A technique for regional analysis of femorotibial cartilage thickness based on quantitative magnetic resonance imaging

The objective of this work was to develop a methodology for measuring cartilage thickness in anatomically based subregions in the tibial and in the central weight-bearing femoral cartilage from magnetic resonance (MR) images. The tibial plateau was divided into a central area of the total subchondral bone area (tAB), and anterior, posterior, internal, and external subregions surrounding it. In the weight-bearing femoral condyles, central, internal, and external subregions were determined. The Euclidean distance between the tAB and cartilage surface was used for determining cartilage thickness. The reproducibility of the method was evaluated on test-retest data sets of 12 participants (six healthy, six with osteoarthritis). The subregion size was varied systematically to study the influence on the reproducibility. The size of the subregions was highly consistent under conditions of repositioning (standard deviation 0.0%-0.3%). The precision errors for regional mean cartilage thickness measurements ranged from 19 mum (1.5%) to 84 mum (4.7%). The computation of regional cartilage thickness values from segmented MR images is shown to be highly reproducible and robust under conditions of joint repositioning. In longitudinal studies, this technique may substantially enhance the ability of quantitative MRI to monitor structural changes in osteoarthritis at narrow time intervals.     

Computer-aided method for quantification of cartilage thickness and volume changes using MRI: validation study using a synthetic model

The primary objective of this study was to develop a computer-aided method for the quantification of three-dimensional (3-D) cartilage changes over time in knees with osteoarthritis (OA). We introduced a local coordinate system (LCS) for the femoral and tibial cartilage boundaries that provides a standardized representation of cartilage geometry, thickness, and volume. The LCS can be registered in different data sets from the same patient so that results can be directly compared. Cartilage boundaries are segmented from 3-D magnetic resonance (MR) slices with a semi-automated method and transformed into offset-maps, defined by the LCS. Volumes and thickness are computed from these offset-maps. Further anatomical labeling allows focal volumes to be evaluated in predefined subregions. The accuracy of the automated behavior of the method was assessed, without any human intervention, using realistic, synthetic 3-D MR images of a human knee. The error in thickness evaluation is lower than 0.12 mm for the tibia and femur. Cartilage volumes in anatomical subregions show a coefficient of variation ranging from 0.11% to 0.32%. This method improves noninvasive 3-D analysis of cartilage thickness and volume and is well suited for in vivo follow-up clinical studies of OA knees.     

Automatic quantification of tibio-femoral contact area and congruity

We present methods to quantify the medial tibio- femoral (MTF) joint contact area (CA) and congruity index (CI) from low-field magnetic resonance imaging (MRI). Firstly, based on the segmented MTF cartilage compartments, we computed the contact area using the Euclidian distance transformation. The CA was defined as the area of the tibial superior surface and the femoral inferior surface that are less than a voxel width apart. Furthermore, the CI is computed point-by-point by assessing the first- and second-order general surface features over the contact area. Mathematically, it is the inverse distance between the local normal vectors (first-order features) scaled by the local normal curvatures (second-order features) along the local direction of principal knee motion in a local reference coordinate system formed by the directions of principal curvature and the surface normal vector. The abilities of the CA and the CI for diagnosing osteoarthritis (OA) at different levels (disease severity was assessed using the Kellgren and Lawrence Index, KL) were cross-validated on 288 knees at baseline. Longitudinal analysis was performed on 245 knees. The precision quantified on 31 scan-rescan pairs (RMS CV) for CA was 13.7% and for CI 7.5%. The CA increased with onset of the disease and then decreased with OA progression. The CI was highest in healthy and decreased with the onset of OA and further with disease progression. The CI showed an AUC of 0.69 (p < 0.0001) for separating KL = 0 and KL > 0. For separating KL < 1 or KL = 1 and KL > 1 knees, the AUC for CI was 0.73 (p < 0.0001). The CA demonstrated longitudinal responsiveness (SRM) at all stages of OA, whereas the CI did for advanced OA only. Eventually, the quantified CA and the CI might be suitable to help explaining OA onset, diagnosis of (early) OA, and measuring the efficacy of DMOADs in clinical trials.     

