2D Mapping Approach to Track Tissue Healing and Remodeling
The approach has high sensitivity and specificity in identifying tissues at risk of reinjury and is compatible with various MRI sequences
Background
Traumatic injuries of the connective tissue such as anterior cruciate ligament (ACL) are serious disabling injuries that can give rise to further complications such as joint degeneration. They are especially common among adolescents and adults who participate in physically demanding activities. Annually, more than 400,000 ACL surgeries are performed in the US. UP to 40% of surgically-treated ACL injuries experience reinjuries, resulting in significant patient morbidity and costs. The high reinjury rate is due, in part, to a lack of effective methods for assessing tissue healing and determining if the patient is at risk of reinjury.
Such methods can help in prescribing the types of activities that the patient can safely engage in during the healing process. Currently, ACL healing is assessed by examining such factors as the range of joint motion and patient balance. These methods are not accurate and suffer from observer bias. Existing MRI-based methods are also highly inaccurate because they assess gross ACL appearance in MRI and fail to account for subtle image intensity variations across the tissue.
Technology Overview
BCH researchers have developed a novel non-invasive method that enables accurate spatial mapping of the tissue healing process based on MRI. The invention is inspired by the fact that visualizing and interpreting a 3D image is difficult and inaccurate, while 2D slices lack the information contained in the full 3D volume. As such, the proposed method works by projecting the pixel intensities of a full 3D MR image onto a 2D plane that can be easily visualized and interpreted by a medical expert. In a typical embodiment of the invention, the ACL is first segmented in the knee MRI. The voxel intensities of the segmented region are then normalized to minimize the inter-scan variabilities. Then, these points are projected onto a common 2D plane. Typically, multiple voxels are projected onto each pixel in the 2D plane, enabling computation of simple statistics such as average and standard deviation. These computed statistics are then compared with the corresponding values computed on healthy tissue. This comparison can be used to rate tissue quality for each pixel in the 2D projection, thereby producing a map of tissue quality or healing. The generated map can be presented to a medical expert to make a clinical determination or it can be further processed by a computer for the same purpose.
The inventors generated healing maps for an ACL reconstruction (ACLR) and for a bridge-enhanced ACL repair (BEAR) over a period of 6-24 months after surgery. They found that the healing maps generated with the proposed method revealed the gradual remodeling of low-quality tissue into high-quality tissue over time.
Benefits
- Has a high sensitivity and specificity in identifying the tissue that are at risk of reinjury.
- Produces a detailed and accurate map of tissue quality.
- Non-invasive and works with various MRI sequences.
IP Status
- Patent application submitted
