Segmentation-Based vs. Regression-Based Biomarker Estimation: A Case Study of Fetus Head Circumference Assessment from Ultrasound Images

The fetus head circumference (HC) is a key biometric to monitor fetus growth during pregnancy, which is estimated from ultrasound (US) images. The standard approach to automatically measure the HC is to use a segmentation network to segment the skull, and then estimate the head contour length from the segmentation map via ellipse fitting, usually after post-processing. In this application, segmentation is just an intermediate step to the estimation of a parameter of interest. Another possibility is to estimate directly the HC with a regression network. Even if this type of segmentation-free approaches have been boosted with deep learning, it is not yet clear how well direct approach can compare to segmentation approaches, which are expected to be still more accurate. This observation motivates the present study, where we propose a fair, quantitative comparison of segmentation-based and segmentation-free (i.e., regression) approaches to estimate how far regression-based approaches stand from segmentation approaches. We experiment various convolutional neural networks (CNN) architectures and backbones for both segmentation and regression models and provide estimation results on the HC18 dataset, as well agreement analysis, to support our findings. We also investigate memory usage and computational efficiency to compare both types of approaches. The experimental results demonstrate that even if segmentation-based approaches deliver the most accurate results, regression CNN approaches are actually learning to find prominent features, leading to promising yet improvable HC estimation results.

Demonstration of Muon-Beam Transverse Phase-Space Compression

We demonstrate efficient transverse compression of a 12.5  MeV/c muon beam stopped in a helium gas target featuring a vertical density gradient and crossed electric and magnetic fields. The muon stop distribution extending vertically over 14 mm was reduced to a 0.25 mm size (rms) within 3.5  μs. The simulation including cross sections for low-energy μ^{+}-He elastic and charge exchange (μ^{+}↔ muonium) collisions describes the measurements well. By combining the transverse compression stage with a previously demonstrated longitudinal compression stage, we can improve the phase space density of a μ^{+} beam by a factor of 10^{10} with 10^{-3} efficiency.

Multiorgan segmentation using distance-aware adversarial networks

Segmentation of organs at risk (OAR) in computed tomography (CT) is of vital importance in radiotherapy treatment. This task is time consuming and for some organs, it is very challenging due to low-intensity contrast in CT. We propose a framework to perform the automatic segmentation of multiple OAR: esophagus, heart, trachea, and aorta. Different from previous works using deep learning techniques, we make use of global localization information, based on an original distance map that yields not only the localization of each organ, but also the spatial relationship between them. Instead of segmenting directly the organs, we first generate the localization map by minimizing a reconstruction error within an adversarial framework. This map that includes localization information of all organs is then used to guide the segmentation task in a fully convolutional setting. Experimental results show encouraging performance on CT scans of 60 patients totaling 11,084 slices in comparison with other state-of-the-art methods.

Medical Image Synthesis with Deep Convolutional Adversarial Networks

Medical imaging plays a critical role in various clinical applications. However, due to multiple considerations such as cost and radiation dose, the acquisition of certain image modalities may be limited. Thus, medical image synthesis can be of great benefit by estimating a desired imaging modality without incurring an actual scan. In this paper, we propose a generative adversarial approach to address this challenging problem. Specifically, we train a fully convolutional network (FCN) to generate a target image given a source image. To better model a nonlinear mapping from source to target and to produce more realistic target images, we propose to use the adversarial learning strategy to better model the FCN. Moreover, the FCN is designed to incorporate an image-gradient-difference-based loss function to avoid generating blurry target images. Long-term residual unit is also explored to help the training of the network. We further apply Auto-Context Model to implement a context-aware deep convolutional adversarial network. Experimental results show that our method is accurate and robust for synthesizing target images from the corresponding source images. In particular, we evaluate our method on three datasets, to address the tasks of generating CT from MRI and generating 7T MRI from 3T MRI images. Our method outperforms the state-of-the-art methods under comparison in all datasets and tasks.

Medical Image Synthesis with Context-Aware Generative Adversarial Networks

Computed tomography (CT) is critical for various clinical applications, e.g., radiation treatment planning and also PET attenuation correction in MRI/PET scanner. However, CT exposes radiation during acquisition, which may cause side effects to patients. Compared to CT, magnetic resonance imaging (MRI) is much safer and does not involve radiations. Therefore, recently researchers are greatly motivated to estimate CT image from its corresponding MR image of the same subject for the case of radiation planning. In this paper, we propose a data-driven approach to address this challenging problem. Specifically, we train a fully convolutional network (FCN) to generate CT given the MR image. To better model the nonlinear mapping from MRI to CT and produce more realistic images, we propose to use the adversarial training strategy to train the FCN. Moreover, we propose an image-gradient-difference based loss function to alleviate the blurriness of the generated CT. We further apply Auto-Context Model (ACM) to implement a context-aware generative adversarial network. Experimental results show that our method is accurate and robust for predicting CT images from MR images, and also outperforms three state-of-the-art methods under comparison.