Monitoring osteoarthritis in the rat model using optical coherence tomography

OBJECTIVE: A need exists for an animal model to assess therapeutics for osteoarthritis (OA) without sacrificing the animal. Our goal is to assess the progression of experimentally induced osteoarthritis in the rat knee joint by monitoring articular cartilage thickness, surface abnormalities, and collagen organization using a new technology known as optical coherence tomography (OCT). DESIGN: OA was generated in Wistar Hanover rats via injection of sodium iodoacetate into the left articular joint of the knee while normal saline was injected as a control in the contralateral right knee. Rats were sacrificed at 1-, 2-, 3-, 4-, and 8-week intervals and the knee joints were subsequently harvested and imaged using normal and polarization sensitive OCT (PS-OCT). Treated knees were compared to normal counterparts in the contralateral leg. Following imaging, knees underwent both routine histological processing and picrosirus staining for organized collagen. RESULTS: OCT images indicate that injection of sodium iodoacetate resulted in a progressive decrease in cartilage thickness and loss of the bone-cartilage interface which correlated with histology. In addition, PS-OCT was able to detect collagen disorganization, an early indicator of OA. CONCLUSIONS: The use of OCT in combination with the induction of OA in rats is a promising new animal model for assessing articular changes with the goal of monitoring therapeutics longitudinally. Future work will extend the model to in vivo assessments.     

A new accurate and precise 3-D segmentation method for skeletal structures in volumetric CT data

We developed a highly automated three-dimensionally based method for the segmentation of bone in volumetric computed tomography (CT) datasets. The multistep approach starts with three-dimensional (3-D) region-growing using local adaptive thresholds followed by procedures to correct for remaining boundary discontinuities and a subsequent anatomically oriented boundary adjustment using local values of cortical bone density. We describe the details of our approach and show applications in the proximal femur, the knee, and the skull. The accuracy of the determination of geometrical parameters was analyzed using CT scans of the semi-anthropomorphic European spine phantom. Depending on the settings of the segmentation parameters cortical thickness could be determined with an accuracy corresponding to the side length of 1 to 2.5 voxels. The impact of noise on the segmentation was investigated by artificially adding noise to the CT data. An increase in noise by factors of two and five changed cortical thickness corresponding to the side length of one voxel. Intraoperator and interoperator precision was analyzed by repeated analysis of nine pelvic CT scans. Precision errors were smaller than 1% for trabecular and total volumes and smaller than 2% for cortical thickness. Intraoperator and interoperator precision errors were not significantly different. Our segmentation approach shows: 1) high accuracy and precision and is 2) robust to noise, 3) insensitive to user-defined thresholds, 4) highly automated and fast, and 5) easy to initialize.     

Segmenting articular cartilage automatically using a voxel classification approach

We present a fully automatic method for articular cartilage segmentation from magnetic resonance imaging (MRI) which we use as the foundation of a quantitative cartilage assessment. We evaluate our method by comparisons to manual segmentations by a radiologist and by examining the interscan reproducibility of the volume and area estimates. Training and evaluation of the method is performed on a data set consisting of 139 scans of knees with a status ranging from healthy to severely osteoarthritic. This is, to our knowledge, the only fully automatic cartilage segmentation method that has good agreement with manual segmentations, an interscan reproducibility as good as that of a human expert, and enables the separation between healthy and osteoarthritic populations. While high-field scanners offer high-quality imaging from which the articular cartilage have been evaluated extensively using manual and automated image analysis techniques, low-field scanners on the other hand produce lower quality images but to a fraction of the cost of their high-field counterpart. For low-field MRI, there is no well-established accuracy validation for quantitative cartilage estimates, but we show that differences between healthy and osteoarthritic populations are statistically significant using our cartilage volume and surface area estimates, which suggests that low-field MRI analysis can become a useful, affordable tool in clinical studies.     

