The application of kinematic data acquired during biomechanical testing to specimen-specific, three-dimensional models of the spine has emerged as a useful tool in spine biomechanics research. However, the development of these models is subject to segmentation error because of complex morphology and pathologic changes of the spine. This error has not been previously characterized.
Eight cadaveric lumbar spines were prepared and underwent computed tomography (CT) scanning. After disarticulation and soft-tissue removal, 5 individual vertebrae from these specimens were scanned a second time. The CT images of the full lumbar specimens were segmented twice each by 2 operators, and the images of the individual vertebrae with soft tissue removed were segmented as well. The solid models derived from these differing segmentation sessions were registered, and the distribution of distances between nearest neighboring points was calculated to evaluate the accuracy and precision of the segmentation technique.
Manual segmentation yielded root-mean-square errors below 0.39 mm for accuracy, 0.33 mm for intrauser precision, and 0.35 mm for interuser precision. Furthermore, the 95th percentile of all distances was below 0.75 mm for all analyses of accuracy and precision.
These findings indicate that such models are highly accurate and that a high level of intrauser and interuser precision can be achieved. The magnitude of the error presented here should inform the design and interpretation of future studies using manual segmentation techniques to derive models of the lumbar spine.
Lumbar spine; Manual segmentation; Precision; Accuracy; Solid models
Restoration of upper limb movements in subjects recovering from stroke is an essential keystone in rehabilitative practices. Rehabilitation of arm movements, in fact, is usually a far more difficult one as compared to that of lower extremities. For these reasons, researchers are developing new methods and technologies so that the rehabilitative process could be more accurate, rapid and easily accepted by the patient. This paper introduces the proof of concept for a new non-invasive FES-assisted rehabilitation system for the upper limb, called smartFES (sFES), where the electrical stimulation is controlled by a biologically inspired neural inverse dynamics model, fed by the kinematic information associated with the execution of a planar goal-oriented movement. More specifically, this work details two steps of the proposed system: an ad hoc markerless motion analysis algorithm for the estimation of kinematics, and a neural controller that drives a synthetic arm. The vision of the entire system is to acquire kinematics from the analysis of video sequences during planar arm movements and to use it together with a neural inverse dynamics model able to provide the patient with the electrical stimulation patterns needed to perform the movement with the assisted limb.
The markerless motion tracking system aims at localizing and monitoring the arm movement by tracking its silhouette. It uses a specifically designed motion estimation method, that we named Neural Snakes, which predicts the arm contour deformation as a first step for a silhouette extraction algorithm. The starting and ending points of the arm movement feed an Artificial Neural Controller, enclosing the muscular Hill's model, which solves the inverse dynamics to obtain the FES patterns needed to move a simulated arm from the starting point to the desired point. Both position error with respect to the requested arm trajectory and comparison between curvature factors have been calculated in order to determine the accuracy of the system.
The proposed method has been tested on real data acquired during the execution of planar goal-oriented arm movements. Main results concern the capability of the system to accurately recreate the movement task by providing a synthetic arm model with the stimulation patterns estimated by the inverse dynamics model. In the simulation of movements with a length of ± 20 cm, the model has shown an unbiased angular error, and a mean (absolute) position error of about 1.5 cm, thus confirming the ability of the system to reliably drive the model to the desired targets. Moreover, the curvature factors of the factual human movements and of the reconstructed ones are similar, thus encouraging future developments of the system in terms of reproducibility of the desired movements.
A novel FES-assisted rehabilitation system for the upper limb is presented and two parts of it have been designed and tested. The system includes a markerless motion estimation algorithm, and a biologically inspired neural controller that drives a biomechanical arm model and provides the stimulation patterns that, in a future development, could be used to drive a smart Functional Electrical Stimulation system (sFES). The system is envisioned to help in the rehabilitation of post stroke hemiparetic patients, by assisting the movement of the paretic upper limb, once trained with a set of movements performed by the therapist or in virtual reality. Future work will include the application and testing of the stimulation patterns in real conditions.
Defining myocardial contours is often the most time consuming portion of dynamic cardiac MRI image analysis. Displacement encoding with stimulated echoes (DENSE) is a quantitative MRI technique that encodes tissue displacement into the phase of the complex MRI images. Cine DENSE provides a time series of these images, thus facilitating the non-invasive study of myocardial kinematics. Epicardial and endocardial contours need to be defined at each frame on cine DENSE images for the quantification of regional displacement and strain as a function of time. This work presents a reliable and effective two dimensional semi-automated segmentation technique that uses the encoded motion to project a manually defined region of interest through time. Contours can then easily be extracted for each cardiac phase. This method boasts several advantages, including, 1. parameters are based on practical physiological limits, 2. contours are calculated for the first few cardiac phases, where it is difficult to visually distinguish blood from myocardium, and 3. the method is independent of the shape of the tissue delineated and can be applied to short- or long-axis views, and on arbitrary regions of interest. Motion-guided contours were compared to manual contours for six conventional and six slice-followed mid-ventricular short-axis cine DENSE datasets. Using an area measure of segmentation error, the accuracy of the segmentation algorithm was shown to be similar to inter-observer variability. In addition, a radial segmentation error metric was introduced for short-axis data. The average radial epicardial segmentation error was 0.36±0.08 and 0.40±0.10 pixels for slice followed and conventional cine DENSE, respectively, and the average radial endocardial segmentation error was 0.46±0.12 and 0.46±0.16 pixels for slice following and conventional cine DENSE, respectively. Motion-guided segmentation employs the displacement-encoded phase shifts intrinsic to DENSE MRI to accurately propagate a single set of pre-defined contours throughout the remaining cardiac phases.
