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Individualized treatment planning in chronic back pain patients by advanced imaging and multi-parametric biomechanical models

Periodic Reporting for period 3 - iBack (Individualized treatment planning in chronic back pain patients by advanced imaging and multi-parametric biomechanical models)

Reporting period: 2018-06-01 to 2019-11-30

Chronic back pain is a major burden and source of disability worldwide. It is primarily attributed to biomechanical factors. In elderly patients, osteoporosis complicates the determination of the source of the pain. Surgery is often performed to treat instability-related pain and to restore the balance of the spine. However, when and how to perform surgery remains a highly subjective decision based on the surgeon's experience. Recent clinical trials demonstrated that surgery is not beneficial in patients with unspecific chronic lower back pain [1]. However, many patients with chronic lower back pain remain, where spinal fusion is still indicated due to other conditions like spondylolisthesis, spinal canal stenosis, fractures or tumors. Using calibrated opportunistic quantitative CTs we recently were able to predict surgery outcome in different surgical techniques. Additionally, we currently establish a machine-learning based model to predict vertebral fractures.
The objective of iBack is to break new ground in individualized therapy planning in these patients based on novel in-vivo imaging and image analysis. We are currently developing fully automatic approaches for individualized biomechanical models that will allow personalized treatment planning and outcome prediction.
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1. Mannion, A.F. et al. Spine J, 2013. 13(11): p. 1438-48.
"WP1: Image optimization
a) Computed tomography.
We evaluated the effect of several dose reduction techniques (both on the acquisition and reconstruction side) on BMD, trabecular parameters and results of finite-element (FE) analysis. We were able to show that sparse sampling allows for further dose reduction while still delivering robust, quantitative BMD values. Most importantly, CT imaging radiation dose could be reduced substantially to 64% with no impact on strength estimates obtained from FE analysis. To quantify BMD values also in non-dedicated CT exams (""opportunistic screening""), we established different methods to calibrate CT scans retrospectively.
[Löffler MT et al., Eur Radiol. 2019 Sep;29(9):4980-4989]

b) Magnetic Resonance Imaging
In collaboration with the ""Body Magnetic Resonance Research Group"" (PI Dimitios Karampinos PhD) we developed a Magnetic Resonance Imaging (MRI)-protocol for the spine including different qualitative as well as quantitative sequences. In addition to the qualitative morphological assessment of the thoracic and lumbar spine, our objective is to measure the bone structure and composition of the paraspinal soft tissue (especially musculature and intervertebral discs) quantitatively in patients suffering from lower back pain (Figure 1).
[Klupp E. et al., J Magn Reson Imaging. 2019 Sep;50(3):816-823]

WP2: Biomechanic Modelling
a) Segmentation and Quantification
We developed a software pipeline published as a freely available webservice (https://deep-spine.de) to fully automatically segment any given CT scan of the spine and derive its substructures. During last MICCAI, we hosted a grand challenge to increase its performance. We currently transfer this approach to MR imaging. From such an existing label mask we are able to extract geometrical features by automatically extracting the geometrical points of interest (GPOI) such as the centroid, vertebral body endplates, the spinal canal, ligament and muscle insertion points to be used for MBS (Figure 2).
[Sekubojina A., https://arxiv.org/abs/1902.02205]

b) Multi-Body Simulation (MBS) of the Spine
In order to provide boundary conditions for the investigation of loads on single spinal segments, an individualized multi-body system of the spine is being developed. Aim is the integration of patient-specific data, such as spinal alignment, patient's posture and material properties of bones, discs, ligaments and muscles. Therefore, we use the MBS-Software SIMPACK. The current version of the model uses data from CT scans registered to full-body x-rays to model the posture and spinal alignment of the patient. Intervertebral discs are modelled as spring-damper systems located in each center of joint between two vertebrae. Muscular structures are modelled in a way that allows to calculate their mechanical properties in consideration of their geometrical, histological and functional characteristics, such as physiological cross section area, intermuscular fat and activation. First simulations will investigate the influence of bodyweight and center of gravity on muscular activation and interspinal forces.

c) Finite Cell Method (FCM)
Based on MBS results and material properties from calibrated CT we apply the finite cell method (FCM) for the simulation of the mechanical interactions between the implanted material and the vertebrae. Finally, the simulations will be extended to estimate forces on discs, facet joints and the bone.
[Elhaddad M. et al., Int J Numer Method Biomed Eng. 2018 Apr;34(4):e2951]

WP3: Clinical validation
After the above described MR- protocol (see WP1b) was validated in a population of healthy control subjects, we started to scan patients suffering from lower back pain with and without spinal canal stenosis. We plan to examine 40 patients suffering from lower back pain with and without spinal canal stenosis as well as 20 healthy control subjects. The course and sensation of pain in these patients will be correlated with these MR-b"
The following results have already been obtained:
- The intramuscular fat fraction and fractional anisotropy are good measures for muscle degeneration and are highly (inversely) correlated with muscle strength.
- Intramuscular fat fraction of the back muscles is highly (inversely) correlated with quality of life.
- Low-dose MDCT with sparse sampling and iterative reconstruction techniques can be used to calculate bone mineral density, texture parameters and biomechanical parameters.
- CT imaging radiation dose could be reduced substantially with no impact on strength estimates obtained from finite element analysis.
- Machine learning based techniques are suitable to segment the spine in CT-datasets.
- FCM allows to precisely model bone strength and bone-screw interactions.

Hypotheses of our ongoing work / Results to be expected:
- An individualized modelling of spinal geometry helps to calculate local forces on vertebrae, discs and facet joints
- Such an approach allows for a better prediction of fracture risk, hardware failure and back pain.
- Strengthening back muscles leads to a direct reduction of pathologic forces at the spine as estimated with such a simulation and correlates with individual back pain and quality of life.
Comparison of different DTI sequences to assess back muscles
Automatic evaluation of vertebral geometry
Calculated stresses for a complex vertebral-screw-interaction