lesion detection, classification, and volume quantification. Machine learning (ML), a branch of artificial intelligence, can rapidly and accurately analyze mpMRI images. ML could provide better standardization and consistency in identifying prostate lesions and enhance prostate carcinoma management. This review summarizes ML applications to prostate mpMRI and focuses on prostate organ segmentation, lesion detection and segmentation, and lesion characterization. A literature search was conducted to find studies that have applied ML methods to prostate mpMRI. To date, prostate organ segmentation and volume approximation have been well executed using various ML techniques. Prostate lesion detection and segmentation are much more challenging tasks for ML and were attempted in several studies. They largely remain unsolved problems due to data scarcity and the limitations of current ML algorithms. By contrast, prostate lesion characterization has been successfully completed in several studies because of better data availability. Overall, ML is well situated to become a tool that enhances radiologists\u27 accuracy and speed