Patient-Specific Modeling of Scoliosis
Current complication rates for adolescent spinal deformity surgery are unacceptably high and in order to improve patient outcomes, the development of a simulation tool which enables the surgical strategy for an individual patient to be optimized is necessary. In this chapter we will present our work to date in developing and validating patient-specific modeling techniques to simulate and predict patient outcomes for surgery to correct adolescent scoliosis deformity. While these simulation tools are currently being developed to simulate adolescent idiopathic scoliosis patients, they will have broader application in simulating spinal disorders and optimizing surgical planning for other types of spine surgery. Our studies to date have highlighted the need for not only patient-specific anatomical data, but also patient-specific tissue parameters and biomechanical loading data, in order to accurately predict the physiological behaviour of the spine. Even so, patient-specific computational models are the state-of-the-art in computational biomechanics and offer much potential as a pre-operative surgical planning tool.
In conservative treatment for AIS, orthotic braces are used in an attempt to prevent curve progression, but in cases where this is not successful in arresting progression, or in patients with a severe deformity, corrective surgery is the only option. This involves attachment of rods to the patient’s spine using screws inserted into the spinal vertebrae (Fig. 1b). The implant construct shown in Fig. 1b is an anterior construct, however posterior constructs (not shown), in which long pairs of rods are attached to the back of the spine using screws, hooks and wires, are also commonly used in scoliosis correction surgery. In either approach, bone graft is applied at the time of surgery to encourage fusion of adjacent vertebrae, so that in a successful procedure the metal implant is required to provide stiffness and stability to the spine for the 3–6 months after surgery so that bony fusion can occur. After this the spinal loading is borne by the fused vertebrae and while the implant often remains in situ, it has no significant load bearing role.
Despite significant stated advantages of the anterior single rod procedure over posterior techniques, including reduced blood loss, reduced muscle dissection and improved cosmesis [22, 29, 41], the anterior fusion procedure still brings with it high (15–20%) implant-related complication rates [4, 31]. Such complications include screw pullout (Fig. 1d) and rod fracture (Fig. 1c). The most significant outcomes for the patient from the surgical screw and rod failures are the potential onset of back pain and loss of deformity correction .
Currently, surgical planning decisions are based on factors such as scoliosis curve severity and curve type (assessed using various radiographic classification systems), pre-operative patient assessment (e.g. flexibility tests) and surgeon judgement. These factors introduce a level of subjectivity into the surgical planning and as such, the surgical procedure chosen for a particular patient may differ considerably between surgeons . The ability of the surgeon to correct spinal deformity while avoiding complications requires a balance between the applied corrective forces (excessive forces will cause implant breakage or tissue damage) and the degree of deformity correction attempted (insufficient correction will leave an unbalanced spine after surgery which is prone to further deformity progression even after skeletal maturity). Attaining this balance is a complex biomechanical challenge. To address this challenge our research group has developed spinal simulation techniques, enabling patient-specific finite element (FE) models of individual patient’s spinal anatomy to be created. Using these FE models, the forces and deformations in the implant and spinal tissues are predicted for intra-operative and physiological spinal loads. Spinal deformity correction is essentially a structural problem, so the use of patient-specific finite element models during pre-operative planning of corrective surgery for scoliosis can potentially provide surgeons with a powerful support tool in making more informed decisions regarding the most appropriate procedure for an individual patient. While our research to date has focused on anterior, single rod scoliosis correction constructs, the patient-specific spinal modeling techniques we describe here are broadly applicable for simulating spinal disorders and optimizing surgical planning for other types of spinal surgery.
