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Abstract: Objective: We present a novel method for intraoperative image-based bone surface reconstruction and its validation. Materials and methods: In the preoperative stage, we construct a CT-like intensity atlas of the anatomy of interest. In the intraoperative stage, we deformably register this atlas to fluoroscopic X-ray images of the patient anatomy. We iteratively refine the atlas-to-patient registration by establishing explicit correspondences between bone surfaces in the atlas and their projections in the X-ray images. The advantage of our method is its use of CT-quality intensity data for correspondence establishment, which eliminates the edge-detection problem and diminishes the miscorrespondence problem. We validate our method on two datasets: (1) an in vitro dry femur; (2) Digitally Reconstructed Radiographs, which were generated from 17 clinical CTs, and simulate realistic in vivo femurs. Results: The mean surface approximation error of our femur atlas was 0.85 ± 0.16 mm. On Digitally Reconstructed Radiographs, the mean surface reconstruction error was 1.40 ± 0.55 mm. On a dry femur, the mean surface reconstruction error was 1.44 mm. Conclusion: The results show that our reconstruction method is on par with the state of the art in reconstruction of ex vivo femurs. In addition, the results demonstrate that our method is effective in realistic simulations of the in vivo scenario.

Abstract: Purpose: Understanding acetabular orientation is important in many orthopaedic procedures. Acetabular orientation, usually described by anteversion and abduction angles, has uncertain measurement variability in adult patients. The goals of this study are threefold: (1) to describe a new method for computing patient-specific abduction/anteversion angles from a single CT study based on the identification of anatomical landmarks and acetabular rim points; (2) to quantify the inaccuracies associated with landmark selection in computing the acetabular angles; and (3) to quantify the variability and symmetry of acetabular orientation. Methods: A total of 25 CT studies from adult patients scanned for non-orthopaedic indications were retrospectively reviewed. The patients were randomly selected from the hospital’s database. Inclusion criteria were adults 20–65 years of age. Acetabular landmark coordinates were identified by expert observers and tabulated in a spreadsheet. Two sets of calculations were done using the data: (1) computation of the abduction and anteversion for each patient, and (2) evaluation of the variability of measurements in the same individual by the same surgeon. The results were tabulated and summary statistics computed. Results: This retrospective study showed that acetabular abduction and anteversion angles averaged 54° and 17°, respectively, in adults. A clinically significant intra-patient variability of >20° was found. We also found that the right and left side rim plane orientation were significantly correlated, but were not always symmetric. Conclusion: A new method of computing patient-specific abduction and anteversion angles from a CT study of the anterior pelvic plane and the left and right acetabular rim planes was reliable and accurate. We found that the acetabular rim plane can be reliably and accurately computed from identified points on the rim. The novelty of this work is that angular measurements are performed between planes on a 3-D model rather than lines on 2-D projections, as was done in the past.

Patient-specific models of anatomical structures and pathologies generated from volumetric medical images play an increasingly central role in many aspects of patient care. A key task in generating these models is the segmentation of anatomical structures and pathologies of interest. Although numerous segmentation methods are available, they often produce erroneous delineations that require time-consuming modifications. We present a new geometry-based algorithm for the reliable detection and correction of segmentation errors in volumetric medical images. The method is applicable to anatomical structures consisting of a few 3D star-shaped components. First, it detects segmentation errors by casting rays from the initial segmentation interior to its outer surface. It then classifies the segmentation surface into correct and erroneous regions by minimizing an energy functional that incorporates first- and second-order properties of the rays lengths. Finally, it corrects the segmentation errors by computing new locations for the erroneous surface points by Laplace deformation so that the new surface has maximum smoothness with respect to the rays-length gradient magnitude. Our evaluation on initial segmentations of 16 abdominal aortic aneurysm and 12 lung tumors in CT scans obtained by both adaptive region-growing and active contours level-set segmentation improved the volumetric overlap error by 66 and 70.5 % respectively, with respect to the ground-truth. The advantages of our method are that it is independent of the initial segmentation algorithm that covers a variety of anatomical structures and pathologies, that it does not require a shape prior, and that it requires minimal user interaction.

