Background Three-dimensional (3D)-printed saw guides are frequently used to optimize osteotomy results and are usually designed based on computed tomography (CT), despite the radiation burden, as radiation-less alternatives like magnetic resonance imaging (MRI) have inferior bone visualization capabilities. This study investigated the usability of MR-based synthetic-CT (sCT), a novel radiation-less bone visualization technique for 3D planning and design of patient-specific saw guides. Methods Eight human cadaveric lower arms (mean age: 78y) received MRI and CT scans as well as high-resolution micro-CT. From the MRI scans, sCT were generated using a conditional generative adversarial network. Digital 3D bone surface models based on the sCT and general CT were compared to the surface model from the micro-CT that was used as ground truth for image resolution. From both the sCT and CT digital bone models saw guides were designed and 3D-printed in nylon for one proximal and one distal bone position for each radius and ulna. Six blinded observers placed these saw guides as accurately as possible on dissected bones. The position of each guide was assessed by optical 3D-scanning of each bone with positioned saw guide and compared to the preplanning. Eight placement errors were evaluated: three translational errors (along each axis), three rotational errors (around each axis), a total translation (∆T) and a total rotation error (∆R). Results Surface models derived from micro-CT were on average smaller than sCT and CT-based models with average differences of 0.27 ± 0.30 mm for sCT and 0.24 ± 0.12 mm for CT. No statistically significant positioning differences on the bones were found between sCT- and CT-based saw guides for any axis specific translational or rotational errors nor between the ∆T (p = .284) and ∆R (p = .216). On Bland-Altman plots, the ∆T and ∆R limits of agreement (LoA) were within the inter-observer variability LoA. Conclusions This research showed a similar error for sCT and CT digital surface models when comparing to ground truth micro-CT models. Additionally, the saw guide study showed equivalent CT- and sCT-based saw guide placement errors. Therefore, MRI-based synthetic CT is a promising radiation-less alternative to CT for the creation of patient-specific osteotomy surgical saw guides.

Semantic segmentation of bone from lower extremity computerized tomography (CT) scans can improve and accelerate the visualization, diagnosis, and surgical planning in orthopaedics. However, the large field of view of these scans makes automatic segmentation using deep learning based methods challenging, slow and graphical processing unit (GPU) memory intensive. We investigated methods to more efficiently represent anatomical context for accurate and fast segmentation and compared these with state-of-the-art methodology. Six lower extremity bones from patients of two different datasets were manually segmented from CT scans, and used to train and optimize a cascaded deep learning approach. We varied the number of resolution levels, receptive fields, patch sizes, and number of V-net blocks. The best performing network used a multi-stage, cascaded V-net approach with 128 3 −64 3 −32 3 voxel patches as input. The average Dice coefficient over all bones was 0.98 ± 0.01, the mean surface distance was 0.26 ± 0.12 mm and the 95th percentile Hausdorff distance 0.65 ± 0.28 mm. This was a significant improvement over the results of the state-of-the-art nnU-net, with only approximately 1/12th of training time, 1/3th of inference time and 1/4th of GPU memory required.Comparison of the morphometric measurements performed on automatic and manual segmentations showed good correlation (Intraclass Correlation Coefficient [ICC] >0.8) for the alpha angle and excellent correlation (ICC >0.95) for the hip-kneeankle angle, femoral inclination, femoral version, acetabular version, Lateral Centre-Edge angle, acetabular coverage. The segmentations were generally of sufficient quality for the tested clinical applications and were performed accurately and quickly compared to state-of-the-art methodology from the literature.

Purpose. To develop a method that enables computed tomography (CT) to magnetic resonance (MR) image registration of complex deformations typically encountered in rotating joints such as the knee joint. Methods. We propose a workflow, denoted quaternion interpolated registration (QIR), consisting of three steps, which makes use of prior knowledge of tissue properties to initialise deformable registration. In the first step, the rigid skeletal components were individually registered. Next, the deformation of soft tissue was estimated using a dual quaternion-based interpolation method. In the final step, the registration was fine-tuned with a rigidity-constrained deformable registration step. The method was applied to paired, unregistered CT and MR images of the knee of 92 patients. It was compared to registration using B-Splines (BS) and B-Splines with a rigidity penalty (BSRP). Registration accuracy was evaluated using mutual information, and by calculating Dice similarity coefficient (DSC), mean absolute surface distance (MASD) and 95th percentile Hausdorff distance (HD95) on bone, and DSC on water and fat dominated tissue. To evaluate the rigidity of bone in the registration, the Jacobian determinant (JD) was calculated. Results. QIR achieved improved results with 0.93, 0.76 mm and 1.88 mm on the DSC, MASD and HD95 metrics on bone, compared to 0.87, 1.40 mm and 4.99 mm for method and 0.87, 1.40 mm and 3.56 mm for the BSRP method. The average DSC of water and fat was 0.77 and 0.86 for the QIR, 0.75 and 0.84 for BS and 0.74 and 0.84 for BSRP. Comparison of the median JD and median interquartile (IQR) ranges of the JD indicated that the QIR (1.00 median, 0.03 IQR) resulted in higher rigidity in the rigid skeletal tissues compared to the BS (0.98 median, 0.19 IQR) and BSRP (1.00 median, 0.05 IQR) methods. Conclusion. This study showed that QIR could improve the outcome of complex registration problems, encountered in joints involving rigid and non-rigid bodies such as occur in the knee, as compared to a conventional registration approach.