Cardiac MRI; DENSE; myocardial tagging; segmentation; tissue tracking
Infectious diseases are the second leading cause of death worldwide. In order to better understand and treat them, an accurate evaluation using multi-modal imaging techniques for anatomical and functional characterizations is needed. For non-invasive imaging techniques such as computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography (PET), there have been many engineering improvements that have significantly enhanced the resolution and contrast of the images, but there are still insufficient computational algorithms available for researchers to use when accurately quantifying imaging data from anatomical structures and functional biological processes. Since the development of such tools may potentially translate basic research into the clinic, this study focuses on the development of a quantitative and qualitative image analysis platform that provides a computational radiology perspective for pulmonary infections in small animal models. Specifically, we designed (a) a fast and robust automated and semi-automated image analysis platform and a quantification tool that can facilitate accurate diagnostic measurements of pulmonary lesions as well as volumetric measurements of anatomical structures, and incorporated (b) an image registration pipeline to our proposed framework for volumetric comparison of serial scans. This is an important investigational tool for small animal infectious disease models that can help advance researchers’ understanding of infectious diseases.
We tested the utility of our proposed methodology by using sequentially acquired CT and PET images of rabbit, ferret, and mouse models with respiratory infections of Mycobacterium tuberculosis (TB), H1N1 flu virus, and an aerosolized respiratory pathogen (necrotic TB) for a total of 92, 44, and 24 scans for the respective studies with half of the scans from CT and the other half from PET. Institutional Administrative Panel on Laboratory Animal Care approvals were obtained prior to conducting this research. First, the proposed computational framework registered PET and CT images to provide spatial correspondences between images. Second, the lungs from the CT scans were segmented using an interactive region growing (IRG) segmentation algorithm with mathematical morphology operations to avoid false positive (FP) uptake in PET images. Finally, we segmented significant radiotracer uptake from the PET images in lung regions determined from CT and computed metabolic volumes of the significant uptake. All segmentation processes were compared with expert radiologists’ delineations (ground truths). Metabolic and gross volume of lesions were automatically computed with the segmentation processes using PET and CT images, and percentage changes in those volumes over time were calculated. (Continued on next page)(Continued from previous page) Standardized uptake value (SUV) analysis from PET images was conducted as a complementary quantitative metric for disease severity assessment. Thus, severity and extent of pulmonary lesions were examined through both PET and CT images using the aforementioned quantification metrics outputted from the proposed framework.
Each animal study was evaluated within the same subject class, and all steps of the proposed methodology were evaluated separately. We quantified the accuracy of the proposed algorithm with respect to the state-of-the-art segmentation algorithms. For evaluation of the segmentation results, dice similarity coefficient (DSC) as an overlap measure and Haussdorf distance as a shape dissimilarity measure were used. Significant correlations regarding the estimated lesion volumes were obtained both in CT and PET images with respect to the ground truths (R2=0.8922,p<0.01 and R2=0.8664,p<0.01, respectively). The segmentation accuracy (DSC (%)) was 93.4±4.5% for normal lung CT scans and 86.0±7.1% for pathological lung CT scans. Experiments showed excellent agreements (all above 85%) with expert evaluations for both structural and functional imaging modalities. Apart from quantitative analysis of each animal, we also qualitatively showed how metabolic volumes were changing over time by examining serial PET/CT scans. Evaluation of the registration processes was based on precisely defined anatomical landmark points by expert clinicians. An average of 2.66, 3.93, and 2.52 mm errors was found in rabbit, ferret, and mouse data (all within the resolution limits), respectively. Quantitative results obtained from the proposed methodology were visually related to the progress and severity of the pulmonary infections as verified by the participating radiologists. Moreover, we demonstrated that lesions due to the infections were metabolically active and appeared multi-focal in nature, and we observed similar patterns in the CT images as well. Consolidation and ground glass opacity were the main abnormal imaging patterns and consistently appeared in all CT images. We also found that the gross and metabolic lesion volume percentage follow the same trend as the SUV-based evaluation in the longitudinal analysis.
We explored the feasibility of using PET and CT imaging modalities in three distinct small animal models for two diverse pulmonary infections. We concluded from the clinical findings, derived from the proposed computational pipeline, that PET-CT imaging is an invaluable hybrid modality for tracking pulmonary infections longitudinally in small animals and has great potential to become routinely used in clinics. Our proposed methodology showed that automated computed-aided lesion detection and quantification of pulmonary infections in small animal models are efficient and accurate as compared to the clinical standard of manual and semi-automated approaches. Automated analysis of images in pre-clinical applications can increase the efficiency and quality of pre-clinical findings that ultimately inform downstream experimental design in human clinical studies; this innovation will allow researchers and clinicians to more effectively allocate study resources with respect to research demands without compromising accuracy.
Quantitative analysis; Pulmonary infections; Small animal models; PET-CT; Image segmentation; H1N1; Tuberculosis
To overcome the severe intensity inhomogeneity and blurry boundaries in HIFU (High Intensity Focused Ultrasound) ultrasound images, an accurate and efficient multi-scale and shape constrained localized region-based active contour model (MSLCV), was developed to accurately and efficiently segment the target region in HIFU ultrasound images of uterine fibroids.
We incorporated a new shape constraint into the localized region-based active contour, which constrained the active contour to obtain the desired, accurate segmentation, avoiding boundary leakage and excessive contraction. Localized region-based active contour modeling is suitable for ultrasound images, but it still cannot acquire satisfactory segmentation for HIFU ultrasound images of uterine fibroids. We improved the localized region-based active contour model by incorporating a shape constraint into region-based level set framework to increase segmentation accuracy. Some improvement measures were proposed to overcome the sensitivity of initialization, and a multi-scale segmentation method was proposed to improve segmentation efficiency. We also designed an adaptive localizing radius size selection function to acquire better segmentation results.
Experimental results demonstrated that the MSLCV model was significantly more accurate and efficient than conventional methods. The MSLCV model has been quantitatively validated via experiments, obtaining an average of 0.94 for the DSC (Dice similarity coefficient) and 25.16 for the MSSD (mean sum of square distance). Moreover, by using the multi-scale segmentation method, the MSLCV model’s average segmentation time was decreased to approximately 1/8 that of the localized region-based active contour model (the LCV model).