Purpose: This study addressed the challenge of detecting and classifying the severity of ductopenia in parotid glands, a structural abnormality characterized by a reduced number of salivary ducts, previously shown to be associated with salivary gland impairment. The aim of the study was to develop an automatic algorithm designed to improve diagnostic accuracy and efficiency in analyzing ductopenic parotid glands using sialo cone-beam CT (sialo-CBCT) images. Methods: We developed an end-to-end automatic pipeline consisting of three main steps: (1) region of interest (ROI) computation, (2) parotid gland segmentation using the Frangi filter, and (3) ductopenia case classification with a residual neural network (RNN) augmented by multidirectional maximum intensity projection (MIP) images. To explore the impact of the first two steps, the RNN was trained on three datasets: (1) original MIP images, (2) MIP images with predefined ROIs, and (3) MIP images after segmentation. Results: Evaluation was conducted on 126 parotid sialo-CBCT scans of normal, moderate, and severe ductopenic cases, yielding a high performance of 100% for the ROI computation and 89% for the gland segmentation. Improvements in accuracy and F1 score were noted among the original MIP images (accuracy: 0.73, F1 score: 0.53), ROI-predefined images (accuracy: 0.78, F1 score: 0.56), and segmented images (accuracy: 0.95, F1 score: 0.90). Notably, ductopenic detection sensitivity was 0.99 in the segmented dataset, highlighting the capabilities of the algorithm in detecting ductopenic cases. Conclusions: Our method, which combines classical image processing and deep learning techniques, offers a promising solution for automatic detection of parotid glands ductopenia in sialo-CBCT scans. This may be used for further research aimed at understanding the role of presence and severity of ductopenia in salivary gland dysfunction.

Abstract: Purpose: To measure and compare the clinical localization and registration errors in image-guided neurosurgery, with the purpose of revising current assumptions. Materials and methods: Twelve patients who underwent brain surgeries with a navigation system were randomly selected. A neurosurgeon localized and correlated the landmarks on preoperative MRI images and on the intraoperative physical anatomy with a tracked pointer. In the laboratory, we generated 612 scenarios in which one landmark pair was defined as the target and the remaining ones were used to compute the registration transformation. Four errors were measured: (1) fiducial localization error (FLE); (2) target registration error (TRE); (3) fiducial registration error (FRE); (4) Fitzpatrick’s target registration error estimation (F-TRE). We compared the different errors and computed their correlation. Results: The image and physical FLE ranges were 0.5–2.0 and 1.6–3.0 mm, respectively. The measured TRE, FRE and F-TRE were 4.1 ±  1.6, 3.9 ± 1.2, and 3.7 ±  2.2 mm, respectively. Low correlations of 0.19 and 0.37 were observed between the FRE and TRE and between the F-TRE and the TRE, respectively. The differences of the FRE and F-TRE from the TRE were 1.3 ±  1.0 mm (max = 5.5 mm) and 1.3 ±  1.2 mm (max = 7.3 mm), respectively. Conclusion: Contrary to common belief, the FLE presents significant variations. Moreover, both the FRE and the F-TRE are poor indicators of the TRE in image-to-patient registration.

Purpose: Estimation of glenoid bone loss in CT scans following shoulder dislocation is required to determine the type of surgery needed to restore shoulder stability. This paper presents a novel automatic method for the computation of glenoid bone loss in CT scans. Methods: The model-based method is a pipeline that consists of four steps: (1) computation of an oblique plane in the CT scan that best matches the glenoid face orientation; (2) selection of the glenoid oblique CT slice; (3) computation of the circle that best fits the posteroinferior glenoid contour; (4) quantification of the glenoid bone loss. The best-fit circle is computed with newly defined Glenoid Clock Circle Constraints. Results: The pipeline and each of its steps were evaluated on 51 shoulder CT scans (44 patients). Ground truth oblique slice, best-fit circle, and glenoid bone loss measurements were obtained manually from three clinicians. The full pipeline yielded a mean absolute error (%) for the bone loss deficiency of 2.3 ± 2.9 mm (4.67 ± 3.32%). The mean oblique CT slice selection difference was 1.42 ± 1.32 slices, above the observer variability of 1.74 ± 1.82 slices. The glenoid bone loss deficiency measure (%) on the ground truth oblique glenoid CT slice has a mean average error of 0.54 ± 1.03 mm (4.76 ± 3.00%), close to the observer variability of 0.93 ± 1.40 mm (2.98 ± 4.97%). Conclusion: Our pipeline is the first fully automatic method for the quantitative analysis of glenoid bone loss in CT scans. The computed glenoid bone loss report may assist orthopedists in selecting and planning surgical shoulder dislocation procedures.

