MRI of hip osteoarthritis and implications for surgery

Osteoarthritis of the hip joint is caused by a combination of intrinsic factors and extrinsic factors. Different surgical techniques are being performed to delay or halt osteoarthritis. Success of salvage procedures of the hip depends on the existing cartilage and joint damage before surgery; the likelihood of therapy failure rises with advanced osteoarthritis. For imaging of intra-articular hip pathology, MR imaging represents the best technique because of its ability to directly visualize cartilage, superior soft tissue contrast, and the prospect of multidimensional imaging. This article gives an overview on the standard MR imaging techniques used for diagnosis of hip osteoarthritis and their implications for surgery.

Hip MRI and its implications for surgery in osteoarthritis patients

Osteoarthritis (OA) of the hip joint stems from a combination of intrinsic factors, such as joint anatomy, and extrinsic factors, such as injuries, diseases, and load. Possible risk factors for OA are instability and impingement. Different surgical techniques, such as osteotomies of the pelvis and femur, surgical dislocation, and hip arthroscopy, are being performed to delay or halt OA. Success of salvage procedures of the hip depends on the existing cartilage and joint damage before surgery. The likelihood of therapy failure rises with advanced OA. For imaging of intra-articular hip pathology, MRI represents the best technique because it enables clinicians to directly visualize cartilage, it provides superior soft tissue contrast, and it offers the prospect of multidimensional imaging. However, opinions differ on the diagnostic efficacy of MRI and on the question of which MRI technique is most appropriate. This article gives an overview of the standard MRI techniques for diagnosis of hip OA and their implications for surgery.

MRI of hip osteoarthritis and implications for surgery

Osteoarthritis of the hip joint is caused by a combination of intrinsic factors and extrinsic factors. Different surgical techniques are being performed to delay or halt osteoarthritis. Success of salvage procedures of the hip depends on the existing cartilage and joint damage before surgery; the likelihood of therapy failure rises with advanced osteoarthritis. For imaging of intra-articular hip pathology, MR imaging represents the best technique because of its ability to directly visualize cartilage, superior soft tissue contrast, and the prospect of multidimensional imaging. This article gives an overview on the standard MR imaging techniques used for diagnosis of hip osteoarthritis and their implications for surgery.

Coding Strategies and Implementations of Compressive Sensing

This dissertation studies the coding strategies of computational imaging to overcome the limitation of conventional sensing techniques. The information capacity of conventional sensing is limited by the physical properties of optics, such as aperture size, detector pixels, quantum efficiency, and sampling rate. These parameters determine the spatial, depth, spectral, temporal, and polarization sensitivity of each imager. To increase sensitivity in any dimension can significantly compromise the others.

This research implements various coding strategies subject to optical multidimensional imaging and acoustic sensing in order to extend their sensing abilities. The proposed coding strategies combine hardware modification and signal processing to exploiting bandwidth and sensitivity from conventional sensors. We discuss the hardware architecture, compression strategies, sensing process modeling, and reconstruction algorithm of each sensing system.

Optical multidimensional imaging measures three or more dimensional information of the optical signal. Traditional multidimensional imagers acquire extra dimensional information at the cost of degrading temporal or spatial resolution. Compressive multidimensional imaging multiplexes the transverse spatial, spectral, temporal, and polarization information on a two-dimensional (2D) detector. The corresponding spectral, temporal and polarization coding strategies adapt optics, electronic devices, and designed modulation techniques for multiplex measurement. This computational imaging technique provides multispectral, temporal super-resolution, and polarization imaging abilities with minimal loss in spatial resolution and noise level while maintaining or gaining higher temporal resolution. The experimental results prove that the appropriate coding strategies may improve hundreds times more sensing capacity.