Other researchers in the field of spine biomechanics and simulation have made substantial contributions to addressing the need for a better understanding of scoliosis biomechanics (for example, ) and moreover, the need for patient-specific thoracolumbar spine models to inform surgeons in their clinical decision making (for example, [2, 16, 32]). However, the models developed to date often use highly idealized representations for the spinal anatomy, with either rigid bodies  or elastic beam elements  representing the spinal vertebrae; and lumped parameter (multi degree-of-freedom spring) representations to simulate the collective behaviour of the spinal ligaments, annulus fibrosus and nucleus pulposus in the intervertebral disc [2, 16]. Physiologically, the spinal vertebrae at each joint are connected by seven separate ligaments, with lines of action between specific bony landmarks on the adjacent vertebral bones. The intervertebral disc alone is a highly complex structure whose mechanics are governed by a complex interplay between collagen fibres embedded within adjacent lamellae of the annulus fibrosus and the fluid-filled, pressurized nucleus pulposus which the annulus surrounds . Furthermore, in previous models the contribution of the ribcage is either not included or represented using an idealized spring stiffness for adjoining spinal segments . However, the ribcage and its articulation with the spinal column (costovertebral joints) have been shown to be of considerable importance in governing the biomechanics of the spine, with as much as 40% decrease in spinal stiffness observed in cadaveric human spines following ribcage removal [27, 40]. All these structures play an important role in the overall biomechanics of both the intact and surgically altered spine.
Modern imaging techniques, visualization algorithms and image post-processing methods permit patient-specific anatomical representations for the osseous structures to be simulated. However, of equal importance in developing patient-specific spine models which are clinically useful, is the ability to derive not only patient specific osseous anatomy but patient specific material properties for the spinal tissues. Of particular importance are the soft tissue structures in the spine (ligaments, muscles and intervertebral discs) as these facilitate the articulation between adjacent vertebrae during physiological motion and primarily govern patient flexibility.
the vertebrae (both vertebral body and posterior elements);
the intervertebral discs (annulus fibrosus lamellae, collagen fibres and nucleus pulposus);
the seven spinal ligaments spanning each joint;
the zygapophyseal (facet) joints;
the vertebrae-rib articulations (costo-vertebral and costo-transverse joints); and
the ribcage (ribs, sternum and costo-sternal joints).
The following sections will detail our methods for simulating both pre-operative flexibility tests and intra-operative deformity corrections; and our investigations to determine patient-specific soft tissue parameters using physiological load cases on the intact and surgically altered scoliotic spine. This chapter concludes with a discussion of current challenges and future work with regard to developing clinically useful and physiologically accurate computer simulations of an individual patient’s spine.
2 Patient-Specific Modeling and Analysis
2.1 Model Development
The upper and lower edges of the ribs were separately represented using fifth order polynomials to describe the variation of the rib profile with distance along the edge from the costo-vertebral joint. Three separate polynomial equations described the profile of the rib edge in each plane and the polynomial constants were derived from user-selected landmarks along the rib edge. Similarly, user-selected landmarks defined the manubrium and sternum, with these structures represented as planar surfaces. The costal cartilage was modeled using a linear interpolation between the sternum/manubrium and the medial rib ends, while the costo-sternal connections were modeled as beam connectors for thoracic levels T1–T7.
Anatomically, the costo-vertebral joint articulates with both the superior and inferior postero-lateral cortices of the adjacent vertebra as well as the lateral surface of the intermediate intervertebral disc. As mentioned, the costo-vertebral joints have been shown to be of key importance in governing the biomechanics of the spine [27, 40] and in light of this, our thoracolumbar spine model represents these joints in detail . The costo-vertebral joints at each spinal level are represented using ‘clusters’ of beam elements, connecting the medial rib with the adjacent vertebral cortices and intervertebral disc surface (Fig. 4b, c). The beam properties were defined to provide a representative cross-sectional joint area of 46 mm2 which was based on the approximate articulating region between the rib and the postero-lateral vertebrae/disc surfaces. Our computational representation for the costo-vertebral joints is relatively complex in an attempt to capture the biomechanical behaviour of this joint. As such, we have conducted a separate finite element study on a single thoracic motion segment to ensure this representation correctly reproduces the physiological behaviour of a human joint (see below). The left and right costo-transverse joints were represented as rigid, kinematic constraints between the lateral aspect of the transverse processes and the posterior most surface of the adjacent rib (Fig. 4b).