An accurate and efficient multi-scale and shape constrained localized region-based active contour model was designed for the semi-automatic segmentation of uterine fibroid ultrasound (UFUS) images in HIFU therapy. Compared with other methods, it provided more accurate and more efficient segmentation results that are very close to those obtained from manual segmentation by a specialist.
Sequential testing by coronary computed tomography angiography (CTA) and myocardial perfusion SPECT (MPS) obtained on standalone scanners may be needed to diagnose coronary artery disease (CAD) in equivocal cases. We have developed an automated technique for MPS-CTA registration and demonstrate its utility for improved MPS quantification by guiding the co-registered physiological (MPS) with anatomical CTA information.
Automated registration of MPS left ventricular (LV) surfaces with CTA coronary trees was accomplished by iterative minimization of voxel differences between pre-segmented CTA volumes and “motion-frozen” MPS data. Studies of 35 sequential patients (26 males), mean age 67±12 years with 64-slice coronary CTA, MPS and with available results of the invasive coronary angiography performed within 3 months were retrospectively analyzed. 3D coronary vessels and CTA slices were extracted and fused with quantitative MPS results mapped on LV surfaces and MPS coronary regions. Automatically co-registered CTA images and extracted trees were used to correct the MPS contours and to adjust the standard vascular region definitions for MPS quantification.
Automated co-registration of MPS and coronary CTA had the success rate of 96% as assessed visually; the average errors were 4.3±3.3 mm in translation and 1.5±2.6 deg in rotation on stress and 4.2±3.1 mm in translation and 1.7±3.2 deg in rotation on rest. MPS vascular region definition was adjusted in 17 studies and LV contours were adjusted in 11 studies using co-registered CTA images as a guide. CTA-guided MP analysis resulted in improved area under the receiver operator characteristics (ROC) curves for the detection of RCA and LCX lesions as compared to standard MPS analysis 0.84±0.08 vs. 0.70±0.11 for LCX (p = 0.03) and 0.92±0.05 vs. 0.75±0.09 (p=0.02) for RCA.
Software image co-registration of standalone coronary CTA and MPS obtained on separate scanners can be performed rapidly and automatically allowing CTA-guided contour and vascular territory adjustment on MPS for improved quantitative MPS analysis.
myocardial perfusion imaging; CT angiography; image registration; image fusion; coronary artery disease; image quantification
Study Design We present a patient-specific computer model created to translate two-dimensional (2D) fluoroscopic motion data into three-dimensional (3D) in vivo biomechanical motion data.
Objective The aim of this study is to determine the in vivo biomechanical differences in patients with and without acute low back pain. Current dynamic imaging of the lumbar spine consists of flexion–extension static radiographs, which lack sensitivity to out-of-plane motion and provide incomplete information on the overall spinal motion. Using a novel technique, in-plane and coupled out-of-plane rotational motions are quantified in the lumbar spine.
Methods A total of 30 participants—10 healthy asymptomatic subjects, 10 patients with low back pain without spondylosis radiologically, and 10 patients with low back pain with radiological spondylosis—underwent dynamic fluoroscopy with a 3D-to-2D image registration technique to create a 3D, patient-specific bone model to analyze in vivo kinematics using the maximal absolute rotational magnitude and the path of rotation.
Results Average overall in-plane rotations (L1–L5) in patients with low back pain were less than those asymptomatic, with the dominant loss of motion during extension. Those with low back pain also had significantly greater out-of-plane rotations, with 5.5 degrees (without spondylosis) and 7.1 degrees (with spondylosis) more out-of-plane rotational motion per level compared with asymptomatic subjects.
Conclusions Subjects with low back pain exhibited greater out-of-plane intersegmental motion in their lumbar spine than healthy asymptomatic subjects. Conventional flexion–extension radiographs are inadequate for evaluating motion patterns of lumbar strain, and assessment of 3D in vivo spinal motion may elucidate the association of abnormal vertebral motions and clinically significant low back pain.
low back pain; lumbar strain; spondylosis; biomechanics; kinematics
In vivo validation during functional loading.
To determine the accuracy and repeatability of a model-based tracking technique that combines subject-specific CT models and high-speed biplane X-ray images to measure three-dimensional (3D) in vivo cervical spine motion.
Summary of Background Data
Accurate 3D spine motion is difficult to obtain in vivo during physiological loading due to the inability to directly attach measurement equipment to individual vertebrae. Previous measurement systems were limited by two-dimensional (2D) results and/or their need for manual identification of anatomical landmarks, precipitating unreliable and inaccurate results. All previous techniques lack the ability to capture true 3D motion during dynamic functional loading.
Three subjects had 1.0 mm diameter tantalum beads implanted into their fused and adjacent vertebrae during ACDF surgery. High resolution CT scans were obtained following surgery and used to create subject-specific 3D models of each cervical vertebra. Biplane X-rays were collected at 30 frames per second while the subjects performed flexion/extension and axial rotation movements six months after surgery. Individual bone motion, intervertebral kinematics, and arthrokinematics derived from dynamic RSA served as a gold standard to evaluate the accuracy of the model-based tracking technique.
Individual bones were tracked with an average precision of 0.19 mm and 0.33 mm in non-fused and fused bones, respectively. Precision in measuring 3D joint kinematics in fused and adjacent segments averaged 0.4 mm for translations and 1.1° for rotations, while anterior and posterior disc height above and below the fusion were measured with a precision ranging between 0.2 mm and 0.4 mm. The variability in 3D joint kinematics associated with tracking the same trial repeatedly was 0.02 mm in translation and 0.06° in rotation.