Human auditory system has the astonishing ability in localizing, tracking, and filtering the selected sound sources or information from a noisy environment. Using engineering efforts to accomplish the same task usually requires multiple detectors, advanced computational algorithms, or artificial intelligence systems. Compressive acoustic sensing incorporates acoustic metamaterials in compressive sensing theory to emulate the abilities of sound localization and selective attention. This research investigates and optimizes the sensing capacity and the spatial sensitivity of the acoustic sensor. The well-modeled acoustic sensor allows localizing multiple speakers in both stationary and dynamic auditory scene; and distinguishing mixed conversations from independent sources with high audio recognition rate.

A multidimensional segmentation evaluation for medical image data.

Evaluation of segmentation methods is a crucial aspect in image processing, especially in the medical imaging field, where small differences between segmented regions in the anatomy can be of paramount importance. Usually, segmentation evaluation is based on a measure that depends on the number of segmented voxels inside and outside of some reference regions that are called gold standards. Although some other measures have been also used, in this work we propose a set of new similarity measures, based on different features, such as the location and intensity values of the misclassified voxels, and the connectivity and the boundaries of the segmented data. Using the multidimensional information provided by these measures, we propose a new evaluation method whose results are visualized applying a Principal Component Analysis of the data, obtaining a simplified graphical method to compare different segmentation results. We have carried out an intensive study using several classic segmentation methods applied to a set of MRI simulated data of the brain with several noise and RF inhomogeneity levels, and also to real data, showing that the new measures proposed here and the results that we have obtained from the multidimensional evaluation, improve the robustness of the evaluation and provides better understanding about the difference between segmentation methods.

NMR spectroscopy and imaging of hyperpolarized gases

Since the discovery of the nuclear magnetic resonance (NMR) phenomenon, countless NMR techniques have been developed that are today indispensable tools in physics, chemistry, biology, and medicine. As one of the main obstacles in NMR is its notorious lack of sensitivity, different hyperpolarization (HP) methods have been established to increase signals up to several orders of magnitude. In this work, different aspects of magnetic resonance, using HP noble gases, are studied, hereby combining different disciplines of research. The first part examines new fundamental effects in NMR of HP gases, in theory and experiment. The spin echo phenomenon, which provides the basis of numerous modern experiments, is studied in detail in the gas phase. The changes of the echo signal in terms of amplitude, shape, and position, due to the fast translational motion, are described by an extension of the existing theory and computer simulations. With this knowledge as a prerequisite, the detection of intermolecular double-quantum coherences was accomplished for the first time in the gas phase. The second part of this thesis focuses on the development of a practical method to enhance the dissolution process of HP 129Xe, without loss of polarization or shortening of T1. Two different setups for application in NMR spectroscopy and magnetic resonance imaging (MRI) are presented. The continuous operation allows biological and multidimensional spectroscopy in solutions. Also, first in vitro MRI images with dissolved HP 129Xe as contrast agent were obtained at a clinical scanner.

Multidimensional, multislaces, and multiscale MRI for quality evaluation in food.

InsideFood explicitly aims at measuring food microstructure, the spatial distribution of food components within foods, with state of the art tomographic, spectroscopic and texture measurement techniques including X-ray micro-and nano CT, MRI,OCT, NMR, TRS and SRS, and acoustic emission. Nutritional quality (sugar and gluten free cereal products), sensory quality (texture of all foods) and safety (foreign material detection in cereal products) are considered. Online and inline techniques including NMR, MRI, TRS, SRS and X-ray imaging to visualise and monitor structure will be developed.

Tensor Completion for Multidimensional Inverse Problems with Applications to Magnetic Resonance Relaxometry

This thesis deals with tensor completion for the solution of multidimensional inverse problems. We study the problem of reconstructing an approximately low rank tensor from a small number of noisy linear measurements. New recovery guarantees, numerical algorithms, non-uniform sampling strategies, and parameter selection algorithms are developed. We derive a fixed point continuation algorithm for tensor completion and prove its convergence. A restricted isometry property (RIP) based tensor recovery guarantee is proved. Probabilistic recovery guarantees are obtained for sub-Gaussian measurement operators and for measurements obtained by non-uniform sampling from a Parseval tight frame. We show how tensor completion can be used to solve multidimensional inverse problems arising in NMR relaxometry. Algorithms are developed for regularization parameter selection, including accelerated k-fold cross-validation and generalized cross-validation. These methods are validated on experimental and simulated data. We also derive condition number estimates for nonnegative least squares problems. Tensor recovery promises to significantly accelerate N-dimensional NMR relaxometry and related experiments, enabling previously impractical experiments. Our methods could also be applied to other inverse problems arising in machine learning, image processing, signal processing, computer vision, and other fields.