The anterior longitudinal ligament (ALL), posterior longitudinal ligament (PLL), inter/supra-spinous ligament (SL), inter-transverse ligament (ITL), ligamentum flavum (LF) and capsular ligaments (CL) were represented as linear connectors between the bony attachment points for each ligament. No ligament wrapping has been represented. Similarly, the inter-costal soft tissue connections were represented as obliquely oriented linear connectors between the edges of adjacent ribs (Fig. 4a—circled in red for level T11–T12). Anatomically, these soft tissues connect adjacent ribs and vary in orientation along the rib and between ribs, so connector elements were inclined at 50–60° to the edge of the inferior rib.
Element types, material types and material parameters defined for each structure in the thoracolumbar spine model
E = 11,300 MPa υ = 0.2
First order brick
E = 140 MPa υ = 0.2
Posterior elements (pedicles, lamellae, transverse processes, spinous processes)
Zygapophyseal joint bone
As for cortical
Anulus fibrosus—ground substance
First order brick
C10 = 0.7
C01 = 0.2
Anulus fibrosus—collagen fibres
Tension-only, embedded rebar
E = 500 MPa υ = 0.3
3D, 4-node fluid
Costal rib bone/Sternum
E = 9,860 MPa υ = 0.3
E = 49 MPa
υ = 0.4
Ecompr = 245 N mm−1
Torsion stiffness, k = 4,167 N mm rad−1
Bending stiffness, k = 6706 N mm rad−1 (average antero-posterior and cranio-caudal flexion stiffness)
Ligaments—ALL and PLL
Ligaments—SL, ITL, LF,CL
E = 25 MPa
There is a paucity of experimental data characterising the mechanical behaviour of soft tissues from adolescent, scoliotic spines; with the only methods to derive such parameters being either direct, intra-operative measurement (which is highly challenging due to difficulties in physically accessing and accurately testing tissues) or inverse determination based on biomechanical modeling of pre-operative patient flexibility tests. Few researchers attempt to incorporate patient-specific tissue properties in thoracolumbar spine models for investigating scoliosis biomechanics .
In the absence of material parameters describing the mechanical behaviour of the adolescent osseous and soft tissue structures, materials in our models were initially derived from data for adult spinal tissues (Table 1). Bending and torsional stiffness parameters prescribed for the beams representing the costo-vertebral joints were derived from Lemosse et al. , who quantified the mechanical behaviour of human cadaveric costo-vertebral joints in the three axes of motion. The material parameters listed in Table 1 were defined as a benchmark set of material data.
The following sections will detail our methods for defining physiological loading conditions on both the intact and surgically altered spine and our recent findings relating to determination of individualized patient tissue properties.
2.2 Validation of the Single Motion Segment Representation
3 Simulating Load Cases in the Intact and Surgically Altered Spine
To date, our patient-specific modeling research has focused on two load cases; (1) a pre-operative clinical assessment known as the fulcrum bending radiograph, and (2) the actual intra-operative deformity correction performed by the surgeon to attach a single rod anterior implant and correct the scoliotic spinal deformity. These load cases are both well suited to initial model development and validation because they are biomechanically well-defined (muscle activation in both cases is minimal), and we have access to detailed clinical and radiographic data for assessment of model predictions. Given the previously mentioned lack of material property data for paediatric spinal tissues, we sought to investigate the importance of patient-specific soft tissue parameters in accurately predicting both pre-operative patient flexibility and intra-operative deformity correction.
Patient details and clinical Cobb angle measurements (degrees)
Pre-operative Cobb angle
Fulcrum bending Cobb angle
Surgically corrected Cobb angle
3.1 Fulcrum Bending Radiograph: Clinical Load Case 1
Patient-specific, CT-derived segmental torso weights for each vertebral level were determined using custom-developed software (Matlab R2007b). These weights were calculated using an average tissue density of 1.04 × 10−3 g mm−3  and were applied at the CT-derived centroid of each vertebral level in the transverse plane (Fig. 6b). A load vector representing the full weight of the uppermost arm and half the weight of the lowermost arm was applied at the centroid of the T1 vertebrae in each model. The magnitude of this vector equated to 9% body weight . Additionally, a load vector equivalent to the weight of the head and neck (8% body weight)  was simulated as a point load, applied superior to the uppermost vertebrae in each model. The bolster was simulated as a rigid body and rigidly constrained. A frictionless, tangential contact relationship was defined between the bolster surface and each rib and an exponential, normal contact relationship was used to represent the foam layer covering the bolster surface. The spine was free to rotate about a point simulating the contact between the pelvis and the table on which the patient rests.