Purpose: Radiographic parameters (RPs) provide objective support for effective decision making in determining clinical treatment of distal radius fractures (DRFs). This paper presents a novel automatic RP computation pipeline for computing the six anatomical RPs associated with DRFs in anteroposterior (AP) and lateral (LAT) forearm radiographs. Methods: The pipeline consists of: (1) segmentation of the distal radius and ulna bones with six 2D Dynamic U-Net deep learning models; (2) landmark points detection and distal radius axis computation from the segmentations with geometric methods; (3) RP computation and generation of a quantitative DRF report and composite AP and LAT radiograph images. This hybrid approach combines the advantages of deep learning and model-based methods. Results: The pipeline was evaluated on 90 AP and 93 LAT radiographs for which ground truth distal radius and ulna segmentations and RP landmarks were manually obtained by expert clinicians. It achieves an accuracy of 94 and 86% on the AP and LAT RPs, within the observer variability, and an RP measurement difference of 1.4 ± 1.2° for the radial angle, 0.5 ± 0.6 mm for the radial length, 0.9 ± 0.7 mm for the radial shift, 0.7 ± 0.5 mm for the ulnar variance, 2.9 ± 3.3° for the palmar tilt and 1.2 ± 1.0 mm for the dorsal shift. Conclusion: Our pipeline is the first fully automatic method that accurately and robustly computes the RPs for a wide variety of clinical forearm radiographs from different sources, hand orientations, with and without cast. The computed accurate and reliable RF measurements may support fracture severity assessment and clinical management.

Purpose: The treatment of pelvic and acetabular fractures remains technically demanding, and traditional surgical navigation systems suffer from the hand–eye mis-coordination. This paper describes a multi-view interactive virtual-physical registration method to enhance the surgeon’s depth perception and a mixed reality (MR)-based surgical navigation system for pelvic and acetabular fracture fixation. Methods: First, the pelvic structure is reconstructed by segmentation in a preoperative CT scan, and an insertion path for the percutaneous LC-II screw is computed. A custom hand-held registration cube is used for virtual-physical registration. Three strategies are proposed to improve the surgeon’s depth perception: vertices alignment, tremble compensation and multi-view averaging. During navigation, distance and angular deviation visual cues are updated to help the surgeon with the guide wire insertion. The methods have been integrated into an MR module in a surgical navigation system. Results: Phantom experiments were conducted. Ablation experimental results demonstrated the effectiveness of each strategy in the virtual-physical registration method. The proposed method achieved the best accuracy in comparison with related works. For percutaneous guide wire placement, our system achieved a mean bony entry point error of 2.76 ± 1.31 mm, a mean bony exit point error of 4.13 ± 1.74 mm, and a mean angular deviation of 3.04 ± 1.22°. Conclusions: The proposed method can improve the virtual-physical fusion accuracy. The developed MR-based surgical navigation system has clinical application potential. Cadaver and clinical experiments will be conducted in future.

Pelvic fracture is a severe trauma with a high rate of morbidity and mortality. Accurate and automatic diagnosis and surgical planning of pelvic fracture require effective identification and localization of the fracture zones. This is a challenging task due to the complexity of pelvic fractures, which often exhibit multiple fragments and sites, large fragment size differences, and irregular morphology. We have developed a novel two-stage method for the automatic identification and localization of complex pelvic fractures. Our method is unique in that it allows to combine the symmetry properties of the pelvic anatomy and capture the symmetric feature differences caused by the fracture on both the left and right sides, thereby overcoming the limitations of existing methods which consider only image or geometric features. It implements supervised contrastive learning with a novel Siamese deep neural network, which consists of two weight-shared branches with a structural attention mechanism, to minimize the confusion of local complex structures of the pelvic bones with the fracture zones. A structure-focused attention (SFA) module is designed to capture the spatial structural features and enhances the recognition ability of fracture zones. Comprehensive experiments on 103 clinical CT scans from the publicly available dataset CTPelvic1K show that our method achieves a mean accuracy and sensitivity of 0.92 and 0.93, which are superior to those reported with three SOTA contrastive learning methods and five advanced classification networks, demonstrating the effectiveness of identifying and localizing various types of complex pelvic fractures from clinical CT images.

Purpose: Radiological detection and follow-up of pancreatic cysts in multisequence MRI studies are required to assess the likelihood of their malignancy and to determine their treatment. The evaluation requires expertise and has not been automated. This paper presents MC3DU-Net, a novel multisequence cascaded pipeline for the detection and segmentation of pancreatic cysts in MRI studies consisting of coronal MRCP and axial TSE MRI sequences. Methods: MC3DU-Net leverages the information in both sequences by computing a pancreas Region of Interest (ROI) segmentation in the TSE MRI scan, transferring it to MRCP scan, and then detecting and segmenting the cysts in the ROI of the MRCP scan. Both the voxel-level ROI of the pancreas and the segmentation of the cysts are performed with 3D U-Nets trained with Hard Negative Patch Mining, a new technique for class imbalance correction and for the reduction in false positives. Results: MC3DU-Net was evaluated on a dataset of 158 MRI patient studies with a training/validation/testing split of 118/17/23. Ground truth segmentations of a total of 840 cysts were manually obtained by expert clinicians. MC3DU-Net achieves a mean recall of 0.80 ± 0.19, a mean precision of 0.75 ± 0.26, a mean Dice score of 0.80 ± 0.19 and a mean ASSD of 0.60 ± 0.53 for pancreatic cysts of diameter > 5 mm, which is the clinically relevant endpoint. Conclusion: MC3DU-Net is the first fully automatic method for detection and segmentation of pancreatic cysts in MRI. Automatic detection and segmentation of pancreatic cysts in MRI can be performed accurately and reliably. It may provide a method for precise disease evaluation and may serve as a second expert reader.

