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Purpose: Detecting knee orientation automatically from scout scans with high speed and accuracy is essential to a successful workflow of MR knee imaging. Although traditional methods of image processing such as rigid image registration and object detection are potentially available solutions, they are sensitive to image noise such as missing features due to the metal implants and anatomical variability in knee size and tissue composition. Method: In this study, a segmentation-based approach was proposed to calculate a 3-D transformation matrix that defined 3-D knee orientation using low-res MR scout scans. Specifically, 3-D U-net was used to segment a plane that was parallel to the knee meniscus plane and reconstruct the plane norm as one of the vectors (v1) needed for a 3d transformation matrix. A separate model of 3-D U-net was then trained to segment another plane that was perpendicular to the meniscus and reconstruct the plane norm as v2. A linear 3-D transformation matrix was then obtained for each patient case in 14 testing subjects that were initially manually rotated in small (group S) and large (group L) degrees for training. Angle corrected images were also visually compared against their corresponding ground truth. Results: The average of v1 and v2 error in group S were 5.62° and 5.12° , respectively, whereas the error average of these two vectors were 6.65° and 8.25° , respectively for group L. The standard deviation for v1 and v2 in group S and L were 2.51° , 2.84° , 5.65° , and 7.65° , respectively. The Dice similarity coefficient (DSC) of reconstructed v1 and v2 planes were 0.78, 0.70, 0.71, and 0.65 for group S and L. The qualitative assessment further showed consistent knee representation after correction for knees with heavy distortion and fatty tissue. Conclusion: Initial results suggest that our approach has the potential to automatically correct for small knee rotations commonly seen in clinical setting and is robust even under stress test for knees with anatomical structures (e.g. fatty tissue) that were even absent in the training data set and that appear heavily distorted.
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