Following analysis of the model under the fulcrum bending load case, the simulated FF was calculated for each of the five patient spine models and compared with the clinically measured value. The simulated FF was calculated using the predicted Cobb angle measured from the initial, undeformed spine geometry and the Cobb angle once the spine was positioned over the bolster (Fig. 7c). However, since CT scans on which the initial geometry of the FE models are based are performed while the patient is supine, the Cobb angle measured from the initial, unloaded model spine geometry is always less than the same patient’s Cobb angle measured clinically on a standing radiograph. This difference between scoliosis severity in supine and standing positions has been previously measured, and therefore can be corrected for. On average, the difference between the Cobb angle measured from supine CT images and standing radiographs is 9° . So that the predicted Cobb angle for the undeformed spine geometry could be compared to the clinically measured value (measured from standing radiographs), the predicted model value was therefore increased by 9° before the simulated FF was calculated.
The mismatch between simulated and clinical spine curve shapes displayed in Fig. 9b was similar for all three patients with a predicted FF outside the acceptable clinical error range (Fig. 8, patients 3–5). Both the qualitative and quantitative comparison of the model predicted and clinical FF results suggested that the patient-specific spine models for these patients (using adult spine tissue properties from Table 1) were overly stiff, with the predicted segmental rotations in the coronal plane too small for the given segmental torso weights. For these patients, the use of personalized tissue parameters which characterize the patient-specific tissue response would be expected to result in improved predictions of the spine behaviour. However, the predicted results for patients with a clinical FF less than 50% (clinically ‘stiff’), showed good agreement (i.e. less than ±5° error) with the clinical data, suggesting that for a subset of patients the benchmark tissue material properties derived from adult data are an appropriate representation for the adolescent tissues.
3.2 Towards Patient-Specific Soft Tissue Properties: What Have We Learned from the Fulcrum Bending Load Case?
Having compared the clinical and predicted FF for the benchmark set of (adult) tissue parameters with a pilot group of five patients, the next challenge is to determine an appropriate method to adjust these tissue parameters to achieve accurate patient-specific tissue properties for those patients where the benchmark (adult) tissue properties from existing literature do not provide a correct prediction of the physiological spine biomechanics. Spinal flexibility is influenced by several soft tissue structures, including the spinal ligaments, the intervertebral disc (annulus fibrosus and nucleus pulposus), the zygapophyseal joints and the cost-vertebral connections. Moreover, the influence of these structures may vary depending on the particular loading condition experienced by the spinal joints. Since spinal flexibility as measured by the fulcrum bending radiograph is a single numeric parameter (FF), the problem of determining a unique set of patient-specific tissue parameters which characterize this flexibility is difficult. However, our previous studies investigating patient-specific tissue parameters have concluded that only a few of these structures are of key importance in governing spinal flexibility [9, 19] and these structures will now be discussed.
Following this, as a first step toward determining patient-specific soft tissue properties, we used the fulcrum bending load case to determine which soft tissue structures were of most importance in governing spinal flexibility . In this study, the disc collagen fibre elastic modulus and the ligament nonlinear elastic stiffnesses were reduced by up to 40% in separate analyses and the effect on predicted vertebral rotations assessed. Note that in this study, stiffnesses for all ligaments were reduced at the same time. Results showed an average increase in coronal vertebral rotation of 3.1 and 13.4% with a 40% reduction in the ligament and collagen fibre stiffnesses, respectively. In a separate analysis, the change in FF due to complete removal of the intervertebral discs was investigated (this was a limiting case since this represented a 100% reduction in the stiffness of all disc materials). The results of this disc removal simulation demonstrated a three-fold increase in the predicted FF compared to the results for the fulcrum bending simulation using the intact model and benchmark tissue stiffnesses (FF = 39% for intact spine, increasing to FF = 120% after intervertebral disc removal). Note that just reducing the disc collagen fibre stiffness had a minimal effect on FF, only increasing it from FF = 39% (intact spine) to FF = 40% (60% reduction in disc collagen fibre stiffness). This study confirmed the importance of the intervertebral disc in influencing spinal flexibility (as measured by vertebral rotation) and showed that a lumped approach to altering the ligament stiffnesses does not necessarily capture the complex interaction in ligament load sharing which occurs across the spinal joints.