Purpose: Radiological follow-up of oncology patients requires the quantitative analysis of lesion changes in longitudinal imaging studies, which is time-consuming, requires expertise, and is subject to variability. This paper presents a comprehensive graph-based method for the automatic detection and classification of lesion changes in current and prior CT scans. Methods: The inputs are the current and prior CT scans and their organ and lesion segmentations. Classification of lesion changes is formalized as bipartite graph matching where lesion pairings are computed by adaptive overlap-based lesion matching. Six types of lesion changes are computed by connected components analysis. The method was evaluated on 208 pairs of lung and liver CT scans from 57 patients with 4600 lesions, 1713 lesion matchings and 2887 lesion changes. Ground-truth lesion segmentations, lesion matchings and lesion changes were created by an expert radiologist. Results: Our method yields a lesion matching rate accuracy of 99.7% (394/395) and 95.0% (1252/1318) for the lung and liver datasets. Precision and recall are > 0.99 and 0.94 and 0.95 (respectively) for the detection of lesion changes. The analysis of lesion changes helped the radiologist detect 48 missed lesions and 8 spurious lesions in the input ground-truth lesion datasets. Conclusion: The classification of lesion classification provides the clinician with a readily accessible and intuitive identification and classification of the lesion changes and their patterns in support of clinical decision making. Comprehensive automatic computer-aided lesion matching and analysis of lesion changes may improve quantitative follow-up and evaluation of disease status, assessment of treatment efficacy and response to therapy.

This paper describes some past and current research activities at the IBM Thomas J. Watson Research Center. We begin with a brief overview of the emerging field of computer-integrated surgery, followed by a research strategy that enables a computer-oriented research laboratory such as ours to participate in this emerging field. We then present highlights of our past and current research in four key areas - orthopaedics, craniofacial surgery, minimally invasive surgery, and medical modeling - and elaborate on the relationship of this work to emerging topics in computer-integrated surgery.

Purpose: Timely, accurate and reliable assessment of fetal brain development is essential to reduce short and long-term risks to fetus and mother. Fetal MRI is increasingly used for fetal brain assessment. Three key biometric linear measurements important for fetal brain evaluation are cerebral biparietal diameter (CBD), bone biparietal diameter (BBD), and trans-cerebellum diameter (TCD), obtained manually by expert radiologists on reference slices, which is time consuming and prone to human error. The aim of this study was to develop a fully automatic method computing the CBD, BBD and TCD measurements from fetal brain MRI. Methods: The input is fetal brain MRI volumes which may include the fetal body and the mother's abdomen. The outputs are the measurement values and reference slices on which the measurements were computed. The method, which follows the manual measurements principle, consists of five stages: (1) computation of a region of interest that includes the fetal brain with an anisotropic 3D U-Net classifier; (2) reference slice selection with a convolutional neural network; (3) slice-wise fetal brain structures segmentation with a multi-class U-Net classifier; (4) computation of the fetal brain midsagittal line and fetal brain orientation, and; (5) computation of the measurements. Results: Experimental results on 214 volumes for CBD, BBD and TCD measurements yielded a mean $L_1$\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$L_{1}$$\end{document}difference of 1.55 mm, 1.45 mm and 1.23 mm, respectively, and a Bland–Altman 95% confidence interval (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$CI_{{95}} )$$\end{document}of 3.92 mm, 3.98 mm and 2.25 mm, respectively. These results are similar to the manual inter-observer variability, and are consistent across gestational ages and brain conditions. Conclusions: The proposed automatic method for computing biometric linear measurements of the fetal brain from MR imaging achieves human-level performance. It has the potential of being a useful method for the assessment of fetal brain biometry in normal and pathological cases, and of improving routine clinical practice.

The American Academy of Pediatrics advises that the nutrition of preterm infants should target a body composition similar to that of a fetus in utero. Still, reference charts for intrauterine body composition are missing. Moreover, data on sexual differences in intrauterine body composition during pregnancy are limited.

