Research On The Needle Insertion Force And Injection Pressure During The Simulated Ultrasound Guided Nerve Block

Ultrasound imaging guidance has been widely used in peripheral nerve block to guide the anaesthetist to insert the needle and observe injection fluid diffusion.However,needle tip visualization can be difficult to achieve because of requirement of high image solution and high skill,and bad visualization could lead to nerve injury and failure of anaesthesia.To better guide the anaesthetist to identify relative positions of the needle tip and nerve(RPNTN)and reduce the needle trauma,we attempted to use needle insertion force and injection pressure as two physical parameters to refine the RPNTN under ultrasound guided nerve block.The relationships between needle insertion force and RPNTN,injection pressure and RPNTN,and the needle insertion force parameters change in the tissues surrounding the nerve are not clear,we conducted prospective,study to investigate whether the needle insertion force and injection pressure under ultrasound guidance can be used to guide anesthesiologists in RPNTN.A multi-functional block needle was designed,and the needle insertion force and injection pressure measurement system were built based on the multi-functional block needle in this study.This system could trace and feedback the needle insertion force and injection pressure during the simulated ultrasound guided nerve block.Needle insertion force and injection pressure were put forwarded and used to achieve the identification of the RPNTN.The needle insertion force and injection pressure were statistically analyzed in different RPNTN(pre-contact,contact,forceful contact)during the simulated ultrasound guided nerve block,and the results indicated that needle insertion force(p<0.001)and injection pressure(p<0.001)both differentiate in three RPNTN.The judgment and prediction of different positions and tissues during the needle insertion procedure were realized by using the continuous needle insertion force.The continuous needle insertion force was studied in four distinguished RPNTN(Outside the nerve,Touch the nerve,On the nerve,Puncture the nerve)and four different tissues(Muscle,Fascia,Epineurium,Intraneural)without pressure injection,and the results revealed that continuous needle insertion force differed in the four RPNTN(p<0.01)and four different tissues(p<0.01),and the continuous needle insertion force could be distinguished totally in the RPNTN.The characteristics of Puncture force and Stiffness in different tissues are studied,and Puncture force and Stiffness are used as machine learning features to achieve effective classification of tissues.Puncture force and Stiffness were put forward,compared and statistically analyzed between the adipose tissue,fat,muscle,nerves and blood vessels samples from ex-vivo leg of lamb and soft embalmed cadavers at five needle insetion speeds 0.25mm/s,0.5mm/s,0.75mm/s,1mm/s,1.5mm/s,respectively,and then use the Puncture force and Stiffness as the machine learning features to classify the different tissues.The results showed Stiffness(p<0.05)and Puncture force(p<0.0001)varied according to tissue type in both Lamb shank and cadaver.Stifness(p=0.0147)and Puncture force(p=0.0462)differed according to needle insertion speed in the cadaver but not in the lamb shank,and the accuracy of tissue classification could achieve 80%by using the Puncture force and Stiffness as the machine learning features.This study provides device support and basic data for the application of needle insertion force and injection pressure in the needle tip position identification and prediction under the ultrasound guided nerve block.