Clinical and predicted Fulcrum flexibility values (%), showing predicted results for both the benchmark tissue properties (Table 1) and altered CVJt stiffness
Pre-operative clinical Cobb angle
Predicted FF using Benchmark Tissue Properties (Table 1)
Changed CVJt Stiffness
Predicted FF using altered CVJt Properties
We conclude that changes to the CVJt stiffness can have an appreciable effect on spinal flexibility and this material sensitivity study went some way towards determining which of the spinal soft tissue structures are of most importance in governing spinal flexibility. Taken together with our previous studies, it is apparent that there is neither an individual soft tissue structure which completely governs spinal flexibility, nor is a lumped approach (varying all tissue properties at the same time) necessarily appropriate for varying soft tissue parameters to achieve patient-specific spine behaviour.
Rather, when patients are observed to be clinically ‘stiff’ (FF < 50%) or ‘flexible’ (FF < 50%) following assessment using the fulcrum bending test, this spinal behaviour is likely governed by a group of soft tissue structures acting in partnership. This suggests that, more detailed pre-operative flexibility assessment data than the currently available single scalar parameter (i.e. FF) is necessary to isolate and characterize patient-specific tissue properties for these structures. Ideally, clinical data characterizing the forces applied to the patient’s spine by the bolster while undergoing the fulcrum bending test would be measured for comparison with FE predicted forces, to assess both force and spinal deformation (and thus, vertebral segmental stiffnesses) in the fulcrum bending radiograph. Possibly, the distribution of segmental torso weights differs between clinically ‘stiff’ and clinically ‘flexible’ patients, resulting in variations in segmental weights which are currently not captured in the patient-specific segmental weights we derive from patient CT datasets. Since the fulcrum bending test has not yet been biomechanically characterized, the forces applied to the patient’s spine in contact with the bolster as well as the forces acting at regions of contact with the X-ray bed are not known. These measurements are a required step toward deriving patient-specific spinal tissue properties.
3.3 Intra-Operative Surgical Corrective Forces: Clinical Load Case II
During anterior scoliosis surgery, the key biomechanical step to reduce the spinal deformity occurs when the surgeon attaches a titanium alloy rod to the patient’s spine using screws which are inserted into the vertebrae within the extents of the structural curve.1 The surgeon applies stepwise compressive forces between pairs of screw heads in adjacent vertebrae (starting with the distal two screws and moving toward the proximal two screws in the construct) and once the desired level-wise correction has been achieved, each screw is locked onto the rod. This successive, level-wise deformity correction results in a reduction in the overall deformity of the structural curve. Prior to compression of the screw heads, the intervertebral discs between these vertebrae are partially removed (approximately the lateral half) and bone graft material is inserted into this disc space to encourage the vertebrae to fuse together in the corrected position.
This surgical process is the focus of our intra-operative surgical modeling, in which we attempt to develop a load case which simulates forces applied by the surgeon to the patient’s spine while undergoing the anterior, single rod, scoliosis correction procedure. As already mentioned, our research to date has focused on the anterior, single rod, scoliosis correction procedure since we have access to a detailed set of clinical data including clinically indicated pre-operative CT scans for AIS patients who undergo this surgery thorascopically (via a minimally invasive or key-hole approach). As such, this patient dataset has provided detailed clinical data for model validation of our patient-specific simulation techniques.
Screw placement/orientation—The vertebral screws were represented as embedded continuum elements in the vertebral bodies, assuming an idealized, perfectly bonded contact relationship between the surface of the screw shaft and underlying cancellous bone elements in the vertebral body (Fig. 12b). Note that this representation does not take into consideration the screw threads in contact with the underlying bone;
Discectomy levels—During surgery, the intervertebral discs between the vertebral bodies attached to the rod are partially removed (half the disc) and bone graft material is inserted into the disc space to promote bony fusion in the 3–6 months after surgery. These discectomy levels were simulated in the surgically altered spinal geometry by removing half the continuum elements representing the annulus fibrosus mesh, and by removing the entire fluid filled cavity representing the nucleus pulposus (Fig. 12b). Since this was an intra-operative load case, no fusion material was simulated as the bone graft offers no appreciable stiffness to the spine at the time of surgery. Interfacial contact between the exposed endplate surfaces of the adjacent vertebrae was simulated using an exponential, softened contact algorithm (normal contact) and Coulomb friction, μ = 0.3 (tangential sliding).
