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Volume reconstruction techniques are gaining increasing interest in medical domains due to their potential to learn complex 3D structural information from sparse 2D images. Recently, neural radiance fields (NeRF), which implicitly model continuous radiance fields based on multi-layer perceptrons to enable volume reconstruction of objects at arbitrary resolution, have gained traction in natural image volume reconstruction. However, the direct application of NeRF to medical volume reconstruction presents unique challenges due to differences in imaging principles, internal structure requirements, and boundary delineation. In this paper, we evaluate different NeRF techniques developed for natural images, including sampling strategies, feature encoding, and the use of complimentary features, by applying them to medical images. We evaluate three state-of-the-art NeRF techniques on four datasets of medical images of different complexity. Our goal is to identify the strengths, limitations, and future directions for integrating NeRF into the medical domain.

Major trauma is a condition that can result in severe bone damage. Customised orthopaedic reconstruction allows for limb salvage surgery and helps to restore joint alignment. For the best possible outcome three dimensional (3D) medical imaging is necessary, but its availability and access, especially in developing countries, can be challenging. In this study, 3D bone shapes of the femur reconstructed from planar radiographs representing bone defects were evaluated for use in orthopaedic surgery. Statistical shape and appearance models generated from 40 cadaveric X-ray computed tomography (CT) images were used to reconstruct 3D bone shapes. The reconstruction simulated bone defects of between 0% and 50% of the whole bone, and the prediction accuracy using anterior–posterior (AP) and anterior–posterior/medial–lateral (AP/ML) X-rays were compared. As error metrics for the comparison, measures evaluating the distance between contour lines of the projections as well as a measure comparing similarities in image intensities were used. The results were evaluated using the root-mean-square distance for surface error as well as differences in commonly used anatomical measures, including bow, femoral neck, diaphyseal–condylar and version angles between reconstructed surfaces from the shape model and the intact shape reconstructed from the CT image. The reconstructions had average surface errors between 1.59 and 3.59 mm with reconstructions using the contour error metric from the AP/ML directions being the most accurate. Predictions of bow and femoral neck angles were well below the clinical threshold accuracy of 3°, diaphyseal–condylar angles were around the threshold of 3° and only version angle predictions of between 5.3° and 9.3° were above the clinical threshold, but below the range reported in clinical practice using computer navigation (i.e., 17° internal to 15° external rotation). This study shows that the reconstructions from partly available planar images using statistical shape and appearance models had an accuracy which would support their potential use in orthopaedic reconstruction.

Purpose Three-dimensional (3D) preoperative planning has become the gold standard for orthopedic surgeries, primarily relying on CT-reconstructed 3D models. However, in contrast to standing radiographs, a CT scan is not part of the standard protocol but is usually acquired for preoperative planning purposes only. Additionally, it is costly, exposes the patients to high doses of radiation and is acquired in a non-weight-bearing position. Methods In this study, we develop a deep-learning based pipeline to facilitate 3D preoperative planning for high tibial osteotomies, based on 3D models reconstructed from low-dose biplanar standing EOS radiographs. Using digitally reconstructed radiographs, we train networks to localize the clinically required landmarks, separate the two legs in the sagittal radiograph and finally reconstruct the 3D bone model. Finally, we evaluate the accuracy of the reconstructed 3D models for the particular application case of preoperative planning, with the aim of eliminating the need for a CT scan in specific cases, such as high tibial osteotomies. Results The mean Dice coefficients for the tibial reconstructions were 0.92 and 0.89 for the right and left tibia, respectively. The reconstructed models were successfully used for clinical-grade preoperative planning in a real patient series of 52 cases. The mean differences to ground truth values for mechanical axis and tibial slope were 0.52° and 4.33°, respectively. Conclusions We contribute a novel framework for the 2D–3D reconstruction of bone models from biplanar standing EOS radiographs and successfully use them in automated clinical-grade preoperative planning of high tibial osteotomies. However, achieving precise reconstruction and automated measurement of tibial slope remains a significant challenge.