Background: Preoperative planning of lower-limb realignment surgical procedures necessitates the quantification of alignment parameters by using landmarks placed on medical scans. Conventionally, alignment measurements are performed on 2-dimensional (2D) standing radiographs. To enable fast and accurate 3-dimensional (3D) planning of orthopaedic surgery, automatic calculation of the lower-limb alignment from 3D bone models is required. The goal of this study was to develop, validate, and apply a method that automatically quantifies the parameters defining lower-limb alignment from computed tomographic (CT) scans.Methods: CT scans of the lower extremities of 50 subjects were both manually and automatically segmented. Thirty-two manual landmarks were positioned twice on the bone segmentations to assess intraobserver reliability in a subset of 20 subjects. The landmarks were also positioned automatically using a shape-fitting algorithm. The landmarks were then used to calculate 25 angles describing the lower-limb alignment for all 50 subjects. Results:The mean absolute difference (and standard deviation) between repeat measurements using the manual method was 2.01 ± 1.64 mm for the landmark positions and 1.05°± 1.48°for the landmark angles, whereas the mean absolute difference between the manual and fully automatic methods was 2.17 ± 1.37 mm for the landmark positions and 1.10°± 1.16°for the landmark angles. The manual method required approximately 60 minutes of manual interaction, compared with 12 minutes of computation time for the fully automatic method. The intraclass correlation coefficient showed good to excellent reliability between the manual and automatic assessments for 23 of 25 angles, and the same was true for the intraobserver reliability in the manual method. The mean for the 50 subjects was within the expected range for 18 of the 25 automatically calculated angles. Conclusions:We developed a method that automatically calculated a comprehensive range of 25 measurements that defined lower-limb alignment in considerably less time, and with differences relative to the manual method that were comparable to the differences between repeated manual assessments. This method could thus be used as an efficient alternative to manual assessment of alignment.Level of Evidence: Diagnostic Level III. See Instructions for Authors for a complete description of levels of evidence. Surgical correction of a lower-limb malalignment typically consists of an osteotomy in the femur and/or tibia. For surgical planning, the malalignment is quantified using predefined landmarks and angles, such as the center points of the femoral head, knee, and ankle, which define the mechanical hip-knee-ankle (mHKA) angle. Paley 1 described these angles, and the methods to restore the alignment, for almost all types of malalignments.Conventionally, these angles are measured on 2-dimensional (2D) radiographs to define the malalignment in the frontal and sagittal planes. The introduction of 3-dimensional (3D) imaging and planning now enables mor...

In this thesis, the ultimate goal has been to strive towards (automatic) computer assisted planning of reconstructive osteotomies. However, the quality of the output of such a workflow depends on the quality of all the intermediary steps, between the acquisition of a medical image, to the virtual reconstruction of the bone. In each chapter of this thesis, we have tried to overcome some of the challenges posed by these steps, and to evaluate the intermediate results. First, a method to improve the registration of CT to MRI scans for knee joint images was developed. Large deformations can arise due to differences in patient positioning between scanners, making registration complex. The study showed that non-rigid registration using deformable B-Splines could be improved by initializing the registration with a rigid registration of bones and estimating soft tissue deformation using a dual quaternion-based interpolation step. Second, an automatic and efficient deep learning-based method for semantic segmentation of six separate bones from lower extremity CT scans was proposed and validated. Existing deep learning segmentation algorithms were found to be slow and memory-intensive for this application. The optimized architecture of a cascaded U-net approach outperformed the state-of-the-art nn-Unet in memory efficiency, speed, and accuracy. Automated morphometric measurements on the hip joint were clinically validated, showing good to excellent correlation with measurements derived from manual segmentations. Third, the impact of using CT and MRI to segment bone and/or cartilage on pre-operative osteotomy planning of the forearm in adolescent patients was evaluated. An automated, deterministic planning method was developed to directly compare planning outcomes and avoid inter- or intraoperator variation. Segmentations of bone from both CT and MRI were used to assess the impact of different modalities on planning. Additionally, segmentations of bone and bone combined with cartilage from MR images were analyzed to study the influence of cartilage on planning outcomes. Results showed excellent correlation between realignment parameters across different segmentations, with small significant differences in the translational part of realignment. A positive correlation was found between cartilage amount and differences in planning on bone and bone with cartilage, suggesting the importance of considering cartilage impact on joint shape in young patients. However, further research is needed to explore MRI as a potential replacement for CT in osteotomy planning due to the small sample size and time-consuming manual segmentation. Lastly, a method to automatically quantify alignment parameters describing lower extremity morphology from 3D bone models was evaluated. A completely automatic pipeline was developed to extract these parameters using a CT scan as input. The alignment parameters were calculated using landmarks from previous studies and expanded upon to quantify all parameters needed for lower extremity realignment planning. This automatic method eliminated the need for manual operation and produced results that closely corresponded to intrarater variability. This advancement could pave the way for automatic realignment planning of lower extremities, addressing cases where a healthy contralateral example is unavailable for unilateral bone realignment planning.