Rod geometry—The rod was represented with a user-defined length and diameter to match clinical data for each patient. The rod and screws are manufactured from Titanium alloy, which was represented as a linear elastic material (E = 108,000 MPa, υ = 0.3).
This surgical load case simulated the intra-operative compressive forces applied by the surgeon in order to correct the spinal deformity. Following removal of the intervertebral discs and insertion of the screws, the screw-to-screw compressive forces used to reduce the scoliosis deformity are simulated between successive vertebral levels within the structural curve. A compressive force is applied between the screw heads at adjacent vertebrae using connector elements, and once the required segmental correction has been simulated, locking of the screw onto the rod is simulated using bonded contact between the two FE meshes, thus simulating the cumulative, level-wise intra-operative correction of the spinal deformity.
Using a novel clinical measurement technique, our group (Cunningham et al. ) have determined the in vivo compressive forces applied intra-operatively for a series of patients undergoing the single rod, anterior corrective procedure. Using a strain-gauged surgical compression tool and data logging system, the study recorded a continual force tracing during the correction of each spinal joint, up until the point at which the screw was locked onto the rod. Using this dataset, Cunningham et al.  defined the average level-wise compressive force applied by the surgeons intra-operatively at each spinal level, relative to the apex of the scoliotic curve.2
3.4 Towards Patient-Specific Soft Tissue Properties: Insights from the Surgical Load Case
Scoliosis is a complex 3D deformity which results in abnormal curvature of the spine in all three anatomical planes. Generally, the spinal deformity will be observed as both a primary structural curve—which is the region where the lateral, side-to-side curvature is a manifestation of abnormal growth—and compensatory curves—which develop above and/or below the primary curve and are non-structural, occurring in order for the spine to maintain balance (a compensatory lumbar curve is visible below vertebral level T12 in Fig. 9a). In addition to the correction of the primary thoracic deformity adjacent to the implant, previous clinical studies of single rod anterior scoliosis surgery have shown that surgical correction of the primary structural deformity also results in a reduction in the compensatory deformity observed distal to the primary thoracic curve . Qualitative comparisons between the model and clinical results in the patient group just described, showed that the FE simulations predicted changes in both the primary (thoracic) and compensatory (lumbar) spinal curves following analysis of the intra-operative load case simulation. This suggests that the current passive osseo-ligamentous spine models are capable of reproducing at least the passive osseo-ligamentous aspect of this compensatory curve behaviour in the adolescent scoliotic spine.
The results of the intra-operative load case suggest that for a subset of patients, the use of a set of benchmark (adult) tissue properties based on existing literature provides a reasonable approximation to adolescent spinal tissue properties. This is in keeping with the conclusions drawn from the results of the Fulcrum Bending load case. However, for the remaining patients, the simulated deformity correction was considerably low in comparison to the clinical value, implying that the simulated spine was overly stiff in comparison to the actual tissues. In order to improve the agreement between the clinical and predicted results, the use of patient-specific paediatric soft tissue properties is essential.
The intra-operative deformity correction load case was applied using data for compressive forces measured in vivo  and such data is novel in the field of scoliosis biomechanics, with few previous studies having included measured force data in computational simulations of scoliosis surgery. However, the loading profile (Fig. 13) applied to each of the models in our pilot analyses to date, is averaged data from force measurements carried out on six patients. Preliminary FE investigations currently underway in our group suggest that the use of individual, patient-specific loading profiles (based on the in vivo data from Cunningham et al. ) will yield different predicted deformity corrections than the use of an ‘average’ loading profile for a particular type of surgical procedure. Thus as well as the need for patient-specific anatomy and patient specific tissue mechanical properties, patient-specific intra-operative surgical forces may also be required for realistic simulation of scoliosis surgery.