Study Of The Homogeneity Of Drug Loaded In Polymeric Films Using Near-infrared Chemical Imaging And Split-plot Design.

Split-plot design (SPD) and near-infrared chemical imaging were used to study the homogeneity of the drug paracetamol loaded in films and prepared from mixtures of the biocompatible polymers hydroxypropyl methylcellulose, polyvinylpyrrolidone, and polyethyleneglycol. The study was split into two parts: a partial least-squares (PLS) model was developed for a pixel-to-pixel quantification of the drug loaded into films. Afterwards, a SPD was developed to study the influence of the polymeric composition of films and the two process conditions related to their preparation (percentage of the drug in the formulations and curing temperature) on the homogeneity of the drug dispersed in the polymeric matrix. Chemical images of each formulation of the SPD were obtained by pixel-to-pixel predictions of the drug using the PLS model of the first part, and macropixel analyses were performed for each image to obtain the y-responses (homogeneity parameter). The design was modeled using PLS regression, allowing only the most relevant factors to remain in the final model. The interpretation of the SPD was enhanced by utilizing the orthogonal PLS algorithm, where the y-orthogonal variations in the design were separated from the y-correlated variation.

Feasibility And Reproducibility Of Diffusion-weighted Magnetic Resonance Imaging Of The Fetal Brain In Twin-twin Transfusion Syndrome.

The aim of this study is to test the feasibility and reproducibility of diffusion-weighted magnetic resonance imaging (DW-MRI) evaluations of the fetal brains in cases of twin-twin transfusion syndrome (TTTS). From May 2011 to June 2012, 24 patients with severe TTTS underwent MRI scans for evaluation of the fetal brains. Datasets were analyzed offline on axial DW images and apparent diffusion coefficient (ADC) maps by two radiologists. The subjective evaluation was described as the absence or presence of water diffusion restriction. The objective evaluation was performed by the placement of 20-mm(2) circular regions of interest on the DW image and ADC maps. Subjective interobserver agreement was assessed by the kappa correlation coefficient. Objective intraobserver and interobserver agreements were assessed by proportionate Bland-Altman tests. Seventy-four DW-MRI scans were performed. Sixty of them (81.1%) were considered to be of good quality. Agreement between the radiologists was 100% for the absence or presence of diffusion restriction of water. For both intraobserver and interobserver agreement of ADC measurements, proportionate Bland-Altman tests showed average percentage differences of less than 1.5% and 95% CI of less than 18% for all sites evaluated. Our data demonstrate that DW-MRI evaluation of the fetal brain in TTTS is feasible and reproducible.

Intraventricular Mass Lesions At Magnetic Resonance Imaging: Iconographic Essay - Part 1.

The present essay is illustrated with magnetic resonance images obtained at the authors' institution over the past 15 years and discusses the main imaging findings of intraventricular tumor-like lesions (ependymoma, pilocytic astrocytoma, central neurocytoma, ganglioglioma, choroid plexus papilloma, primitive neuroectodermal tumors, meningioma, epidermoid tumor). Such lesions represent a subgroup of intracranial lesions with unique characteristics and some image patterns that may facilitate the differential diagnosis.

Intraventricular Mass Lesions At Magnetic Resonance Imaging: Iconographic Essay - Part 2.

The present essay is illustrated with magnetic resonance images obtained at the authors' institution over the past 15 years and discusses the main imaging findings of intraventricular tumor-like lesions (colloid cyst, oligodendroglioma, astroblastoma, lipoma, cavernoma) and of inflammatory/infectious lesions (neurocysticercosis and an atypical presentation of neurohistoplasmosis). Such lesions represent a subgroup of intracranial lesions with unique characteristics and some imaging patterns that may facilitate the differential diagnosis.

