Literature DB >> 23877453

Computed tomography guidance for spinal intervention: basics of technique, pearls, and avoiding pitfalls.

Vincent M Timpone1, Joshua A Hirsch, Christopher J Gilligan, Ronil V Chandra.   

Abstract

The utilization of spinal interventional pain techniques has grown rapidly over the last decade. However, practitioners use widely different techniques in these procedures, particularly in the use of image guidance. The importance of image guidance was highlighted by the fact that in recent systematic reviews on therapeutic effectiveness of epidural steroid injections and facet joint interventions, only studies that used image guidance were included. The choice of image guidance remains a matter of physician preference with conventional fluoroscopic or Computed Tomography (CT) guidance most common. There are many advantages to CT guidance for certain spinal interventional pain procedures, mainly due to increased needle tip positioning accuracy. CT guidance provides greater anatomical detail that facilitates accurate needle trajectory planning, monitoring and final placement. Unlike conventional fluoroscopy that may be hindered by tissue overlap and lack of surrounding anatomical detail CT guidance offers direct visualization of the entire needle trajectory and the surrounding soft tissue and bone structures. Large osteophytes and adjacent vascular structures can be identified and safely avoided. The goals of this narrative review are to provide a basic overview of CT techniques available for spinal interventional pain procedures, to discuss the potential advantages and disadvantages of CT guidance, to provide a simple step-by-step approach to use of CT guidance, to share technical pearls, and to discuss methods to avoid potential pitfalls. This review will provide interventional pain physicians with knowledge of relevant CT image acquisition techniques and appropriate radiation dose reduction strategies. This will contribute to increased technical success rates while reducing radiation dose to the patient and staff.

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Year:  2013        PMID: 23877453

Source DB:  PubMed          Journal:  Pain Physician        ISSN: 1533-3159            Impact factor:   4.965


  3 in total

1.  Deep Learning-Based Automatic Segmentation of Lumbosacral Nerves on CT for Spinal Intervention: A Translational Study.

Authors:  G Fan; H Liu; Z Wu; Y Li; C Feng; D Wang; J Luo; W M Wells; S He
Journal:  AJNR Am J Neuroradiol       Date:  2019-05-30       Impact factor: 3.825

Review 2.  Spine injections: the rationale for CT guidance.

Authors:  Sanja Bogdanovic; Reto Sutter; Veronika Zubler
Journal:  Skeletal Radiol       Date:  2022-09-23       Impact factor: 2.128

3.  Automated Magnetic Resonance Image Segmentation of Spinal Structures at the L4-5 Level with Deep Learning: 3D Reconstruction of Lumbar Intervertebral Foramen.

Authors:  Tao Chen; Zhi-Hai Su; Zheng Liu; Min Wang; Zhi-Fei Cui; Lei Zhao; Lian-Jun Yang; Wei-Cong Zhang; Xiang Liu; Jin Liu; Shu-Yuan Tan; Shao-Lin Li; Qian-Jin Feng; Shu-Mao Pang; Hai Lu
Journal:  Orthop Surg       Date:  2022-08-18       Impact factor: 2.279

  3 in total

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