4 Conclusions and Future Directions
Patient-specific spine modeling presents a complex and multi-factorial challenge to not only accurately simulate the anatomy of the spine but also to appropriately replicate the physiological behaviour of the spinal soft tissues and to individualize the loading conditions applied to the spinal structures during surgical procedures. Our computational studies to date have demonstrated there is no one single soft tissue structure which governs the flexibility of the spine, but rather that there is a complex interaction between the spinal soft tissues which regulates the overall biomechanics of the spine.
In an attempt to better understand and ultimately derive patient-specific soft tissue properties it is appealing to take advantage of existing clinically performed patient flexibility assessments. Patient flexibility as assessed using the Fulcrum bending radiograph is strongly dependent on the relative stiffness of the various spinal soft tissues and derivation of tissue stiffnesses using these assessments provides in vivo data without the necessity for highly invasive testing of patient tissues. However, in order for such patient assessments to be useful in deriving patient-specific tissue properties, it is necessary for biomechanical characterization of these assessments to be carried out, through measurements of the forces applied to the patient’s spine.
Our studies to date demonstrate that the use of patient-specific anatomy alone is not sufficient to accurately simulate the biomechanical behaviour of the adolescent spine. The use of a set of benchmark material properties based on adult data can adequately reproduce the clinically observed behaviour of AIS patients for only a subset of patients. Patient-specific tissue properties are of key importance in creating accurate simulations of the spine and ribcage and in reproducing the clinically observed flexibility and motion of the spine.
In our work to date the focus has been on model development and validation by comparison with readily available clinical data such as FF and post-operative Cobb angle. However, once appropriately validated, the patient-specific models potentially provide a wealth of biomechanical information which can be used to assess the likely success of a proposed deformity correction procedure. For example, predicted rod strain is related to the likelihood of rod fracture under cyclic loads, predicted relative motion between adjacent vertebrae is related to the likelihood of successful bony fusion (as opposed to pseudarthrosis) following surgery, and predicted changes in tissue strain after a surgical intervention are valuable indicators of the degree of tissue remodeling which would be expected to occur in response to the altered spinal loading conditions.
The integration of patient-specific finite element simulations in a pre-operative surgical planning tool shows much potential. By utilizing a spine model which is truly personalized to the patient; in terms of anatomy, tissue properties and intra-operative corrective forces; accurate predictions for the outcomes of a particular surgical procedure should be possible prior to surgery. The overall aim of this approach to surgery simulations is to assist surgeons to plan the optimal biomechanical correction for a particular patient, while avoiding the risk of implant or tissue damage. This concept represents a paradigm shift in scoliosis surgery planning—by complementing a surgeon’s expertise with biomechanical simulation tools, it will be possible to accurately predict treatment outcomes for individual patients and ultimately, improve overall patient health post-operatively. There are significant challenges in realizing the clinical potential of patient specific biomechanical simulations such as these. Imaging modalities must allow pre-operative 3D imaging with sufficient resolution to accurately define patient-specific spinal anatomy, but without excessive radiation dose. Secondly, reliable biomechanical characterization techniques (based on clinical assessments such as the fulcrum bending radiograph for example) must be developed to ensure that patient-specific tissue mechanical properties can be prescribed which will correctly simulate spinal stiffness. Thirdly, prediction of the biomechanics of the surgically altered spine must be extended beyond the operating theatre to simulate physiologically relevant load cases after surgery (i.e. during gait and other post-operative activities involving muscle activation). Finally, issues of model convergence and solution time must be addressed to ensure that results can be obtained in a clinically relevant timeframe. These challenges can only be overcome through sustained effort by the biomechanical modeling community, but the goal of improved outcomes for complex spinal surgery patients make this research effort highly worthwhile.
The region of the spine with an intrinsic lateral curvature rather than a deformity related to a functional imbalance of the spinal soft tissues. The structural curve includes vertebrae which are within the extents of the spinal deformity measured by the Cobb angle.
The apex of the scoliotic curve is the most laterally deviated vertebra on a frontal radiograph.
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