Spinal pain relief procedures with the assistance of the MRI-updated statistical shape model.

BACKGROUND: Updating the statistical shape model (SSM) used in image guidance systems for the treatment of back pain using pre-op computed tomography (CT) and intra-op ultrasound (US) is challenging due to the scarce availability of pre-op images and the low resolution of the two imaging modalities. METHODS: A new approach is proposed here to update SSMs based on the sparse representation of the preoperative MRI images of patients as well as CT images, followed by displaying the injection needle and 3D tracking view of the patients' spine. RESULTS: The statistical analysis shows that updating the SSM using the patients' available MRI images (in more than 95% of the cases) instead of CT images (in less than 5%) will help maintain the required accuracy of needle injection based on the evaluation of injection in different parts of the phantom. CONCLUSION: The results show that using the proposed model helps reduce the dosage and processing time significantly while maintaining the precision required for the pain procedures.

Artificially enriching the training dataset of statistical shape models via constrained cage-based deformation.

The construction of a powerful statistical shape model (SSM) requires a rich training dataset that includes the large variety of complex anatomical topologies. The lack of real data causes most SSMs unable to generalize possible unseen instances. Artificial enrichment of training data is one of the methods proposed to address this issue. In this paper, we introduce a novel technique called constrained cage-based deformation (CCBD), which has the ability to produce unlimited artificial data that promises to enrich variability within the training dataset. The proposed method is a two-step algorithm: in the first step, it moves a few handles together, and in the second step transfers the displacements of these handles to the base mesh vertices to generate a real new instance. The evaluation of statistical characteristics of the CCBD confirms that our proposed technique outperforms notable data-generating methods quantitatively, in terms of the generalization ability, and with respect to specificity.