## 1 Introduction  
Over the last decade, there has been a steady shift in spinal surgical techniques toward using minimal incisions and a minimal amount of X-ray exposure (MIMA) to lower blood loss, accelerate patient recovery, and shorten hospital stays [1]. To precisely perform spine surgery with MIMA, it might be necessary to accurately register a patient and an image to develop surgical robots and image-guided navigation systems. Various image-to-patient registration technologies exist on image-guided navigation systems for spinal surgeries [2; 3; 4; 5; 6]. Recently, computer navigation was employed as a useful tool to aid in boosting the accuracy of spinal-surgery performance, particularly for the most practiced pedicle screw-placement technique [7; 8; 9; 10]. However, outcomes of image-guided robotic spine surgery remain disputable [10]. Among several errors that could be made by robots, deviation in robotic-arm positioning and the trajectory of the robotic drill might be influenced by pressure that occurs while going through tissue [11]. In earlier studies, soft-tissue-related errors in robot-assisted techniques were revealed [12; 13]. Therefore, to aid surgical procedures that use MIMA, utilizing aforementioned various image-to-patientregistration technologies on the basis of the surface information of patients' backs might not be enough to accurately estimate the spine location and possible biomechanics that could influence the trajectory of the robotic drill. Without taking numerous X-rays of a patient's various postures during surgery, a biomechanical spine model can simulate multiple possible scenarios with various patient postures that could occur during surgery. Surgical training and planning can be better informed by surgical instrument and position [14; 15]. Therefore, generating a biomechanical spine model could be beneficial.  
To generate a more accurate biomechanical spinal model, both hard-tissue shape information obtained from computerized-tomography (CT) images, and soft-tissue shape and elasticity information obtained from magnetic resonance imaging (MRI) or ultrasound imaging could be beneficial. However, not many studies include soft-tissue information when creating a biomechanical model. Recently, shear-wave elastography (SWE) was used to investigate musculoskeletal pathologies and conditions because tissue stiffness can be algebraically derived from shear-wave speed in the ultrasound machine [16]. Shear-wave speed is estimated by tissue motion induced by generated shear waves using acoustic radiation force along with an ultrasound probe [17; 18]. Although various studies investigated the elastic modulus of soft tissue using SWE, only a few investigated the elastic modulus of medial gastrocnemius muscles at different ankle positions using both SWE and strain elastography. Additionally, little research has been conducted on erector spinae and multifidus muscles at different trunk positions to find an ideal stretching posture using shear-wave ultrasound elastography [19; 20; 21]. The elastic modulus of muscles around the spine, such as erector spinae and multifidus, could be different depending on subject and posture. However, the elastic modulus of the erector spinae and multifidus muscles at the prone posture related to spine surgery on a Wilson table has not been investigated. Since spinal surgeries are performed on Wilson tables, it is necessary to investigate whether differences in the elastic modulus of muscles around the spine exist between surgical and resting postures.  
Therefore, the goal of this study was to compare skin thickness, muscle thickness, and the elastic modulus of muscles around the lumbar spine, such as erector spinae and multifidus muscles, using shear-wave ultrasound images of the prone posture on a flat table and a Wilson table. We hypothesize that the two postures led to significant differences in the elastic modulus of the erector spinae and multifidus muscles, and skin and muscle thickness.