An Integrated Segmentation and Analysis Approach for QCT of the Knee to Determine Subchondral Bone Mineral Density and Texture

We have developed a new integrated approach for quantitative computed tomography of the knee in order to quantify bone mineral density (BMD) and subchondral bone structure. The present framework consists of image acquisition and reconstruction, 3-D segmentation, determination of anatomic coordinate systems, and reproducible positioning of analysis volumes of interest (VOI). Novel segmentation algorithms were developed to identify growth plates of the tibia and femur and the joint space with high reproducibility. Five different VOIs with varying distance to the articular surface are defined in the epiphysis. Each VOI is further subdivided into a medial and a lateral part. In each VOI, BMD is determined. In addition, a texture analysis is performed on a high-resolution computed tomography (CT) reconstruction of the same CT scan in order to quantify subchondral bone structure. Local and global homogeneity, as well as local and global anisotropy were measured in all VOIs. Overall short-term precision of the technique was evaluated using double measurements of 20 osteoarthritic cadaveric human knees. Precision errors for volume were about 2-3% in the femur and 3-5% in the tibia. Precision errors for BMD were about 1-2% lower. Homogeneity parameters showed precision errors up to about 2% and anisotropy parameters up to about 4%.     

Completely automated multiresolution edge snapper--a new technique for an accurate carotid ultrasound IMT measurement: clinical validation and benchmarking on a multi-institutional database

The aim of this paper is to describe a novel and completely automated technique for carotid artery (CA) recognition, far (distal) wall segmentation, and intima-media thickness (IMT) measurement, which is a strong clinical tool for risk assessment for cardiovascular diseases. The architecture of completely automated multiresolution edge snapper (CAMES) consists of the following two stages: 1) automated CA recognition based on a combination of scale-space and statistical classification in a multiresolution framework and 2) automated segmentation of lumen-intima (LI) and media-adventitia (MA) interfaces for the far (distal) wall and IMT measurement. Our database of 365 B-mode longitudinal carotid images is taken from four different institutions covering different ethnic backgrounds. The ground-truth (GT) database was the average manual segmentation from three clinical experts. The mean distance ± standard deviation of CAMES with respect to GT profiles for LI and MA interfaces were 0.081 ± 0.099 and 0.082 ± 0.197 mm, respectively. The IMT measurement error between CAMES and GT was 0.078 ± 0.112 mm. CAMES was benchmarked against a previously developed automated technique based on an integrated approach using feature-based extraction and classifier (CALEX). Although CAMES underestimated the IMT value, it had shown a strong improvement in segmentation errors against CALEX for LI and MA interfaces by 8% and 42%, respectively. The overall IMT measurement bias for CAMES improved by 36% against CALEX. Finally, this paper demonstrated that the figure-of-merit of CAMES was 95.8% compared with 87.4% for CALEX. The combination of multiresolution CA recognition and far-wall segmentation led to an automated, low-complexity, real-time, and accurate technique for carotid IMT measurement. Validation on a multiethnic/multi-institutional data set demonstrated the robustness of the technique, which can constitute a clinically valid IMT measurement for assistance in atherosclerosis disease management.     

Amelioration of Osteoarthritis via Tetrahedral Framework Nucleic Acids Delivering Microrna-124 for Cartilage Regeneration

MicroRNAs (miRNAs) regulate several physiological and pathological processes involved in various diseases, including osteoarthritis (OA). OA is the most common global musculoskeletal disorder, characterized by the irreversible progressive destruction of articular cartilage. Supplementation with exogenous miRNAs may represent a promising therapeutic OA treatment, with miRNA-124 (miR-124) being a prime candidate for its anti-inflammatory ability; however, an effective drug delivery system is urgently required to enhance miR-124 stability and capacity to enter chondrocytes. To this end, tetrahedral framework nucleic acids’ (tFNAs) self-assembled 3D DNA nanostructures possess superior inherent biocompatibility, versatile functionality, unsurpassed editability, and strong cellular internalization ability. In this study, tFNAs carrying one or three miR-124 (T-miR1 or T-miR3) are successfully synthesized. T-miR3 is largely absorbed via induced inflammatory chondrocytes by IL-1β. With reactive oxygen species’ scavenging ability and inflammation-suppressive miR-124 release behavior, T-miR3 efficiently protects chondrocytes against IL-1β injury in vitro. Additionally, T-miR3 effectively prevents OA progression by inhibiting chondrocyte apoptosis, smoothing cartilage surfaces, suppressing extracellular matrix degradation, and increasing synovial thickness, effectively protecting in vivo articular cartilage, and illustrating the therapeutic ability of T-miR3 in OA treatment. This study provides experimental evidence and novel therapeutic strategies for OA treatment in the clinical setting.     