Endovascular therapy in patients suffering from peripheral arterial disease shows high rates of restenosis. The poor clinical outcomes are commonly explained by the demanding mechanical environment due to leg movements, but the mechanisms responsible for restenosis remain unknown. In this study, we hypothesized that restenosis following revascularization is associated with hemodynamical markers derived from blood flow during leg flexion. Therefore, we performed personalized computational fluid dynamics (CFD) analyses of 20 patients, who underwent routine endovascular femoro-popliteal interventions. The CFD analyses were conducted using 3D models of the arterial geometry in straight and flexed positions, which were reconstructed from 2D angiographic images. Based on restenosis rates reported at 6-month follow-up, logistic regression analyses were performed to predict restenosis from hemodynamical parameters. Results showed that severe arterial deformations, such as kinking, induced by leg flexion in stented arteries led to adverse hemodynamic conditions that may trigger restenosis. A logistic regression analysis based solely on hemodynamical markers had an accuracy of 75%, which showed that flow parameters are sufficient to predict restenosis (p = 0.031). However, better predictions were achieved by adding the treatment method in the model. This suggests that a more accurate image acquisition technique is required to capture the localized modifications of blood flow following intervention, especially around the stented artery. This approach, based on the immediate postoperative configuration of the artery, has the potential to identify patients at increased risk of restenosis. Based on this information, clinicians could take preventive measures and more closely follow these patients to avoid complications.

Purpose Accurate three-dimensional (3D) models play crucial roles in computer assisted planning and interventions. MR or CT images are frequently used to derive 3D models but have the disadvantages that they are expensive or involving ionizing radiation (e.g., CT acquisition). An alternative method based on calibrated 2D biplanar X-ray images is highly desired. Methods A point cloud network, referred as LatentPCN, is developed for reconstruction of 3D surface models from calibrated biplanar X-ray images. LatentPCN consists of three components: an encoder, a predictor, and a decoder. During training, a latent space is learned to represent shape features. After training, LatentPCN maps sparse silhouettes generated from 2D images to a latent representation, which is taken as the input to the decoder to derive a 3D bone surface model. Additionally, LatentPCN allows for estimation of a patient-specific reconstruction uncertainty. Results We designed and conducted comprehensive experiments on datasets of 25 simulated cases and 10 cadaveric cases to evaluate the performance of LatentLCN. On these two datasets, the mean reconstruction errors achieved by LatentLCN were 0.83 mm and 0.92 mm, respectively. A correlation between large reconstruction errors and high uncertainty in the reconstruction results was observed. Conclusion LatentPCN can reconstruct patient-specific 3D surface models from calibrated 2D biplanar X-ray images with high accuracy and uncertainty estimation. The sub-millimeter reconstruction accuracy on cadaveric cases demonstrates its potential for surgical navigation applications.

In this paper, we introduce new sets of 2D and 3D rotation, scaling and translation invariants based on orthogonal radial Racah moments. We also provide theoretical mathematics to derive them. Thus, this work proposes in the first case a new 2D radial Racah moments based on polar representation of an object by one-dimensional orthogonal discrete Racah polynomials on non-uniform lattice, and a circular function. In the second case, we present new 3D radial Racah moments using a spherical representation of volumetric image by one-dimensional orthogonal discrete Racah polynomials and a spherical function. Further 2D and 3D invariants are extracted from the proposed 2D and 3D radial Racah moments respectively will appear in the third case. To validate the proposed approach, we have resolved three problems. The 2D/ 3D image reconstruction, the invariance of 2D/3D rotation, scaling and translation, and the pattern recognition. The result of experiments show that the Racah moments have done better than the Krawtchouk moments, with and without noise. Simultaneously, the mentioned reconstruction converges rapidly to the original image using 2D and 3D radial Racah moments, and the test 2D/3D images are clearly recognized from a set of images that are available in COIL-20 database for 2D image, and PSB database for 3D image.

State-of-the-art fluoroscopic knee kinematic analysis methods require the patient-specific bone shapes segmented from CT or MRI. Substituting the patient-specific bone shapes with personalizable models, such as statistical shape models (SSM), could eliminate the CT/MRI acquisitions, and thereby decrease costs and radiation dose (when eliminating CT). SSM based kinematics, however, have not yet been evaluated on clinically relevant joint motion parameters. Therefore, in this work the applicability of SSMs for computing knee kinematics from biplane fluoroscopic sequences was explored. Kinematic precision with an edge based automated bone tracking method using SSMs was evaluated on 6 cadaveric and 10 in-vivo fluoroscopic sequences. The SSMs of the femur and the tibia–fibula were created using 61 training datasets. Kinematic precision was determined for medial–lateral tibial shift, anterior–posterior tibial drawer, joint distraction–contraction, flexion, tibial rotation and adduction. The relationship between kinematic precision and bone shape accuracy was also investigated. The SSM based kinematics resulted in sub-millimeter (0.48–0.81mm) and approximately 1° (0.69–0.99°) median precision on the cadaveric knees compared to bone-marker-based kinematics. The precision on the in-vivo datasets was comparable to that of the cadaveric sequences when evaluated with a semi-automatic reference method. These results are promising, though further work is necessary to reach the accuracy of CT-based kinematics. We also demonstrated that a better shape reconstruction accuracy does not automatically imply a better kinematic precision. This result suggests that the ability of accurately fitting the edges in the fluoroscopic sequences has a larger role in determining the kinematic precision than that of the overall 3D shape accuracy.

