Frédéric Lavoie

Conservative Femoral Stem Revision: Avoiding Therapeutic Escalation

A conservative approach to femoral revision is assessed. We report on 41 femoral revisions using an extensively coated hydroxyapatite primary femoral stem. Clinical, operative, and radiological data were gathered. Harris hip scores increased from 65/100 to 90/100 at the minimal follow-up of 1 year (P < .05). All stems showed signs of osseous integration. No significant migration was measured. No patient had to be reoperated because of problems related to the stem. Good results are reported for femoral revision with Paprosky type I and II bone defects with no significant difference between the 2 subgroups, hereby proving that conservative femoral revision is a reasonable treatment alternative. Reproducible results with such a technique may bring surgeons to be more aggressive when noticing early signs of femoral loosening.

Tibio-femoral joint contact in healthy and osteoarthritic knees during quasi-static squat: A bi-planar X-ray analysis

The aim of this study was to quantify the tibio-femoral contact point (CP) locations in healthy and osteoarthritic (OA) subjects during a weight-bearing squat using stand-alone biplanar X-ray images. Ten healthy and 9 severe OA subjects performed quasi-static squats. Bi-planar X-ray images were recorded at 0°, 15°, 30°, 45°, and 70° of knee flexion. A reconstruction/registration process was used to create 3D models of tibia, fibula, and femur from bi-planar X-rays and to measure their positions at each posture. A weighted centroid of proximity algorithm was used to calculate the tibio-femoral CP locations. The accuracy of the reconstruction/registration process in measuring the quasi-static kinematics and the contact parameters was evaluated in a validation study. The quasi-static kinematics data revealed that in OA knees, adduction angles were greater (p<0.01), and the femur was located more medially relative to the tibia (p<0.01). Similarly, the average CP locations on the medial and lateral tibial plateaus of the OA patients were shifted (6.5±0.7mm; p<0.01) and (9.6±3.1mm; p<0.01) medially compared to the healthy group. From 0° to 70° flexion, CPs moved 8.1±5.3mm and 8.9±5.3mm posteriorly on the medial and lateral plateaus of healthy knees; while in OA joints CPs moved 10.1±8.4mm and 3.6±2.8mm posteriorly. The average minimum tibio-femoral bone-to-bone distances of the OA joints were lower in both compartments (p<0.01). The CPs in the OA joints were located more medially and displayed a higher ratio of medial to lateral posterior translations compared to healthy joints.

A method to study 3D knee pseudo-kinematics using low-dose stereoradiography during static squat.

To investigate clinical questions as kinematic or postural differences between healthy and pathological populations, the current trend is to couple imagery to movement or to posture. To do so, researchers generally register a threedimensional (3D) model acquired from either CT scan or magnetic resonance imaging on 2D fluoroscopic images (registration on dynamic activities) or several radiographs (registration on posture or pseudo-kinematics) (Moro-oka et al. 2007). The literature suggests the use of so-called ‘extrinsic 2D/3D registration’. This implies the need of geometrical objects in the radiographic scene. Embedded tantalum beads or prostheses (Scarvell et al. 2010; Sharma et al. 2012),well detectable on the radiographs, are generally used. They allow attaining the expected measurement accuracy ,18 and ,1mm. When no tantalum beads or prostheses are present, the registration process lacks reliability and accuracy because of the difficulty to extract precise information from the radiographic images (blurry contours, superimposition of structures and cylindrical nature of long bones) (Belvedere et al. 2013). The goal of this work was to present an intrinsic 2D/3D registration method and to validate its reliability on patients’ images aswell as its accuracy on simulated radiographs in a pseudo-kinematic context.

Gesture as an important factor in 3D kinematic assessment of the knee

Contradictions exist between studies of the 3D kinematics of the knee. We hypothesize that they are in part due to differences in the gesture performed by the subjects during kinematic assessment. The purpose of this study is to evaluate the impact of gesture variations on knee kinematics. Seventeen healthy male subjects performed 20-s series of knee-bends in a knee-bend standardizing structure. All series differed regarding either foot rotation, knee excursion, or hip rotation. 3D knee kinematics were recorded using optical position sensors mounted on a skin-motion-reducing harness. Kinematic comparisons were made between a gesture of reference (the standard gesture) and every other gesture. Analyses were performed on average differences. Differences of up to 15° of tibial rotation were found for gestures involving different foot rotation. Gestures involving different knee excursion brought on differences of more than 4° of tibial rotation while hip rotation induced more than 5° of tibial rotation. It is hereby demonstrated that gesture differences can have a dramatic impact on measured knee kinematics. Hence gesture performance needs to be carefully monitored during 3D kinematic assessment of the weight-bearing human knee.