3D cervical spine motion can be precisely measured in vivo with sub-millimeter accuracy during functional loading without the need for bead implantation. Fusion instrumentation did not diminish the accuracy of kinematic and arthrokinematic results. The semi-automated model-based tracking technique has excellent repeatability.
kinematics; accuracy; X-ray; image registration; RSA
We are developing a computer-aided diagnosis (CAD) system to classify malignant and benign lung nodules found on CT scans. A fully automated system was designed to segment the nodule from its surrounding structured background in a local volume of interest (VOI) and to extract image features for classification. Image segmentation was performed with a three-dimensional (3D) active contour (AC) method. A data set of 96 lung nodules (44 malignant, 52 benign) from 58 patients was used in this study. The 3D AC model is based on two-dimensional AC with the addition of three new energy components to take advantage of 3D information: (1) 3D gradient, which guides the active contour to seek the object surface, (2) 3D curvature, which imposes a smoothness constraint in the z direction, and (3) mask energy, which penalizes contours that grow beyond the pleura or thoracic wall. The search for the best energy weights in the 3D AC model was guided by a simplex optimization method. Morphological and gray-level features were extracted from the segmented nodule. The rubber band straightening transform (RBST) was applied to the shell of voxels surrounding the nodule. Texture features based on run-length statistics were extracted from the RBST image. A linear discriminant analysis classifier with stepwise feature selection was designed using a second simplex optimization to select the most effective features. Leave-one-case-out resampling was used to train and test the CAD system. The system achieved a test area under the receiver operating characteristic curve (Az) of 0.83±0.04. Our preliminary results indicate that use of the 3D AC model and the 3D texture features surrounding the nodule is a promising approach to the segmentation and classification of lung nodules with CAD. The segmentation performance of the 3D AC model trained with our data set was evaluated with 23 nodules available in the Lung Image Database Consortium (LIDC). The lung nodule volumes segmented by the 3D AC model for best classification were generally larger than those outlined by the LIDC radiologists using visual judgment of nodule boundaries.
computer-aided diagnosis; active contour model; object segmentation; classification; texture analysis; computed tomography (CT); malignancy; pulmonary nodule
Spinal fusion is a widely and successfully performed strategy for the treatment of spinal deformities and degenerative diseases. The general approach has been to stabilize the spine with implants so that a solid bony fusion between the vertebrae can develop. However, new implant designs have emerged that aim at preservation or restoration of the motion of the spinal segment. In addition to static, load sharing principles, these designs also require a profound knowledge of kinematic and dynamic properties to properly characterise the in vivo performance of the implants.
To address this, an apparatus was developed that enables the intraoperative determination of the load–displacement behavior of spinal motion segments. The apparatus consists of a sensor-equipped distractor to measure the applied force between the transverse processes, and an optoelectronic camera to track the motion of vertebrae and the distractor. In this intraoperative trial, measurements from two patients with adolescent idiopathic scoliosis with right thoracic curves were made at four motion segments each.
At a lateral bending moment of 5 N m, the mean flexibility of all eight motion segments was 0.18 ± 0.08°/N m on the convex side and 0.24 ± 0.11°/N m on the concave side.
The results agree with published data obtained from cadaver studies with and without axial preload. Intraoperatively acquired data with this method may serve as an input for mathematical models and contribute to the development of new implants and treatment strategies.
Scoliosis; Motion segment; Spine; Mechanical properties; In vivo measurements
The Subacromial Impingement Syndrome (SIS) is the most common diagnosed disorder of the shoulder in primary health care, but its aetiology is unclear. Conservative treatment regimes focus at reduction of subacromial inflammatory reactions or pathologic scapulohumeral motion patterns (intrinsic aetiology). Long-lasting symptoms are often treated with surgery, which is focused at enlarging the subacromial space by resection of the anterior part of the acromion (based on extrinsic aetiology). Despite that acromionplasty is in the top-10 of orthopaedic surgical procedures, there is no consensus on its indications and reported results are variable (successful in 48-90%). We hypothesize that the aetiology of SIS, i.e. an increase in subacromial pressure or decrease of subacromial space, is multi-factorial. SIS can be the consequence of pathologic scapulohumeral motion patterns leading to humerus cranialisation, anatomical variations of the scapula and the humerus (e.g. hooked acromion), a subacromial inflammatory reaction (e.g. due to overuse or micro-trauma), or adjoining pathology (e.g. osteoarthritis in the acromion-clavicular-joint with subacromial osteophytes).
We believe patients should be treated according to their predominant etiological mechanism(s). Therefore, the objective of our study is to identify and discriminate etiological mechanisms occurring in SIS patients, in order to develop tailored diagnostic and therapeutic strategies.
In this cross-sectional descriptive study, applied clinical and experimental methods to identify intrinsic and extrinsic etiologic mechanisms comprise: MRI-arthrography (eligibility criteria, cuff status, 3D-segmented bony contours); 3D-motion tracking (scapulohumeral rhythm, arm range of motion, dynamic subacromial volume assessment by combining the 3D bony contours and 3D-kinematics); EMG (adductor co-activation) and dynamometry instrumented shoulder radiographs during arm tasks (force and muscle activation controlled acromiohumeral translation assessments); Clinical phenotyping (Constant Score, DASH, WORC, and SF-36 scores).
By relating anatomic properties, kinematics and muscle dynamics to subacromial volume, we expect to identify one or more predominant pathophysiological mechanisms in every SIS patient. These differences in underlying mechanisms are a reflection of the variations in symptoms, clinical scores and outcomes reported in literature. More insight in these mechanisms is necessary in order to optimize future diagnostic and treatment strategies for patients with SIS symptoms.
Dutch Trial Registry (Nederlands Trial Register) NTR2283.