Purpose: The elimination of abdominal tumors by percutaneous cryoablation has been shown to be an effective and less invasive alternative to open surgery. Cryoablation destroys malignant cells by freezing them with one or more cryoprobes inserted into the tumor through the skin. Alternating cycles of freezing and thawing produce an enveloping iceball that causes the tumor necrosis. Planning such a procedure is difficult and time-consuming, as it is necessary to plan the number and cryoprobe locations and predict the iceball shape which is also influenced by the presence of heating sources, e.g., major blood vessels and warm saline solution, injected to protect surrounding structures from the cold. Methods: This paper describes a method for fast GPU-based iceball modeling based on the simulation of thermal propagation in the tissue. Our algorithm solves the heat equation within a cube around the cryoprobes tips and accounts for the presence of heating sources around the iceball. Results: Experimental results of two studies have been obtained: an ex vivo warm gel setup and simulation on five retrospective patient cases of kidney tumors cryoablation with various levels of complexity of the vascular structure and warm saline solution around the tumor tissue. The experiments have been conducted in various conditions of cube size and algorithm implementations. Results show that it is possible to obtain an accurate result within seconds. Conclusion: The promising results indicate that our method yields accurate iceball shape predictions in a short time and is suitable for surgical planning.

We present a new method for flexible needle and patient localization in interventional CT procedures based on fractional CT scanning. Our method accurately localizes the trajectory of a flexible needle to which a spherical marker is attached at a known distance from the tip with respect to a baseline scan of patient in the CT scanner coordinate frame. The localization is achieved with a significantly lower dose compared to a full scan using sparse view angle sampling and without reconstructing the CT image of the repeat scan. Our method starts by performing rigid registration between the patient and the baseline scan in 3D Radon space computed from the sparse projection data. It then computes 2D projection difference images in which the flexible needle and the spherical marker appear as prominent features. Their 3D spatial locations are then automatically extracted from the projection images to accurately trace the flexible needle trajectory. To validate our method, we conducted registration and needle trajectory localization experiments in seven abdomen phantom scans using a short and a long flexible needle. Our experimental results yield a mean needle trajectory localization error of 0.7 ± 0.2 mm and a mean tip localization error of 2.4 ± 0.9 mm with a $$\times $$×7.5 radiation dose reduction with respect to a full CT scan. The significant radiation dose reduction enables more frequent needle trajectory localization during the needle insertion for a similar total dose, or a reduced total dose for the same localization frequency.

AbstractMany medical image segmentation methods require the selection of seed points inside the target structure. Often times, the location of these seed points determines the accuracy of the resulting target structure delineation and may lead to undesirably high delineation variability. We present Robust-Seed, a new method for automatically reducing the variability of manual and semi-automatic seed-based segmentation methods with respect to the seed point location without compromising the target structure segmentation accuracy. The inputs are a volumetric image, a seed point inside the target structure, and a seed-based segmentation method. The output is a new seed point that optimises the target structure segmentation result. The algorithm iteratively computes a new seed point location that improves the expected target structure segmentation for the given method. Experimental evaluation of seed-based fast-marching level-set and adaptive region growing segmentation of the kidney and the liver on 32 CT scans with ground-truth delineations shows that Robust-Seedyields a perfect robustness score with no significant compromise on the segmentation quality (paired t-test, p < 0.05). The key advantages of Robust-Seedare that it is automatic, that it is independent of target structure and segmentation method used, and that it applies to a wide class of anatomical structures and clinical tasks.

Stereotactic radiosurgery (SRS) is a common treatment for intracranial meningiomas. SRS is planned on a pre-therapy gadolinium-enhanced T1-weighted MRI scan (Gd-T1w MRI) in which the meningioma contours have been delineated. Post-SRS therapy serial Gd-T1w MRI scans are then acquired for longitudinal treatment evaluation. Accurate tumor volume change quantification is required for treatment efficacy evaluation and for treatment continuation. We present a new algorithm for the automatic segmentation and volumetric assessment of meningioma in post-therapy Gd-T1w MRI scans. The inputs are the pre- and post-therapy Gd-T1w MRI scans and the meningioma delineation in the pre-therapy scan. The output is the meningioma delineations and volumes in the post-therapy scan. The algorithm uses the pre-therapy scan and its meningioma delineation to initialize an extended Chan–Vese active contour method and as a strong patient-specific intensity and shape prior for the post-therapy scan meningioma segmentation. The algorithm is automatic, obviates the need for independent tumor localization and segmentation initialization, and incorporates the same tumor delineation criteria in both the pre- and post-therapy scans. Our experimental results on retrospective pre- and post-therapy scans with a total of 32 meningiomas with volume ranges 0.4–26.5 cm$$^{3}$$ 3 yield a Dice coefficient of $$87.0\, \pm \, 6.2$$ 87.0±6.2 % with respect to ground-truth delineations in post-therapy scans created by two clinicians. These results indicate a high correspondence to the ground-truth delineations. Our algorithm yields more reliable and accurate tumor volume change measurements than other stand-alone segmentation methods. It may be a useful tool for quantitative meningioma prognosis evaluation after SRS.