Web-Based Multilayer Viewing Interface for Knee Cartilage

Many adults suffer from osteoarthritis (OA) with the majority of people over 65 showing radiographic evidence of the disease. To carry out effective diagnosis and treatment, it is necessary to understand the progression of cartilage loss and study the effectiveness of therapeutic interventions. Hence, it is important to have accurate, fast diagnosis of the disease. In this paper, we describe a Web-based user interface that enables the direct viewing of 2-D and 3-D image data from the visceral and tissue levels of the biological continuum (i.e., the continuum comprising systems, viscera, tissue, cells, proteins, and genes)–-while preserving geometric integrity. This is achieved despite the fact that the data are from different modalities (i.e., magnetic resonance (MR) and light microscopy). The user interface was tested using image data acquired from a study of articular cartilage thickness in the porcine knee. The interface allows the clinician to view both MR and light microscopy images in an integrated manner—with the information linked geometrically.      

Fabrication and characterization of nonplanar microelectrode array circuits for use in arthroscopic diagnosis of cartilage diseases

A process to fabricate nonplanar microelectrode array circuits was developed and the microelectrodes were characterized. These platinum microelectrode arrays are for recording streaming potential signals generated during indentation of articular cartilage. The nonplanar substrate was produced by permanent deformation of a 7-in-diameter circular stainless-steel wafer to form 32 semi-spherical caps (radius of curvature = 4.65 mm and height = 250 microm) at the periphery. The wafer was covered with a 2.5-microm-thick layer of insulating polyimide. Standard microelectronic processes were applied to produce 32 circuits (60 mm long x 4 mm wide) with 37 exposed circular microelectrodes (diameter = 100 microm) centered over each semi-spherical cap. A 2.5-microm-thick photodefinable polyimide layer encapsulated the conducting lines. Capacitances between one microelectrode and either another microelectrode or the metallic substrate were 14.6 +/- 2.0 and 34.4 +/- 3.3 pF, respectively, at 100 Hz. The impedance of the microelectrodes in a 0.15 M saline bath (PBS) was 0.25 +/- 0.08 Mohms while the crosstalk (Vinduced/Vapplied) between two microelectrodes was 0.20 +/- 0.11%, at 100 Hz. Indentation measurements were performed on articular cartilage in vitro showing,streaming potentials that indicate electrode-tissue contact times and generation of streaming potentials.     

基于自组织神经网络的多模态MRI图像分割

磁共振成像(MRI)是一项重要的医学成像技术,在人体组织器官的诊断治疗方面被广泛应用。在脑肿瘤的临床诊断应用中,如何实现脑肿瘤图像的有效自动分割是一个研究的难点和重点。利用多个自组织神经网络(SOM)构造一个并行自组织神经网络(CSOM),将肿瘤图像的分割问题转化为并行自组织神经网络的分类问题。实验表明,并行自组织神经网络的应用,有效提高了分割精确度,有利于自动分割的实现。     

Multifeature landmark-free active appearance models: application to prostate MRI segmentation

Active shape models (ASMs) and active appearance models (AAMs) are popular approaches for medical image segmentation that use shape information to drive the segmentation process. Both approaches rely on image derived landmarks (specified either manually or automatically) to define the object's shape, which require accurate triangulation and alignment. An alternative approach to modeling shape is the levelset representation, defined as a set of signed distances to the object's surface. In addition, using multiple image derived attributes (IDAs) such as gradient information has previously shown to offer improved segmentation results when applied to ASMs, yet little work has been done exploring IDAs in the context of AAMs. In this work, we present a novel AAM methodology that utilizes the levelset implementation to overcome the issues relating to specifying landmarks, and locates the object of interest in a new image using a registration based scheme. Additionally, the framework allows for incorporation of multiple IDAs. Our multifeature landmark-free AAM (MFLAAM) utilizes an efficient, intuitive, and accurate algorithm for identifying those IDAs that will offer the most accurate segmentations. In this paper, we evaluate our MFLAAM scheme for the problem of prostate segmentation from T2-w MRI volumes. On a cohort of 108 studies, the levelset MFLAAM yielded a mean Dice accuracy of 88% ± 5%, and a mean surface error of 1.5 mm ±.8 mm with a segmentation time of 150/s per volume. In comparison, a state of the art AAM yielded mean Dice and surface error values of 86% ± 9% and 1.6 mm ± 1.0 mm, respectively. The differences with respect to our levelset-based MFLAAM model are statistically significant . In addition, our results were in most cases superior to several recent state of the art prostate MRI segmentation methods.     