Purpose Malposition of the acetabular component in total hip arthroplasty (THA) is a common surgical problem that can lead to hip dislocation, reduced range of motion and may result in early loosening. The aim of this study is to validate the accuracy and reproducibility of a single x-ray image based 2D/3D reconstruction technique in determining cup inclination and anteversion against two different computer tomography (CT)-based measurement techniques. Methods Cup anteversion and inclination of 20 patients after cementless primary THA was measured on standard antero-posterior (AP) radiographs with the help of the single x-ray 2D/3D reconstruction program and compared with two different 3D CT-based analyses [Ground Truth (GT) and MeVis (MV) reconstruction model]. Results The measurements from the single x-ray 2D/3D reconstruction technique were strongly correlated with both types of CT image-processing protocols for both cup inclination [ R² =0.69 (GT); R² =0.59 (MV)] and anteversion [ R² =0.89 (GT); R² =0.80 (MV)]. Conclusions The single x-ray image based 2D/3D reconstruction technique is a feasible method to assess cup position on postoperative x-rays. CT scans remain the golden standard for a more complex biomechanical evaluation when a lower tolerance limit (+/-2 degrees) is required.

Purpose To present a clinical validation of a novel technology called “3X” which allows for 3D prosthesis planning and treatment evaluation in total knee arthroplasty (TKA) using only 2D X-ray radiographs.Materials and methods After local institution review board approvals, 3X was evaluated on 43 cases (23 for preoperative planning and 20 for postoperative treatment evaluation). All the patients underwent CT scans according to a standard protocol. The results measured on the CT data were regarded as the ground truth. Additionally, two X-ray images were acquired for each affected leg and were used by 3X technology to derive patient-specific measurements of the leg. In total, we compared seven parameters for planning TKA and five parameters for postoperative prosthesis alignment.Results Our experimental results demonstrated that the mean distances between the surface models reconstructed from 2D X-rays and the associated surface models obtained from 3D CT data were smaller than 1.5 mm. The average differences for all angular parameters were smaller than 1.5∘ . In over 78% cases 3X technology derived the same femoral component size as the CT-based ground truth and this value went down to 70% when 3X technology was used to predict the size of tibial component.Conclusion 3X is a technology that allows for true 3D preoperative planning and postoperative treatment evaluation based on 2D X-ray radiographs.

Purpose:Fracture reduction and fixation of syndesmotic injuries is a common procedure in trauma surgery. An intra-operative evaluation of the surgical outcome is challenging due to high inter-individual anatomical variation. A comparison to the contralateral uninjured ankle would be highly beneficial but would also incur additional radiation and time consumption. In this work, we pioneer automatic contralateral side comparison while avoiding an additional 3D scan. Methods:We reconstruct an accurate 3D surface of the uninjured ankle joint from three low-dose 2D fluoroscopic projections. Through CNN complemented 3D shape model segmentation, we create a reference model of the injured ankle while addressing the issues of metal artifacts and initialization. Following 2D–3D multiple bone reconstruction, a final reference contour can be created and matched to the uninjured ankle for contralateral side comparison without any user interaction.Results:The accuracy and robustness of individual workflow steps were assessed using 81 C-arm datasets, with 2D and 3D images available for injured and uninjured ankles. Furthermore, the entire workflow was tested on eleven clinical cases. These experiments showed an overall average Hausdorff distance of 2.4±1.1 mm measured at clinical evaluation level.Conclusion:Reference contours of the contralateral side reconstructed from three projection images can assist surgeons in optimizing reduction results, reducing the duration of radiation exposure and potentially improving postoperative outcomes in the long term.