Accuo - Needle Integrated Ultrasound Imaging

Last updated: 5/17/17 2:54pm

Summary

The system we will be improving consists of a single disc shaped PZT element mounted to the tip of a 14G lumbar puncture introducer needle. The system creates a B-mode image by pulsing the element while sweeping it through tissue and tracking its angular position. The goal of this project is to improve three elements of the real time ultrasound imaging system. By the end of the semester, I hope to integrate the beamforming algorithm and scan conversion for real time imaging, improve the speed of the system by basing the whole system on C++, and adding depth tracking to the device. (Revised goals, see below).

  • Students: Ernest Scalabrin
  • Mentor(s): Kai Zhang, Younsu Kim, Emad Boctor

Background, Specific Aims, and Significance

The lumbar puncture (LP) is a clinical diagnostic technique involving the collection of cerebrospinal fluid (CSF) from the subarachnoid space in a patient. In order to successfully collect CSF, clinicians have to blindly yet accurately navigate a needle through the L3-L5 intervertebral space, epidural space, dura mater, and arachnoid mater whilst avoiding nerves, blood vessels, and bone. More than 400,000 LP’s are performed annually but nearly 23.3% end in failure due to the myriad of challenges. These failures lead to misdiagnoses, treatment delays, and subsequent unnecessary and dangerous follow-up procedures. Patients with abnormal spinal anatomy and excess adipose tissue between skin and target structure, namely scoliotic and obese patients, suffer a significantly increased probability of LP failure.

Specific Aims:

  1. Decrease the time between data acquisition and visualization
    • Allows the device to be clinically useful in a time sensitive environment.

Deliverables

  • Minimum: Simulated Realtime Scanconversion Algorithm (4/18/17)
  • Expected: Simulated Realtime Beamforming Algorithm (4/25/17)
  • Maximum: Integrate Algorithms into Realtime Code (5/11/17)

Technical Approach

Use matlab imaging techniques to develop pseudo realtime data acquisition algorithm. Algorithm simulates real time data acquisition by taking A-lines in (from previously collected data) and processing individually to form a B-mode image.

Dependencies

Need for working PC for realtime data acquisition is required for the maximum deliverable. Will conduct experiments during meetings when I will have access to said machine.

Milestones and Status

  1. Milestone name: Scan Conversion Algorithm
    • Status: completed
  2. Milestone name: Beamforming Algorithm
    • Planned Date: 4/20/17
    • Expected Date: 5/16/17
    • Status: Completed
  3. Milestone name: Realtime integration

Reports and presentations

Project Bibliography

  • [1] Armon C., Evans R. W., “Addendum to assessment: prevention of post-lumbar puncture headaches,” Neurology 65, 510-512 (2005).
  • [2] American Society for Healthcare Risk Management, “Risk Management Handbook for Health Care Organizations”, Jossey-Bass, 5 (2009).
  • [3] Edwards C., Leira E. C., and Gonzalez-Alegre P., “Residency Training: A Failed Lumbar Puncture Is More about Obesity than Lack of Ability,” Neurology 84(10), e69-72 (2015).
  • [4] Shah K. H., Richard K. M., et al., “Incidence of traumatic lumbar puncture,” Academic Emergency Medicine 10(2), 151-4 (2003).
  • [5] Ahmed S. V., Jayawarna C., and Jude E., “Post lumbar puncture headache: Diagnosis and management,” Postgraduate Medical Journal 82(273), 713-716 (2006).
  • [6] Shaikh F., Brzezinski J., Alexander S., Arzola C., Carvalho J. C., Beyene J., and Sung L., “Ultrasound imaging for lumbar punctures and epidural catheterisations: systematic review and meta-analysis,” BMJ 346 (2013).
  • [7] Brook A. D., Burns J., Dauer E., Schoendfeld A. H., and Miller T. S., “Comparison of CT and Fluoroscopic Guidance for Lumbar Puncture in an Obese Population with Prior Failed Unguided Attempt,” Journal of NeuroInterventional Surgery 323-27 (2013).
  • [8] Engedal T. S., Ørding H., Vilholm O. J., “Changing the needle for lumbar punctures,” Clinical Neurology and Neurosurgery 130, 74-79 (2015).
  • [9] Tamas U., Abolmaesumi P., Jalal R., Welch M., Ayukawa I., Nagpal S., Lasso A., Jaeger M., Borschneck D., Fichtinger G., and Mousavi P., “Spinal Needle Navigation by Tracked Ultrasound Snapshots,” IEEE Transactions on Biomedical Engineering 59(10), 2766-72 (2012).
  • [10]Moore J., Clarke C., Bainbridge D., Wedlake C., Wiles A., Pace D., and Peters T., “Image Guidance for Spinal Facet Injections Using Tracked Ultrasound,” Medical Image Computing and Computer-Assisted Intervention, (2009).
  • [11]Chen E. C. S., Mousavi P., Gill S., Fichtinger G., Abolmaesumi P., “Ultrasound guided spine needle insertion,” Proc. SPIE 7625, 762538 (2010).
  • [12]Najafi M., Abolmaesumi P., Rohling R., “Single-Camera Closed-Form Real-Time Needle Tracking for UltrasoundGuided Needle Insertion,” Ultrasound in Medicine and Biology, 41(10), 2663-2676 (2015).
  • [13]Wang X. L., Stolka P. J., Boctor E., Hager G., Choti M., “The Kinect as an interventional tracking system,” Proc. SPIE 8316, 83160U (2012).
  • [14]Nagpal S., Abolmaesumi P., Rasoulian A., et al., “A multi-vertebrae CT to US registration of the lumbar spine in clinical data,” Int. J. CARS 10(9), 1371-81 (2015).
  • [15]Jensen J. A., Nikolov S. I., Gammelmark K. L., Pedersen M. H., “Synthetic aperture ultrasound imaging,” Ultrasonics 44(22), e5-e15 (2006).
  • [16]Zhang H. K., Cheng A., Bottenus N., Guo X., Trahey G. E., Boctor E. M., “Synthetic Tracked Aperture Ultrasound (STRATUS) Imaging: Design, Simulation, and Experimental Evaluation,” Journal of Medical Imaging 3(2), 027001 (2016).
  • [17]Bottenus N., Long W., Zhang H. K., Jakovljevic M., Bradway D. P., Boctor E. M., Trahey G. E., “Feasibility of Swept Synthetic Aperture Ultrasound Imaging,” IEEE Transactions on Medical Imaging 35(7), 1676-1685 (2016).

Other Resources and Project Files

courses/446/2017/446-2017-11/project.txt · Last modified: 2017/05/18 10:48 by escalab1@johnshopkins.edu




ERC CISST    LCSR    WSE    JHU