The correct segmentation of blood vessels in optical coherence tomography (OCT) images may be an important requirement for the analysis of intra-retinal layer thickness in human retinal diseases. We developed a shape model based procedure for the automatic segmentation of retinal blood vessels in spectral domain (SD)-OCT scans acquired with the Spectralis OCT system. The segmentation procedure is based on a statistical shape model that has been created through manual segmentation of vessels in a training phase. The actual segmentation procedure is performed after the approximate vessel position has been defined by a shadowgraph that assigns the lateral vessel positions. The active shape model method is subsequently used to segment blood vessel contours in axial direction. The automated segmentation results were validated against the manual segmentation of the same vessels by three expert readers. Manual and automated segmentations of 168 blood vessels from 34 B-scans were analyzed with respect to the deviations in the mean Euclidean distance and surface area. The mean Euclidean distance between the automatically and manually segmented contours (on average 4.0 pixels respectively 20 µm against all three experts) was within the range of the manually marked contours among the three readers (approximately 3.8 pixels respectively 18 µm for all experts). The area deviations between the automated and manual segmentation also lie within the range of the area deviations among the 3 clinical experts. Intra reader variability for the experts was between 0.9 and 0.94. We conclude that the automated segmentation approach is able to segment blood vessels with comparable accuracy as expert readers and will provide a useful tool in vessel analysis of whole C-scans, and in particular in multicenter trials.
(170.4500) Optical coherence tomography; (110.6880) Three-dimensional image acquisition; (100.0100) Image processing; (100.3008) Image recognition, algorithms and filters
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 SSM-s for computing knee kinematics from biplane fluoroscopic sequences was explored. Kinematic precision with an edge based automated bone tracking method using SSM-s was evaluated on 6 cadaver 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 (0.48–0.81 mm) and approximately one degree (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 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 the overall 3D shape accuracy.
Ttracking; SSM; Femur; Tibia; 2D/3D reconstruction
To demonstrate a multi-atlas segmentation approach to facilitating accurate and consistent delineation of low-contrast brachial plexuses on CT images for lung cancer radiotherapy.
Materials and Methods
We retrospectively identified 90 lung cancer patients with treatment volumes near the brachial plexus. Ten representative patients were selected to form an atlas group, and their brachial plexuses were delineated manually. We used deformable image registration to map each atlas brachial plexus to the remaining 80 patients. In each patient, a composite contour was created from 10 individual segmentations using the Simultaneous Truth and Performance Level Estimation (STAPLE) algorithm. This auto-delineated contour was reviewed and modified appropriately for each patient. We also performed 10 leave-one-out tests using the 10 atlases to validate the segmentation accuracy and demonstrate the contouring consistency using multi-atlas segmentation.
The multi-atlas segmentation took less than 2 minutes to complete. Contour modification took 5 minutes compared with 20 minutes for manual contouring from scratch. The multi-atlas segmentation from the 10 leave-one-out tests had a mean 3D volume overlap of 59.2% ± 8.2% and a mean 3D surface distance of 2.4 mm ± 0.5 mm. The distances between the individual and average contours in the 10 leave-one-out tests demonstrated much better contouring consistency for modified contours than for manual contours. The auto-segmented contours did not require substantial modification, demonstrated by the good agreement between the modified and auto-segmented contours in the 80 patients. Dose volume histograms of auto-segmented and modified contours were also in good agreement, showing that editing auto-segmented contours is clinically acceptable in view of the dosimetric impact.
Multi-atlas segmentation greatly reduced contouring time and improved contouring consistency. Editing auto-segmented contours to delineate the brachial plexus proved to be a better clinical practice than manually contouring from scratch.
brachial plexus; multi-atlas segmentation; deformable image registration; STAPLE
Deformable image registration (DIR) is an integral component for adaptive radiation therapy. However, accurate registration between daily cone-beam computed tomography (CBCT) and treatment planning CT is challenging, due to significant daily variations in rectal and bladder fillings as well as the increased noise levels in CBCT images. Another significant challenge is the lack of “ground-truth” registrations in the clinical setting, which is necessary for quantitative evaluation of various registration algorithms. The aim of this study is to establish benchmark registrations of clinical patient data.
Three pairs of CT/CBCT datasets were chosen for this IRB-approved retrospective study. On each image, in order to reduce the contouring uncertainty, ten independent sets of organs were manually delineated by five physicians. The mean contour set for each image was derived from the ten contours. A set of distinctive points (round natural calcifications and 3 implanted prostate fiducial markers) were also manually identified. The mean contours and point features were then incorporated as constraints into a B-spline based DIR algorithm. Further, a rigidity penalty was imposed on the femurs and pelvic bones to preserve their rigidity. A piecewise-rigid registration approach was adapted to account for the differences in femur pose and the sliding motion between bones. For each registration, the magnitude of the spatial Jacobian (|JAC|) was calculated to quantify the tissue compression and expansion. Deformation grids and finite-element-model-based unbalanced energy maps were also reviewed visually to evaluate the physical soundness of the resultant deformations. Organ DICE indices (indicating the degree of overlap between registered organs) and residual misalignments of the fiducial landmarks were quantified.
Manual organ delineation on CBCT images varied significantly among physicians with overall mean DICE index of only 0.7 among redundant contours. Seminal vesicle contours were found to have the lowest correlation amongst physicians (DICE=0.5). After DIR, the organ surfaces between CBCT and planning CT were in good alignment with mean DICE indices of 0.9 for prostate, rectum, and bladder, and 0.8 for seminal vesicles. The Jacobian magnitudes |JAC| in the prostate, rectum, and seminal vesicles were in the range of 0.4–1.5, indicating mild compression/expansion. The bladder volume differences were larger between CBCT and CT images with mean |JAC| values of 2.2, 0.7, and 1.0 for three respective patients. Bone deformation was negligible (|JAC|=~1.0). The difference between corresponding landmark points between CBCT and CT was less than 1.0 mm after DIR.
We have presented a novel method of establishing benchmark deformable image registration accuracy between CT and CBCT images in the pelvic region. The method incorporates manually delineated organ surfaces and landmark points as well as pixel similarity in the optimization, while ensuring bone rigidity and avoiding excessive deformation in soft tissue organs. Redundant contouring is necessary to reduce the overall registration uncertainty.
deformable image registration; finite element method; adaptive radiation therapy
Controlled laboratory study.