Two-dimensional Ultrasound And Ultrasound Elastography Imaging Of Trigger Points In Women With Myofascial Pain Syndrome Treated By Acupuncture And Electroacupuncture: A Double-blinded Randomized Controlled Pilot Study.

Chronic pain has been often associated with myofascial pain syndrome (MPS), which is determined by myofascial trigger points (MTrP). New features have been tested for MTrP diagnosis. The aim of this study was to evaluate two-dimensional ultrasonography (2D US) and ultrasound elastography (UE) images and elastograms of upper trapezius MTrP during electroacupuncture (EA) and acupuncture (AC) treatment. 24 women participated, aged between 20 and 40 years (M ± SD = 27.33 ± 5.05) with a body mass index ranging from 18.03 to 27.59 kg/m2 (22.59 ± 3.11), a regular menstrual cycle, at least one active MTrP at both right (RTPz) and left trapezius (LTPz) and local or referred pain for up to six months. Subjects were randomized into EA and AC treatment groups and the control sham AC (SHAM) group. Intensity of pain was assessed by visual analogue scale; MTrP mean area and strain ratio (SR) by 2D US and UE. A significant decrease of intensity in general, RTPz, and LTPz pain was observed in the EA group (p = 0.027; p < 0.001; p = 0.005, respectively) and in general pain in the AC group (p < 0.001). Decreased MTrP area in RTPz and LTPz were observed in AC (p < 0.001) and EA groups (RTPz, p = 0.003; LTPz, p = 0.005). Post-treatment SR in RTPz and LTPz was lower than pre-treatment in both treatment groups. 2D US and UE effectively characterized MTrP and surrounding tissue, pointing to the possibility of objective confirmation of subjective EA and AC treatment effects.

Detection Of Explosives On The Surface Of Banknotes By Raman Hyperspectral Imaging And Independent Component Analysis.

The aim of this study was to develop a methodology using Raman hyperspectral imaging and chemometric methods for identification of pre- and post-blast explosive residues on banknote surfaces. The explosives studied were of military, commercial and propellant uses. After the acquisition of the hyperspectral imaging, independent component analysis (ICA) was applied to extract the pure spectra and the distribution of the corresponding image constituents. The performance of the methodology was evaluated by the explained variance and the lack of fit of the models, by comparing the ICA recovered spectra with the reference spectra using correlation coefficients and by the presence of rotational ambiguity in the ICA solutions. The methodology was applied to forensic samples to solve an automated teller machine explosion case. Independent component analysis proved to be a suitable method of resolving curves, achieving equivalent performance with the multivariate curve resolution with alternating least squares (MCR-ALS) method. At low concentrations, MCR-ALS presents some limitations, as it did not provide the correct solution. The detection limit of the methodology presented in this study was 50μgcm(-2).

Determination Of Detergent And Dispensant Additives In Gasoline By Ring-oven And Near Infrared Hypespectral Imaging.

A method using the ring-oven technique for pre-concentration in filter paper discs and near infrared hyperspectral imaging is proposed to identify four detergent and dispersant additives, and to determine their concentration in gasoline. Different approaches were used to select the best image data processing in order to gather the relevant spectral information. This was attained by selecting the pixels of the region of interest (ROI), using a pre-calculated threshold value of the PCA scores arranged as histograms, to select the spectra set; summing up the selected spectra to achieve representativeness; and compensating for the superimposed filter paper spectral information, also supported by scores histograms for each individual sample. The best classification model was achieved using linear discriminant analysis and genetic algorithm (LDA/GA), whose correct classification rate in the external validation set was 92%. Previous classification of the type of additive present in the gasoline is necessary to define the PLS model required for its quantitative determination. Considering that two of the additives studied present high spectral similarity, a PLS regression model was constructed to predict their content in gasoline, while two additional models were used for the remaining additives. The results for the external validation of these regression models showed a mean percentage error of prediction varying from 5 to 15%.