Prognostication for patients with critical conditions remains challenging, especially for very old individuals. Time-limited trials (TLT) are used to decrease prognostic uncertainty in the individual patient by monitoring the response to treatment over a pre-determined period of time. However, there are substantial difficulties with determining the length of that period. This study presents a probabilistic method to estimate a suitable duration of a TLT based on temporal profiles of uncertainty about critical care and outcome.

Volumetric measurements of plexiform neurofibromas (PNs) are time consuming and error prone, as they require the delineation of the PN boundaries, which is mostly impractical in the daily clinical setup. Accurate volumetric measurements are seldom performed for these tumors mainly due to their great dispersion, size and multiple locations. This paper presents a semiautomatic method for segmentation of PN from STIR MRI scans. Plexiform neurofibroma interactive segmentation tool (PNist) is a new tool to segment PNs in STIR MRI scans. The method is based on histogram tumor models computed from a training set. Experimental results from 28 datasets show an average absolute volume difference of 6.8 % with an average user time of approximately 7 min versus more than 13 min with manual delineation. In complex cases, the PNist user time is less than half in compared to state-of-the-art tools. PNist is a new method for the semiautomatic segmentation of PN lesions. Its simplicity and reliability make it unique among other state-of-the-art methods. It has the potential to become a clinical tool that allows the reliable evaluation of PN burden and progression.

Abstract: Objective: The most commonly used imaging device for assessment of fracture reduction is the two-dimensional X-ray fluoroscope. Two recently introduced 3D fluoroscopic devices, the Siremobil ISO-C3D (Siemens) and the C-InSight (Mazor Surgical Technologies), enable the surgeon to obtain spatial information for the assessment of articular reduction and hardware placement. The purpose of this study was to assess the reliability and accuracy of these two 3D fluoroscopic systems in measuring articular reduction in a cadaveric tibial plateau fracture. Methods: Six cadaveric knee specimens were osteotomized at the lateral tibial plateau and fixed with a maximal articular step-off of 0, 1, 2.5, 5 and 7.5 mm. Each specimen was scanned 10 times with two 3D fluoroscopes, the Siremobil ISO-C3D and the C-InSight. The resulting images were reformatted and interpreted for articular displacements at four different locations at the plateau level and were compared with high-resolution CT scans by an independent observer. Results: For the non-displaced fracture, no displacement (mean <  0.1 mm) was observed in either modality. The mean scanning time for the ISO-C3D was 2 min, while each C-InSight scan took 20 s. The readings at four different points along the malreduced fractures were similar for most measurements with either of the two modalities. The C-InSight readings were less accurate than those of the ISO-C3D, relative to the CT scan, but most errors were within clinically acceptable limits (< 2 mm) and used less radiation. Conclusions: Intraoperative 3D fluoroscopes can detect clinically significant intra-articular step-off with acceptable measurement errors, using newer devices that enable the use of a conventional C-arm and reduced radiation.

Abstract: Objective: We present a method and a validation study for the nearly automatic segmentation of liver tumors in CTA scans.Materials and methods: Our method inputs a liver CTA scan and a small number of user-defined seeds. It first classifies the liver voxels into tumor and healthy tissue classes with an SVM classification engine from which a new set of high- quality seeds is generated. Next, an energy function describing the propagation of these seeds is defined over the 3D image. The functional consists of a set of linear equations that are optimized with the conjugate gradients method. The result is a continuous segmentation map that is thresholded to obtain a binary segmentation. Results: A retrospective study on a validated clinical dataset consisting of 20 tumors from nine patients’ CTA scans from the MICCAI’08 3D Liver Tumors Segmentation Challenge Workshop yielded an average aggregate score of 67, an average symmetric surface distance of 1.76 mm (SD = 0.61 mm) which is better than the 2.0 mm of other methods on the same database, and a comparable volumetric overlap error (33.8 vs. 32.6%). The advantage of our method is that it requires less user interaction compared to other methods. Conclusion: Our results indicate that our method is accurate, efficient, and robust to wide variety of tumor types and is comparable or superior to other semi-automatic segmentation methods, with much less user interaction.

The goal of this study was to evaluate a new second generation of computer-assisted fluoroscopic navigation software for fracture reduction (SGFR) of long-bone fractures. The new software allows simultaneous tracking of two moving bone fragments.