To evaluate the effect of lumbar degenerative disc diseases (DDDs) on motion of the facet joints during functional weight-bearing activities.
Summary of Background Data
It has been suggested that DDD adversely affects the biomechanical behavior of the facet joints. Altered facet joint motion, in turn, has been thought to associate with various types of lumbar spine pathology including facet degeneration, neural impingement, and DDD progression. However, to date, no data have been reported on the motion patterns of the lumbar facet joint in DDD patients.
Ten symptomatic patients of DDD at L4–S1 were studied. Each participant underwent magnetic resonance images to obtain three-dimensional models of the lumbar vertebrae (L2–S1) and dual fluoroscopic imaging during three characteristic trunk motions: left-right torsion, left-right bending, and flexion-extension. In vivo positions of the vertebrae were reproduced by matching the three-dimensional models of the vertebrae to their outlines on the fluoroscopic images. The kinematics of the facet joints and the ranges of motion (ROMs) were compared with a group of healthy participants reported in a previous study.
In facet joints of the DDD patients, there was no predominant axis of rotation and no difference in ROMs was found between the different levels. During left-right torsion, the ROMs were similar between the DDD patients and the healthy participants. During left-right bending, the rotation around mediolateral axis at L4–L5, in the DDD patients, was significantly larger than that of the healthy participants. During flexion-extension, the rotations around anterioposterior axis at L4–L5 and around craniocaudal axis at the adjacent level (L3–L4), in the DDD patients, were also significantly larger, whereas the rotation around mediolateral axis at both L2–L3 and L3–L4 levels in the DDD patients were significantly smaller than those of the healthy participants.
DDD alters the ROMs of the facet joints. The rotations can increase significantly not only at the DDD levels but also at their adjacent levels when compared to those of the healthy participants. The increase in rotations did not occur around the primary rotation axis of the torso motion but around the coupled axes. This hypermobility in coupled rotations might imply a biomechanical mechanism related to DDD.
degenerative disc diseases; facet joint kinematics; in vivo spine biomechanics; lumbar spine; range of motion
Little is known about the coupled motions of the spine during functional dynamic motion of the body. This study investigated the in vivo characteristic motion patterns of the human lumbar spine during a dynamic axial rotation of the body. Specifically, the contribution of each motion segment to the lumbar axial rotation and the coupled bending of the vertebrae during the dynamic axial rotation of the body were analyzed.
Eight asymptomatic subjects (M/F, 7/1; age, 40–60 years) were recruited. The lumbar segment of each subject was MRI scanned for construction of 3D models of the vertebrae from L2 to S1. The lumbar spine was then imaged using a dual fluoroscopic system while the subject performed a dynamic axial rotation from maximal left to maximal right in a standing position. The 3D vertebral models and the fluoroscopic images were used to reproduce the in vivo vertebral motion. In this study, we analyzed the primary left–right axial rotation, the coupled left–right bending of each vertebral segment from L2 to S1 levels.
The primary axial rotations of all segments (L2–S1) followed the direction of the body axial rotation. Contributions of each to the overall segment axial rotation were 6.7° ± 3.0° (27.9 %) for the L2–L3, 4.4° ± 1.2° (18.5 %) for the L3–L4, 6.4° ± 2.2° (26.7 %) for the L4–L5, and 6.4° ± 2.6° (27.0 %) for the L5–S1 vertebral motion segments. The upper segments of L2–L3 and L3–L4 demonstrated a coupled contralateral bending towards the opposite direction of the axial rotation, while the lower segments of L4–L5 and L5–S1 demonstrated a coupled ipsilateral bending motion towards the same direction of the axial rotation. Strong correlation between the primary axial rotation and the coupled bending was found at each vertebral level. We did not observe patterns of coupled flexion/extension rotation with the primary axial rotation.
This study demonstrated that a dynamic lumbar axial rotation coupling with lateral bendings is segment–dependent and can create a coordinated dynamic coupling to maintain the global dynamic balance of the body. The results could improve our understanding of the normal physiologic lumbar axial rotation and to establish guidelines for diagnosing pathological lumbar motion.
Dynamic lumbar axial rotation; Coupled motion; Lateral bending; Compensatory scoliosis; Dynamic spine balance
Minimally-invasive measurement of continuous inter-vertebral motion in clinical settings is difficult to achieve. This paper describes the reliability, validity and radiation exposure levels in a new Objective Spinal Motion Imaging Assessment system (OSMIA) based on low-dose fluoroscopy and image processing.
Fluoroscopic sequences in coronal and sagittal planes were obtained from 2 calibration models using dry lumbar vertebrae, plus the lumbar spines of 30 asymptomatic volunteers. Calibration model 1 (mobile) was screened upright, in 7 inter-vertebral positions. The volunteers and calibration model 2 (fixed) were screened on a motorised table comprising 2 horizontal sections, one of which moved through 80 degrees. Model 2 was screened during motion 5 times and the L2-S1 levels of the volunteers twice. Images were digitised at 5fps.
Inter-vertebral motion from model 1 was compared to its pre-settings to investigate accuracy. For volunteers and model 2, the first digitised image in each sequence was marked with templates. Vertebrae were tracked throughout the motion using automated frame-to-frame registration. For each frame, vertebral angles were subtracted giving inter-vertebral motion graphs. Volunteer data were acquired twice on the same day and analysed by two blinded observers. The root-mean-square (RMS) differences between paired data were used as the measure of reliability.
RMS difference between reference and computed inter-vertebral angles in model 1 was 0.32 degrees for side-bending and 0.52 degrees for flexion-extension. For model 2, X-ray positioning contributed more to the variance of range measurement than did automated registration. For volunteer image sequences, RMS inter-observer variation in intervertebral motion range in the coronal plane was 1.86 degreesand intra-subject biological variation was between 2.75 degrees and 2.91 degrees. RMS inter-observer variation in the sagittal plane was 1.94 degrees. Radiation dosages in each view were below the levels recommended for a plain film.