Surface extraction and thickness measurement of the articular cartilage from MR images using directional gradient vector flow snakes

The accuracy of the surface extraction of magnetic resonance images of highly congruent joints with thin articular cartilage layers has a significant effect on the percentage errors and reproducibility of quantitative measurements (e.g., thickness and volume) of the articular cartilage. Traditional techniques such as gradient-based edge detection are not suitable for the extraction of these surfaces. This paper studies the extraction of articular cartilage surfaces using snakes, and a gradient vector flow (GVF)-based external force is proposed for this application. In order to make the GVF snake more stable and converge to the correct surfaces, directional gradient is used to produce the gradient vector flow. Experimental results show that the directional GVF snake is more robust than the traditional GVF snake for this application. Based on the newly developed snake model, an articular cartilage surface extraction algorithm is developed. Thickness is computed based on the surfaces extracted using the proposed algorithm. In order to make the thickness measurement more reproducible, a new thickness computation approach, which is called T-norm, is introduced. Experimental results show that the thickness measurement obtained by the new thickness computation approach has better reproducibility than that obtained by the existing thickness computation approaches.     

Statistical shape model-based femur kinematics from biplane fluoroscopy

Studying joint kinematics is of interest to improve prosthesis design and to characterize postoperative motion. State of the art techniques register bones segmented from prior computed tomography or magnetic resonance scans with X-ray fluoroscopic sequences. Elimination of the prior 3D acquisition could potentially lower costs and radiation dose. Therefore, we propose to substitute the segmented bone surface with a statistical shape model based estimate. A dedicated dynamic reconstruction and tracking algorithm was developed estimating the shape based on all frames, and pose per frame. The algorithm minimizes the difference between the projected bone contour and image edges. To increase robustness, we employ a dynamic prior, image features, and prior knowledge about bone edge appearances. This enables tracking and reconstruction from a single initial pose per sequence. We evaluated our method on the distal femur using eight biplane fluoroscopic drop-landing sequences. The proposed dynamic prior and features increased the convergence rate of the reconstruction from 71% to 91%, using a convergence limit of 3 mm. The achieved root mean square point-to-surface accuracy at the converged frames was 1.48 ± 0.41 mm. The resulting tracking precision was 1-1.5 mm, with the largest errors occurring in the rotation around the femoral shaft (about 2.5° precision).     

A Hybrid Geometric–Statistical Deformable Model for Automated 3-D Segmentation in Brain MRI

We present a novel 3-D deformable model-based approach for accurate, robust, and automated tissue segmentation of brain MRI data of single as well as multiple magnetic resonance sequences. The main contribution of this study is that we employ an edge-based geodesic active contour for the segmentation task by integrating both image edge geometry and voxel statistical homogeneity into a novel hybrid geometric-statistical feature to regularize contour convergence and extract complex anatomical structures. We validate the accuracy of the segmentation results on simulated brain MRI scans of both single T1-weighted and multiple T1/T2/PD-weighted sequences. We also demonstrate the robustness of the proposed method when applied to clinical brain MRI scans. When compared to a current state-of-the-art region-based level-set segmentation formulation, our white matter and gray matter segmentation resulted in significantly higher accuracy levels with a mean improvement in Dice similarity indexes of 8.55% (p<0.0001) and 10.18% (p<0.0001), respectively.     