Abstract: Purpose: Understanding acetabular orientation is important in many orthopaedic procedures. Acetabular orientation, usually described by anteversion and abduction angles, has uncertain measurement variability in adult patients. The goals of this study are threefold: (1) to describe a new method for computing patient-specific abduction/anteversion angles from a single CT study based on the identification of anatomical landmarks and acetabular rim points; (2) to quantify the inaccuracies associated with landmark selection in computing the acetabular angles; and (3) to quantify the variability and symmetry of acetabular orientation. Methods: A total of 25 CT studies from adult patients scanned for non-orthopaedic indications were retrospectively reviewed. The patients were randomly selected from the hospital’s database. Inclusion criteria were adults 20–65 years of age. Acetabular landmark coordinates were identified by expert observers and tabulated in a spreadsheet. Two sets of calculations were done using the data: (1) computation of the abduction and anteversion for each patient, and (2) evaluation of the variability of measurements in the same individual by the same surgeon. The results were tabulated and summary statistics computed. Results: This retrospective study showed that acetabular abduction and anteversion angles averaged 54° and 17°, respectively, in adults. A clinically significant intra-patient variability of >20° was found. We also found that the right and left side rim plane orientation were significantly correlated, but were not always symmetric. Conclusion: A new method of computing patient-specific abduction and anteversion angles from a CT study of the anterior pelvic plane and the left and right acetabular rim planes was reliable and accurate. We found that the acetabular rim plane can be reliably and accurately computed from identified points on the rim. The novelty of this work is that angular measurements are performed between planes on a 3-D model rather than lines on 2-D projections, as was done in the past.

Intraoperative fluoroscopically based computed tomography, integrated with a navigation system, holds great potential for improving visualization and navigation in orthopedic procedures. However, a limited field of view generated by the fluoroscopically based computed tomography has imposed a serious limitation, especially for navigation-based procedures. The device presented in this article enables one to overcome the limitation of the small field of view. The device has been evaluated in vitro by five physicians and has been used successfully in one clinical case. In general, we have developed a simple, low-cost in-house device that helps overcome an intrinsic limitation of high-cost systems.

Abstract: Purpose: To measure and compare the clinical localization and registration errors in image-guided neurosurgery, with the purpose of revising current assumptions. Materials and methods: Twelve patients who underwent brain surgeries with a navigation system were randomly selected. A neurosurgeon localized and correlated the landmarks on preoperative MRI images and on the intraoperative physical anatomy with a tracked pointer. In the laboratory, we generated 612 scenarios in which one landmark pair was defined as the target and the remaining ones were used to compute the registration transformation. Four errors were measured: (1) fiducial localization error (FLE); (2) target registration error (TRE); (3) fiducial registration error (FRE); (4) Fitzpatrick’s target registration error estimation (F-TRE). We compared the different errors and computed their correlation. Results: The image and physical FLE ranges were 0.5–2.0 and 1.6–3.0 mm, respectively. The measured TRE, FRE and F-TRE were 4.1 ±  1.6, 3.9 ± 1.2, and 3.7 ±  2.2 mm, respectively. Low correlations of 0.19 and 0.37 were observed between the FRE and TRE and between the F-TRE and the TRE, respectively. The differences of the FRE and F-TRE from the TRE were 1.3 ±  1.0 mm (max = 5.5 mm) and 1.3 ±  1.2 mm (max = 7.3 mm), respectively. Conclusion: Contrary to common belief, the FLE presents significant variations. Moreover, both the FRE and the F-TRE are poor indicators of the TRE in image-to-patient registration.

Abstract: Objective: We present a novel method for intraoperative image-based bone surface reconstruction and its validation. Materials and methods: In the preoperative stage, we construct a CT-like intensity atlas of the anatomy of interest. In the intraoperative stage, we deformably register this atlas to fluoroscopic X-ray images of the patient anatomy. We iteratively refine the atlas-to-patient registration by establishing explicit correspondences between bone surfaces in the atlas and their projections in the X-ray images. The advantage of our method is its use of CT-quality intensity data for correspondence establishment, which eliminates the edge-detection problem and diminishes the miscorrespondence problem. We validate our method on two datasets: (1) an in vitro dry femur; (2) Digitally Reconstructed Radiographs, which were generated from 17 clinical CTs, and simulate realistic in vivo femurs. Results: The mean surface approximation error of our femur atlas was 0.85 ± 0.16 mm. On Digitally Reconstructed Radiographs, the mean surface reconstruction error was 1.40 ± 0.55 mm. On a dry femur, the mean surface reconstruction error was 1.44 mm. Conclusion: The results show that our reconstruction method is on par with the state of the art in reconstruction of ex vivo femurs. In addition, the results demonstrate that our method is effective in realistic simulations of the in vivo scenario.