OSMIA can measure inter-vertebral angular motion patterns in routine clinical settings if modern image intensifier systems are used. It requires skilful radiography to achieve optimal positioning and dose limitation. Reliability in individual subjects can be judged from the variance of their averaged inter-vertebral angles and by observing automated image registration.
Objective: To provide a foundation of knowledge concerning the functional anatomy, kinematic response, and mechanisms involved in axial-compression cervical spine injury as they relate to sport injury.
Data Sources: We conducted literature searches through the Index Medicus, SPORT Discus, and PubMed databases and the Library of Congress from 1975–2003 using the key phrases cervical spine injury, biomechanics of cervical spine, football spinal injuries, kinematics of the cervical spine, and axial load.
Data Synthesis: Research on normal kinematics and minor and major injury mechanisms to the cervical spine reveals the complex nature of movement in this segment. The movement into a single plane is not the product of equal and summative movement between and among all cervical vertebrae. Instead, individual vertebrae may experience a reversal of motion while traveling through a single plane of movement. Furthermore, vertebral movement in 1 plane often requires contributed movement in 1 or 2 other planes. Injury mechanisms are even more complex. The reaction of the cervical spine to an axial-load impact has been investigated using cadaver specimens and demonstrates a buckling effect. Impact location and head orientation affect the degree and level of resultant injury.
Conclusions/Recommendations: As with any joint of the body, our understanding of the mechanisms of cervical spine injury will ultimately serve to reduce their occurrence and increase the likelihood of recognition and immediate care. However, the cervical spine is unique in its normal kinematics compared with joints of the extremities. Injury biomechanics in the cervical spine are complex, and much can still be learned about mechanisms of the cervical spine injury specific to sports.
catastrophic injury; whiplash; injury mechanisms; spinal cord; axial load
In vivo three-dimensional (3D) kinematics of the lumbar spine has not been well evaluated by the conventional methods because of their methodological limitations, while 3D intervertebral motions have been quantitatively determined by cadaver studies. We thus developed a novel 3D analyzing system for the relative motions of individual vertebrae using 3D magnetic resonance imaging (MRI) and analyzed in vivo 3D intervertebral motions of the lumbar spine during trunk rotation. Ten healthy volunteers underwent 3D MRI of the lumbar spine in nine positions with 15° increments during trunk rotation (0°, 15°, 30°, 45°, and maximum). Relative motions of the lumbar spine were calculated by automatically superimposing a segmented 3D MRI of the vertebra in the neutral position over images of each position using the voxel-based registration method. These 3D motions were represented with 6 degrees of freedom by Euler angles and translations on the coordinate system. The mean axial rotation of ten healthy volunteers of each lumbar spinal segment in 45° trunk rotation to each side ranged from 1.2° to 1.7°. Coupled flexion with axial rotation was observed at the segments from L1/2 to L5/S1. Coupled lateral bending of the segments from L1/2 to L4/5 was in the opposite direction of the trunk rotation, while that of T12/L1 and L5/S1 was in the same direction. The direction of the coupled lateral bending in the present study was different from that in the previous cadaver study only at L4/5. This difference might result from the non-load state of the supine position in the current study and/or the non-physiological state in the cadaver study. Our system has two limitations: (1) the study was conducted with each volunteer in the supine position, and (2) because the rotation device regulated trunk rotation, trunk rotation might not have been physiological. In vivo 3D intervertebral motions of the lumbar spine during trunk rotation were evaluated using our novel motion analysis system. These data may be useful for the optimal orthopaedic management of lumbar spinal disorders.
Electronic supplementary material
The online version of this article (doi:10.1007/s00586-007-0373-3) contains supplementary material, which is available to authorized users.
Kinematics; Coupled motion; Lumbar spine; Volume registration
Accurate measurement of intervertebral kinematics of the cervical spine can support the diagnosis of widespread diseases related to neck pain, such as chronic whiplash dysfunction, arthritis, and segmental degeneration. The natural inaccessibility of the spine, its complex anatomy, and the small range of motion only permit concise measurement in vivo. Low dose X-ray fluoroscopy allows time-continuous screening of cervical spine during patient's spontaneous motion. To obtain accurate motion measurements, each vertebra was tracked by means of image processing along a sequence of radiographic images. To obtain a time-continuous representation of motion and to reduce noise in the experimental data, smoothing spline interpolation was used. Estimation of intervertebral motion for cervical segments was obtained by processing patient's fluoroscopic sequence; intervertebral angle and displacement and the instantaneous centre of rotation were computed. The RMS value of fitting errors resulted in about 0.2 degree for rotation and 0.2 mm for displacements.
Image-guided surgery provides navigational assistance to the surgeon by displaying the surgical probe position on a set of preoperative tomograms in real time. In this study, the feasibility of implementing image-guided surgery concepts into liver surgery was examined during eight hepatic resection procedures. Preoperative tomographic image data were acquired and processed. Accompanying intraoperative data on liver shape and position were obtained through optically tracked probes and laser range scanning technology. The preoperative and intraoperative representations of the liver surface were aligned using the iterative closest point surface matching algorithm. Surface registrations resulted in mean residual errors from 2 to 6 mm, with errors of target surface regions being below a stated goal of 1 cm. Issues affecting registration accuracy include liver motion due to respiration, the quality of the intraoperative surface data, and intraoperative organ deformation. Respiratory motion was quantified during the procedures as cyclical, primarily along the cranial–caudal direction. The resulting registrations were more robust and accurate when using laser range scanning to rapidly acquire thousands of points on the liver surface and when capturing unique geometric regions on the liver surface, such as the inferior edge. Finally, finite element models recovered much of the observed intraoperative deformation, further decreasing errors in the registration. Image-guided liver surgery has shown the potential to provide surgeons with important navigation aids that could increase the accuracy of targeting lesions and the number of patients eligible for surgical resection.
