59,357 research outputs found

    A randomized controlled trial of PEEK versus titanium interference screws for anterior cruciate ligament reconstruction with 2-year follow-up

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    Purpose: To compare the clinical performance of ACL reconstruction with PEEK and titanium interference screws at 2 years and to evaluate a novel method of measuring tunnel volume. Study Design: Randomized controlled trial; Level of evidence, 1. Methods: A total of 133 patients underwent arthroscopic ACL reconstruction with 4-strand hamstring autografts and were randomized to have titanium or PEEK interference screws for femoral and tibial tunnel fixation. At 2 years, subjective Lysholm and International Knee Documentation Committee scores were assessed and clinical examination performed. At 12 months, MRI was performed to assess graft incorporation and cyst formation, and a novel technique was employed to measure tunnel volumes. Results: There were no significant differences in graft rerupture rate, contralateral ACL rupture rate, subjective outcomes, or objective outcomes. In the titanium and PEEK groups, MRI demonstrated high overall rates of graft integration (96%-100% and 90%-93%, respectively) and ligamentization (89% and 84%) and low rates of synovitis (22% and 10%) and cyst formation (0%-18% and 13%-15%). There was a higher proportion of patients with incomplete graft integration within the femoral tunnel in the PEEK group as compared with the titanium group (10% vs 0%, P = .03); however, the authors suggest that metal artifact precluded proper assessment of the graft in the titanium group by MRI. Tunnel volumes also appeared to be equivalent in the 2 groups and were measured with a novel technique that was highly reproducible in the PEEK group secondary to the absence of flare. Conclusion: Two-year clinical analysis of PEEK interference screws for femoral and tibial fixation of ACL reconstructions showed equivalent clinical performance to titanium interference screws. Given the excellent mechanical characteristics, biological compatibility, and absence of metal artifact on MRI, PEEK has become our material of choice for interference screw fixation in ACL reconstruction

    PVR: Patch-to-Volume Reconstruction for Large Area Motion Correction of Fetal MRI

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    In this paper we present a novel method for the correction of motion artifacts that are present in fetal Magnetic Resonance Imaging (MRI) scans of the whole uterus. Contrary to current slice-to-volume registration (SVR) methods, requiring an inflexible anatomical enclosure of a single investigated organ, the proposed patch-to-volume reconstruction (PVR) approach is able to reconstruct a large field of view of non-rigidly deforming structures. It relaxes rigid motion assumptions by introducing a specific amount of redundant information that is exploited with parallelized patch-wise optimization, super-resolution, and automatic outlier rejection. We further describe and provide an efficient parallel implementation of PVR allowing its execution within reasonable time on commercially available graphics processing units (GPU), enabling its use in the clinical practice. We evaluate PVR's computational overhead compared to standard methods and observe improved reconstruction accuracy in presence of affine motion artifacts of approximately 30% compared to conventional SVR in synthetic experiments. Furthermore, we have evaluated our method qualitatively and quantitatively on real fetal MRI data subject to maternal breathing and sudden fetal movements. We evaluate peak-signal-to-noise ratio (PSNR), structural similarity index (SSIM), and cross correlation (CC) with respect to the originally acquired data and provide a method for visual inspection of reconstruction uncertainty. With these experiments we demonstrate successful application of PVR motion compensation to the whole uterus, the human fetus, and the human placenta.Comment: 10 pages, 13 figures, submitted to IEEE Transactions on Medical Imaging. v2: wadded funders acknowledgements to preprin

    K-Bayes Reconstruction for Perfusion MRI II: Modeling and Technical Development

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    Despite the continued spread of magnetic resonance imaging (MRI) methods in scientific studies and clinical diagnosis, MRI applications are mostly restricted to high-resolution modalities such as structural MRI. While perfusion MRI gives complementary information on blood flow in the brain, its reduced resolution limits its power for detecting specific disease effects on perfusion patterns. This reduced resolution is compounded by artifacts such as partial volume effects, Gibbs ringing, and aliasing, which are caused by necessarily limited k-space sampling and the subsequent use of discrete Fourier transform (DFT) reconstruction. Here, a Bayesian modeling procedure (K-Bayes) is developed for the reconstruction of perfusion MRI. The K-Bayes approach combines a process model for the MRI signal in k-space with a Markov random field prior distribution that incorporates high-resolution segmented structural MRI information. A simulation study, described in Part I (Concepts and Applications), was performed to determine qualitative and quantitative improvements in K-Bayes reconstructed images compared with those obtained via DFT. The improvements were validated using in vivo perfusion MRI data of the human brain. The K-Bayes reconstructed images were demonstrated to provide reduced bias, increased precision, greater effect sizes, and higher resolution than those obtained using DFT

    K-Bayes Reconstruction for Perfusion MRI I: Concepts and Application

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    Despite the continued spread of magnetic resonance imaging (MRI) methods in scientific studies and clinical diagnosis, MRI applications are mostly restricted to high-resolution modalities, such as structural MRI. While perfusion MRI gives complementary information on blood flow in the brain, its reduced resolution limits its power for detecting specific disease effects on perfusion patterns. This reduced resolution is compounded by artifacts such as partial volume effects, Gibbs ringing, and aliasing, which are caused by necessarily limited k-space sampling and the subsequent use of discrete Fourier transform (DFT) reconstruction. In this study, a Bayesian modeling procedure (K-Bayes) is developed for the reconstruction of perfusion MRI. The K-Bayes approach (described in detail in Part II: Modeling and Technical Development) combines a process model for the MRI signal in k-space with a Markov random field prior distribution that incorporates high-resolution segmented structural MRI information. A simulation study was performed to determine qualitative and quantitative improvements in K-Bayes reconstructed images compared with those obtained via DFT. The improvements were validated using in vivo perfusion MRI data of the human brain. The K-Bayes reconstructed images were demonstrated to provide reduced bias, increased precision, greater effect sizes, and higher resolution than those obtained using DFT