Superpixel and Entropy-Based Multi-atlas Fusion Framework for the Segmentation of X-ray Images

X-ray images segmentation can be useful to aid in accurate diagnosis or faithful 3D bone reconstruction but remains a challenging and complex task, particularly when dealing with large and complex anatomical structures such as the human pelvic bone. In this paper, we propose a multi-atlas fusion framework to automatically segment the human pelvic structure from 45 or 135-degree oblique X-ray radiographic images. Unlike most atlas-based approach, this method combines a data set of a priori segmented X-ray images of the human pelvis (or multi-atlas) to generate an adaptive superpixel map in order to take efficiently into account both the imaging pose variability along with the inter-patient (bone) shape non-linear variability. In addition, we propose a new label propagation or fusion step based on the variation of information criterion for integrating the multi-atlas information into the final consensus segmentation. We thoroughly evaluated the method on 30 manually segmented 45 or 135 degree oblique X-ray radiographic images data set by performing a leave-one-out study. Compared to the manual gold standard segmentations, the accuracy of our automatic segmentation approach is \(85\%\) which remains in the error range of manual segmentations due to the inter intra/observer variability.

Postoperative Stiffening after Bicruciate-Retaining Total Knee Arthroplasty

Abstract Bicruciate‐retaining (BCR) total knee arthroplasty (TKA) has recently experienced a resurgence of popularity. It may be a good option among a younger, more active population because it restores knee kinematics better than other prosthetic designs. Results obtained in the first 100 BCR TKAs implanted with a simplified gap‐balancing technique are reported, with special attention paid to knee flexion, through comparison with a cohort of 100 posterior‐stabilized (PS) TKAs. We conducted a retrospective comparative cohort study of 100 BCR TKAs (90 patients) and 100 PS TKAs (88 patients). Knees with a BCR TKA lost significantly more flexion PS TKA in the early postoperative period when their preoperative flexion was less than 130 degrees (loss of 40 degrees vs. loss of 24 degrees). Postoperative range of motion was similar between BCR TKA and PS TKA when preoperative knee flexion was 130 degrees or more, and when there was no preoperative flexion contracture. Postoperative stiffening seems to be more frequent and of greater magnitude after BCR TKA, compared with PS TKA, in patients suffering from preoperative flexion stiffness. Further investigation on the causes of this phenomenon is warranted.

An homomorphic filtering and expectation maximization approach for the point spread function estimation in ultrasound imaging

In modern ultrasound imaging systems, the spatial resolution is severely limited due to the effects of both the finite aperture and overall bandwidth of ultrasound transducers and the non-negligible width of the transmitted ultrasound beams. This low spatial resolution remains the major limiting factor in the clinical usefulness of medical ultrasound images. In order to recover clinically important image details, which are often masked due to this resolution limitation, an image restoration procedure should be applied. To this end, an estimation of the Point Spread Function (PSF) of the ultrasound imaging system is required. This paper introduces a novel, original, reliable, and fast Maximum Likelihood (ML) approach for recovering the PSF of an ultrasound imaging system. This new PSF estimation method assumes as a constraint that the PSF is of known parametric form. Under this constraint, the parameter values of its associated Modulation Transfer Function (MTF) are then efficiently estimated using a homomorphic filter, a denoising step, and an expectation-maximization (EM) based clustering algorithm. Given this PSF estimate, a deconvolution can then be efficiently used in order to improve the spatial resolution of an ultrasound image and to obtain an estimate (independent of the properties of the imaging system) of the true tissue reflectivity function. The experiments reported in this paper demonstrate the efficiency and illustrate all the potential of this new estimation and blind deconvolution approach.