Image-guided surgery; Liver resection; Surface registration; Laser range scanning; Finite element
Skin toxicity is the most common side effect of breast cancer radiotherapy and impairs the quality of life of many breast cancer survivors. We, along with other researchers, have recently found quantitative ultrasound to be effective as a skin toxicity assessment tool. Although more reliable than standard clinical evaluations (visual observation and palpation), the current procedure for ultrasound-based skin toxicity measurements requires manual delineation of the skin layers (i.e., epidermis-dermis and dermis-hypodermis interfaces) on each ultrasound B-mode image. Manual skin segmentation is time consuming and subjective. Moreover, radiation-induced skin injury may decrease image contrast between the dermis and hypodermis, which increases the difficulty of delineation. Therefore, we have developed an automatic skin segmentation tool (ASST) based on the active contour model with two significant modifications: (i) The proposed algorithm introduces a novel dual-curve scheme for the double skin layer extraction, as opposed to the original single active contour method. (ii) The proposed algorithm is based on a geometric contour framework as opposed to the previous parametric algorithm. This ASST algorithm was tested on a breast cancer image database of 730 ultrasound breast images (73 ultrasound studies of 23 patients). We compared skin segmentation results obtained with the ASST with manual contours performed by two physicians. The average percentage differences in skin thickness between the ASST measurement and that of each physician were less than 5% (4.8 ± 17.8% and −3.8 ± 21.1%, respectively). In summary, we have developed an automatic skin segmentation method that ensures objective assessment of radiation-induced changes in skin thickness. Our ultrasound technology offers a unique opportunity to quantify tissue injury in a more meaningful and reproducible manner than the subjective assessments currently employed in the clinic.
Skin segmentation; Radiation toxicity; Breast cancer radiotherapy; Breast ultrasound
This work aims to develop a methodology for automated atlas-guided analysis of small animal positron emission tomography (PET) data through deformable registration to an anatomical mouse model.
A non-rigid registration technique is used to put into correspondence relevant anatomical regions of rodent CT images from combined PET/CT studies to corresponding CT images of the Digimouse anatomical mouse model. The latter provides a pre-segmented atlas consisting of 21 anatomical regions suitable for automated quantitative analysis. Image registration is performed using a package based on the Insight Toolkit allowing the implementation of various image registration algorithms. The optimal parameters obtained for deformable registration were applied to simulated and experimental mouse PET/CT studies. The accuracy of the image registration procedure was assessed by segmenting mouse CT images into seven regions: brain, lungs, heart, kidneys, bladder, skeleton and the rest of the body. This was accomplished prior to image registration using a semi-automated algorithm. Each mouse segmentation was transformed using the parameters obtained during CT to CT image registration. The resulting segmentation was compared with the original Digimouse atlas to quantify image registration accuracy using established metrics such as the Dice coefficient and Hausdorff distance. PET images were then transformed using the same technique and automated quantitative analysis of tracer uptake performed.
The Dice coefficient and Hausdorff distance show fair to excellent agreement and a mean registration mismatch distance of about 6 mm. The results demonstrate good quantification accuracy in most of the regions, especially the brain, but not in the bladder, as expected. Normalized mean activity estimates were preserved between the reference and automated quantification techniques with relative errors below 10 % in most of the organs considered.
The proposed automated quantification technique is reliable, robust and suitable for fast quantification of preclinical PET data in large serial studies.
PET/CT; Small animals; Quantification; Deformable registration; Atlas
Left ventricular (LV) segmentation, including accurate assignment of LV contours, is essential for the quantitative assessment of myocardial perfusion SPECT (MPS). Two major types of segmentation failures are observed in clinical practices: incorrect LV shape determination and incorrect valve-plane (VP) positioning. We have developed a technique to automatically detect these failures for both nongated and gated studies.
A standard Cedars-Sinai perfusion SPECT (quantitative perfusion SPECT [QPS]) algorithm was applied to derive LV contours in 318 consecutive 99mTc-sestamibi rest/stress MPS studies consisting of stress/rest scans with or without attenuation correction and gated stress/rest images (1,903 scans total). Two numeric parameters, shape quality control (SQC) and valve-plane quality control, were derived to categorize the respective contour segmentation failures. The results were compared with the visual classification of automatic contour adequacy by 3 experienced observers.
The overall success of automatic LV segmentation in the 1,903 scans ranged from 66% on nongated images (incorrect shape, 8%; incorrect VP, 26%) to 87% on gated images (incorrect shape, 3%; incorrect VP, 10%). The overall interobserver agreement for visual classification of automatic LV segmentation was 61% for nongated scans and 80% for gated images; the agreement between gray-scale and color-scale display for these scans was 86% and 91%, respectively. To improve the reliability of visual evaluation as a reference, the cases with intra- and interobserver discrepancies were excluded, and the remaining 1,277 datasets were considered (101 with incorrect LV shape and 102 with incorrect VP position). For the SQC, the receiver-operating-characteristic area under the curve (ROC-AUC) was 1.0 ± 0.00 for the overall dataset, with an optimal sensitivity of 100% and a specificity of 98%. The ROC-AUC was 1.0 in all specific datasets. The algorithm was also able to detect the VP position errors: VP overshooting with ROC-AUC, 0.91 ± 0.01; sensitivity, 100%; and specificity, 70%; and VP undershooting with ROC-AUC, 0.96 ± 0.01; sensitivity, 100%; and specificity, 70%.
A new automated method for quality control of LV MPS contours has been developed and shows high accuracy for the detection of failures in LV segmentation with a variety of acquisition protocols. This technique may lead to an improvement in the objective, automated quantitative analysis of MPS.
single photon emission computed tomography; quality control; myocardial perfusion imaging; left ventricle segmentation