    Increasing temporal resolution of DSC perfusion MRI using the analytic image concept

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    Object: Dynamic susceptibility contrast MRI (DSC-MRI) is increasingly being used to evaluate cerebral microcirculation. In this study, the use of the analytic image reconstruction (AIR), with the aim to increase the temporal resolution, is evaluated for DSC-MRI in small animals. Materials and methods: Imaging was performed using a T 2*- weighted sequence to acquire male Lewis rats raw data. Results show that AIR satisfactory reconstructs DSC-MRI while preserving a good reconstruction quality and the image characteristics compared to the full k-space and keyhole reconstructed images. The combination of the choice of the baseline image and the proposed asymmetric acquisition schema enables an increase in temporal resolution, by a factor of four, thus having more sample points for better estimating perfusion parameters. Results: Computer simulations result in a mean cerebral blood volume of 1.22 that deviates from the full k-space value by −3% and a mean cerebral blood flow of 1.97 deviating from the full k-space value by −3% when the mean transit time did not change. Even if these deviations increase when achieving real acquisitions, AIR still better computes quantitative values than keyhole. Conclusion: AIR allows a good reconstruction of the dynamic stage of the image series thus leading to better dynamic effects analysi

    Methods to determine the volume of infrapatellar fat pad as an indicator of anterior cruciate ligament tear

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    Introduction: Anterior knee pain is a common problem which affects adolescents and young adults. The most common mechanism for anterior knee pain arises from combination of compression and shear forces across the patellofemoral joint. The structures that commonly cause anterior knee pain include medial and lateral retinaculum, the patellar subchondral bone, the anterior synovium, joint capsule, patellar tendon, and infrapatellar fat pad. It is important to develop methods to measure volume of infrapatellar fat pad without invasive means. The volume of the fat pad was determined based on 3D image reconstruction using Mimics (a software developed by Materialise), by ellipsoidal methods a mathematical method, and program developed using MATLAB. All three methods had used MRI images to determine its volume. The objective of this study is to determine the accuracy of these new methods. The following hypotheses were tested: (1) increased volume of infrapatellar fat pad among torn ACL, (2) increased body mass index would have larger infrapatellar fat pad, and (3) volumes determined from Mimics, MATLAB, and ellipsoidal model would be accurate. Methods: Our institutional review board approved this retrospective study, which involved a search of patient medical records, and waived the requirement for informed consent because there was no change in patient diagnosis or treatment. The study was an evaluation of all consecutive patients who complained of knee problems undergoing MRI examinations at our institution from 2007 to 2013. Two patient groups were evaluated after a search of surgery records for knee procedures: group 1 consisted of patients who had either a partial tear or complete tear of the ACL based on radiologists’ reading on MRI. Group 2 consisted of patients with an intact ACL on MRI. MRI scans were performed using a 1.5 Tesla General Electric (Milwaukee, Wisconsin) signal MRI Scanner. T1 weighted images in 4-mm thick cuts were evaluated based on the integrity of the image. 3D reconstruction was performed using Mimics (Materialise) software. Results: There is a strong correlation between the volumes determined by ellipsoidal model and MRI. It was determined that the coefficient of determination to be 0.9936. The volume estimated by MATLAB was found to be within a band of ±2 MRI values (27.29 mm3; R2 = 0.4186) and may be considered with high statistical confidence. No significant difference was observed between the two groups (p of 0.99 and 0.26) for ellipsoidal and MATLAB, respectively. Conclusions: Volume determinations using ellipsoidal approximation model had been shown to be comparable to that determined by MRI and MATLAB code within a statistical band of ±2. No statistical significance was observed among methods, with p values of 0.99 and 0.26 for ellipsoidal and MATLAB, respectively. The volume and surface of fat pad in patients with torn ACL are significantly larger than those with intact ACL, p values of 0.01 and 0.04, respectively

    AFFIRM: Affinity Fusion-based Framework for Iteratively Random Motion correction of multi-slice fetal brain MRI

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    Multi-slice magnetic resonance images of the fetal brain are usually contaminated by severe and arbitrary fetal and maternal motion. Hence, stable and robust motion correction is necessary to reconstruct high-resolution 3D fetal brain volume for clinical diagnosis and quantitative analysis. However, the conventional registration-based correction has a limited capture range and is insufficient for detecting relatively large motions. Here, we present a novel Affinity Fusion-based Framework for Iteratively Random Motion (AFFIRM) correction of the multi-slice fetal brain MRI. It learns the sequential motion from multiple stacks of slices and integrates the features between 2D slices and reconstructed 3D volume using affinity fusion, which resembles the iterations between slice-to-volume registration and volumetric reconstruction in the regular pipeline. The method accurately estimates the motion regardless of brain orientations and outperforms other state-of-the-art learning-based methods on the simulated motion-corrupted data, with a 48.4% reduction of mean absolute error for rotation and 61.3% for displacement. We then incorporated AFFIRM into the multi-resolution slice-to-volume registration and tested it on the real-world fetal MRI scans at different gestation stages. The results indicated that adding AFFIRM to the conventional pipeline improved the success rate of fetal brain super-resolution reconstruction from 77.2% to 91.9%
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