A new computer-based method for measuring periaxial rotation of healthy and fractured femurs from preoperative CT during closed femoral fracture reduction surgery is described. The method provides a comparative quantitative measure to align the distal and proximal femur fragments based on periaxial rotation. The periaxial rotation is defined in terms of patient-specific bone features. An algorithm for automatically extracting these features from the CT based on this definition has been developed. The algorithm extracts the condyle landmarks and neck axis of the healthy bone, determines its periaxial rotation, and extrapolates this data, assuming mirror symmetry between the healthy and fractured bones, to measure periaxial rotation between the fractured fragments. Unlike existing techniques, the method requires minimal user intervention. In a feasibility study, the method was applied to five dry femurs and one patient data set, and simulated a reduction based on the periaxial measurements with satisfactory results. The experiments showed the measured angle on the fractured femur to be within 1-4.5° of that of the healthy bone.

We present an in vitro study of rigid registration methods for computer-aided surgery. The goals of the study were to obtain accuracy measures empirically under optimal laboratory conditions, and to identify the weak links in the registration chain. Specifically, we investigated two common registration methods (contact-based registration and image-based landmark registration) and established a framework for comparing the accuracy of both methods. The phantoms, protocols, and algorithms for tool tip calibration, contact-based registration with an optical tracker, fluoroscopic X-ray camera calibration, and fluoroscopic X-ray image-based landmark registration are described. Average accuracies of 0.5 mm (1.5 mm maximum) and 2.75 mm (3.4 mm maximum) were found for contact-based and image-based landmark registration, respectively. Based on these findings, the camera calibration was identified as being the main source of error in image-based landmark registration. Protocol improvements and algorithmic refinements to improve the accuracy of image-based landmark registration are proposed.

No abstract.

We present a new algorithm for fine motion planning in geometrically complex situations. Geometrically complex situations have complex robot and environment geometry, crowded environments, narrow passages and tight fits. They require complex robot motions with coupled degrees of freedom. The algorithm constructs a path by incrementally building a graph of linearized convex configuration space cells and solving a series of linear optimization problems with varying objective functions. Its advantages are that it better exploits the local geometry of narrow passages in configuration space, and that its complexity does not significantly increase as the clearance of narrow passages decreases. We demonstrate the algorithm on examples which other planners could not solve.

This article describes FRACAS, a computer-integrated orthopedic system for assisting surgeons in performing closed medullary nailing of long bone fractures. FRACAS's goal is to reduce the surgeon's cumulative exposure to radiation and surgical complications associated with alignment and positioning errors of bone fragments, nail insertion, and distal screw locking. It replaces uncorrelated, static fluoroscopic images with a virtual reality display of three-dimensional bone models created from preoperative computed tomography and tracked intraoperatively in real time. Fluoroscopic images are used to register the bone models to the intraoperative situation and to verify that the registration is maintained. This article describes the system concept, software prototypes of preoperative modules (modeling, nail selection, and visualization), intraoperative modules (fluoroscopic image processing and tracking), and preliminary in vitro experimental results to date. Our experiments suggest that the modeling, nail selection, and visualization modules yield adequate results and that fluoroscopic image processing with submillimetric accuracy is practically feasible on clinical images. Comp Aid Surg 3:271–288 (1998). © 1999 Wiley-Liss, Inc.

When designing new devices, engineers seldomly start their design from scratch. Instead, they redesign or compose existing ones. Finding candidate devices based on their properties is thus an essential design subtask. To be effective, future intelligent computer-aided design (CAD) systems must be able to compare and evaluate existing devices. Due to the large number of known devices, such systems must create and maintain a devices' knowledge base, indexed by the properties of the devices.

We address the problem of building intelligent systems to reason about linear arithmetic constraints. We develop, along the lines of Logic Programming, a unifying framework based on the concept of Parametric Queries and a quasi-dual generalization of the classical Linear Programming optimization problem. Variable (quantifier) elimination is the key underlying operation which provides an oracle to answer all queries and plays a role similar to Resolution in Logic Programming. We discuss three methods for variable elimination, compare their feasibility, and establish their applicability. We then address practical issues of solvability and canonical representation, as well as dynamical updates and feedback. In particular, we show how the quasi-dual formulation can be used to achieve the discriminating characteristics of the classical Fourier algorithm regarding solvability, detection of implicit equalities and, in case of unsolvability, the detection of minimal unsolvable subsets. We illustrate the relevance of our approach with examples from the domain of spatial reasoning and demonstrate its viability with empirical results from two practical applications: computation of canonical forms and convex hull construction.