Computer-Assisted Preoperative Planning for Reduction of Proximal Femoral Fracture Using 3-D-CT Data

This paper describes procedures for repositioning calculations of fractured bone fragments using 3-D-computed tomography (CT), aimed at preoperative planning for computer-guided fracture reduction of the proximal femur. Fracture boundaries of the bone fragments, as ldquofracture lines (FLs),rdquo and the mirror-transformed contralateral femur shape extracted from 3-D-CT were used for repositioning of the fragments. We first describe a method for extracting FLs based on 3-D curvature analysis and then formulate repositioning methods based on registration of bone fragments using the following three constraints: 1) contralateral (CL) femur shape; 2) FLs; and 3) both CL femur shape and fracture lines, as ldquoboth constraintsrdquo. We performed experiments using CT datasets from five simulated and four real patients with proximal femoral fracture. We evaluated the rotation error in reposition calculations and the contact ratio between repositioned fragment boundaries, which are crucial for the recovery of proper functional axes and bone adhesion of fragments, respectively. Experimental results showed that good accuracy and stability were attainable when registration using both constraints was performed after registration using the fracture-line constraint. On average, 6.0deg plusmn0.8deg in rotation error and 89% plusmn 3% in contact ratio were obtained without providing precise initial values.     

Automated segmentation of acetabulum and femoral head from 3-d CT images

This paper describes several new methods and software for automatic segmentation of the pelvis and the femur, based on clinically obtained multislice computed tomography (CT) data. The hip joint is composed of the acetabulum, cavity of the pelvic bone, and the femoral head. In vivo CT data sets of 60 actual patients were used in the study. The 120 (60 /spl times/ 2) hip joints in the data sets were divided into four groups according to several key features for segmentation. Conventional techniques for classification of bony tissues were first employed to distinguish the pelvis and the femur from other CT tissue images in the hip joint. Automatic techniques were developed to extract the boundary between the acetabulum and the femoral head. An automatic method was built up to manage the segmentation task according to image intensity of bone tissues, size, center, shape of the femoral heads, and other characters. The processing scheme consisted of the following five steps: 1) preprocessing, including resampling 3-D CT data by a modified Sine interpolation to create isotropic volume and to avoid Gibbs ringing, and smoothing the resulting images by a 3-D Gaussian filter; 2) detecting bone tissues from CT images by conventional techniques including histogram-based thresholding and binary morphological operations; 3) estimating initial boundary of the femoral head and the joint space between the acetabulum and the femoral head by a new approach utilizing the constraints of the greater trochanter and the shapes of the femoral head; 4) enhancing the joint space by a Hessian filter; and 5) refining the rough boundary obtained in step 3) by a moving disk technique and the filtered images obtained in step 4). The above method was implemented in a Microsoft Windows software package and the resulting software is freely available on the Internet. The feasibility of this method was tested on the data sets of 60 clinical cases (5000 CT images).     

Pulmonary Lobe Segmentation in CT Examinations Using Implicit Surface Fitting

Lobe identification in computed tomography (CT) examinations is often an important consideration during the diagnostic process as well as during treatment planning because of their relative independence of each other in terms of anatomy and function. In this paper, we present a new automated scheme for segmenting lung lobes depicted on 3-D CT examinations. The unique characteristic of this scheme is the representation of fissures in the form of implicit functions using Radial Basis Functions (RBFs), capable of seamlessly interpolating “holes” in the detected fissures and smoothly extrapolating the fissure surfaces to the lung boundaries resulting in a “natural” segmentation of lung lobes. A previously developed statistically based approach is used to detect pulmonary fissures and the constraint points for implicit surface fitting are selected from detected fissure surfaces in a greedy manner to improve fitting efficiency. In a preliminary assessment study, lobe segmentation results of 65 chest CT examinations, five of which were reconstructed with three section thicknesses of 0.625 mm, 1.25 mm, and 2.5 mm, were subjectively and independently evaluated by two experienced chest radiologists using a five category rating scale (i.e., excellent, good, fair, poor, and unacceptable). Thirty-three of 65 examinations (50.8%) with a section thickness of 0.625 mm were rated as either “excellent” or “good” by both radiologists and only one case (1.5%) was rated by both radiologists as “poor” or “unacceptable.” Comparable performance was obtained with a slice thickness of 1.25 mm, but substantial performance deterioration occurred in examinations with a section thickness of 2.5 mm. The advantages of this scheme are its full automation, relative insensitivity to fissure completeness, and ease of implementation.