Superpixel and multi-atlas based fusion entropic model for the segmentation of X-ray images

HighlightsUsing oblique X‐ray radiographic images for pelvis, talus and patella segmentation.Using a training dataset of co‐registered/pre‐segmented X‐ray images.Estimate a superpixel map from this training dataset.Using a novel label propagation step based on the entropy concept. Graphical abstract Figure. No caption available. ABSTRACT X‐ray image segmentation is an important and crucial step for three‐dimensional (3D) bone reconstruction whose final goal remains to increase effectiveness of computer‐aided diagnosis, surgery and treatment plannings. However, this segmentation task is rather challenging, particularly when dealing with complicated human structures in the lower limb such as the patella, talus and pelvis. In this work, we present a multi‐atlas fusion framework for the automatic segmentation of these complex bone regions from a single X‐ray view. The first originality of the proposed approach lies in the use of a (training) dataset of co‐registered/pre‐segmented X‐ray images of these aforementioned bone regions (or multi‐atlas) to estimate a collection of superpixels allowing us to take into account all the nonlinear and local variability of bone regions existing in the training dataset and also to simplify the superpixel map pruning process related to our strategy. The second originality is to introduce a novel label propagation step based on the entropy concept for refining the resulting segmentation map into the most likely internal regions to the final consensus segmentation. In this framework, a leave‐one‐out cross‐validation process was performed on 31 manually segmented radiographic image dataset for each bone structure in order to rigorously evaluate the efficiency of the proposed method. The proposed method resulted in more accurate segmentations compared to the probabilistic patch‐based label fusion model (PB) and the classical patch‐based majority voting fusion scheme (MV) using different registration strategies. Comparison with manual (gold standard) segmentations revealed that the good classification accuracy of our unsupervised segmentation scheme is, respectively, 93.79% for the patella, 88.3% for the talus and 85.02% for the pelvis; a score that falls within the range of accuracy levels of manual segmentations (due to the intra inter/observer variability).

Spacer-Based Gap Balancing in Total Knee Arthroplasty: Clinical Success with a Reproducible Technique

&NA; Proper coronal and transverse ligament balancing is an important aspect of total knee arthroplasty (TKA) and has an impact on postoperative outcome. Many variations of the gap balancing technique were described to address this challenge, most of them using various tensioning devices, but none for which the use is widespread. The aim of this paper is to describe a gap technique variant for TKA using spacer blocks and report the clinical results for a cohort of patients on which it was used. A total of 114 TKAs were performed in 101 patients using a standardized surgical technique that integrates ligament balancing with sizing and positioning of the femoral component. Clinical variables were assessed preoperatively and after a mean follow‐up of 43 months using the Knee Society score, the Knee injury and Osteoarthritis Outcome Score (KOOS), and the International Knee Documentation Committee (IKDC) score. A significant improvement in every clinical function score was noted at the latest follow‐up compared with preoperative values. All the knees except four (96%) were well‐balanced at the last follow‐up examination; a firm but delayed end‐point was noted in the remaining four knees but did not seem to affect patient outcome. The patellar button was centered in the prosthetic trochlear groove for all knees on the postoperative radiographs. Similar improvements in the scores and in range of motion were noted for knees with a preoperative coronal misalignment of 10 degrees or more (n = 26) compared with knees with less than 10 degrees of varus or valgus (n = 77). The described surgical technique appears to be reliable to obtain well‐balanced knees and good patellar tracking when performing a primary TKA, even in knees with important coronal misalignment.

Unsupervised Segmentation of Ultrasound Images by Fusion of Spatio-Frequential Textural Features

Image segmentation plays an important role in both qualitative and quantitative analysis of medical ultrasound images. However, due to their poor resolution and strong speckle noise, segmenting objects from this imaging modality remains a challenging task and may not be satisfactory with traditional image segmentation methods. To this end, this paper presents a simple, reliable, and conceptually different segmentation technique to locate and extract bone contours from ultrasound images. Instead of considering a new elaborate (texture) segmentation model specifically adapted for the ultrasound images, our technique proposes to fuse (i.e. efficiently combine) several segmentation maps associated with simpler segmentation models in order to get a final reliable and accurate segmentation result. More precisely, our segmentation model aims at fusing several K-means clustering results, each one exploiting, as simple cues, a set of complementary textural features, either spatial or frequential. Eligible models include the gray-level co-occurrence matrix, the re-quantized histogram, the Gabor filter bank, and local DCT coefficients. The experiments reported in this paper demonstrate the efficiency and illustrate all the potential of this segmentation approach.