Joint Segmentation of Multiple Thoracic Organs in CT Images with Two Collaborative Deep Architectures

Computed Tomography (CT) is the standard imaging technique for radiotherapy planning. The delineation of Organs at Risk (OAR) in thoracic CT images is a necessary step before radiotherapy, for preventing irradiation of healthy organs. However, due to low contrast, multi-organ segmentation is a challenge. In this paper, we focus on developing a novel framework for automatic delineation of OARs. Different from previous works in OAR segmentation where each organ is segmented separately, we propose two collaborative deep architectures to jointly segment all organs, including esophagus, heart, aorta and trachea. Since most of the organ borders are ill-defined, we believe spatial relationships must be taken into account to overcome the lack of contrast. The aim of combining two networks is to learn anatomical constraints with the first network, which will be used in the second network, when each OAR is segmented in turn. Specifically, we use the first deep architecture, a deep SharpMask architecture, for providing an effective combination of low-level representations with deep high-level features, and then take into account the spatial relationships between organs by the use of Conditional Random Fields (CRF). Next, the second deep architecture is employed to refine the segmentation of each organ by using the maps obtained on the first deep architecture to learn anatomical constraints for guiding and refining the segmentations. Experimental results show superior performance on 30 CT scans, comparing with other state-of-the-art methods.

Fully automated esophagus segmentation with a hierarchical deep learning approach

Segmentation of organs at risk in CT volumes is a prerequisite for radiotherapy treatment planning. In this paper we focus on esophagus segmentation, a challenging problem since the walls of the esophagus have a very low contrast in CT images. Making use of Fully Convolutional Networks (FCN), we present several extensions that improve the performance, including a new architecture that allows to use low level features with high level information, effectively combining local and global information for improving the localization accuracy. Experiments demonstrate competitive performance on a dataset of 30 CT scans.

Automatic classification of human sperm head morphology

BACKGROUND AND OBJECTIVE

Infertility is a problem that affects up to 15% of couples worldwide with emotional and physiological implications and semen analysis is the first step in the evaluation of an infertile couple. Indeed the morphology of human sperm cells is considered to be a clinical tool dedicated to the fertility prognosis and serves, mainly, for making decisions regarding the options of assisted reproduction technologies. Therefore, a complete analysis of not only normal sperm but also abnormal sperm turns out to be critical in this context. This paper sets out to develop, implement and calibrate a novel methodology to characterize and classify sperm heads towards morphological sperm analysis. Our work is aimed at focusing on a depth analysis of abnormal sperm heads for fertility diagnosis, prognosis, reproductive toxicology, basic research or public health studies.

METHODS

We introduce a morphological characterization for human sperm heads based on shape measures. We also present a pipeline for sperm head classification, according to the last Laboratory Manual for the Examination and Processing of Human Semen of the World Health Organization (WHO). In this sense, we propose a two-stage classification scheme that permits to classify sperm heads among five different classes (one class for normal sperm heads and four classes for abnormal sperm heads) combining an ensemble strategy for feature selection and a cascade approach with several support vector machines dedicated to the verification of each class. We use Fisher's exact test to demonstrate that there is no statistically significant differences between our results and those achieved by domain experts.

RESULTS

Experimental evaluation shows that our two-stage classification scheme outperforms some state-of-the-art monolithic classifiers, exhibiting 58% of average accuracy. More interestingly, on the subset of data for which there is a total agreement between experts for the label of the samples, our system is able to provide 73% of average classification accuracy.

CONCLUSIONS

We show that our system behaves like a human expert; therefore it can be used as a supplementary source for labeling new unknown data. However, as sperm head classification is still a challenging issue due to the uncertainty on the class label of each sperm head, with the consequent high degree of variability among domain experts, we conclude that there are still opportunities for further improvement in designing a more accurate system by investigating other feature extraction methods and classification schemes.

SEGMENTATION OF ORGANS AT RISK IN THORACIC CT IMAGES USING A SHARPMASK ARCHITECTURE AND CONDITIONAL RANDOM FIELDS

Cancer is one of the leading causes of death worldwide. Radiotherapy is a standard treatment for this condition and the first step of the radiotherapy process is to identify the target volumes to be targeted and the healthy organs at risk (OAR) to be protected. Unlike previous methods for automatic segmentation of OAR that typically use local information and individually segment each OAR, in this paper, we propose a deep learning framework for the joint segmentation of OAR in CT images of the thorax, specifically the heart, esophagus, trachea and the aorta. Making use of Fully Convolutional Networks (FCN), we present several extensions that improve the performance, including a new architecture that allows to use low level features with high level information, effectively combining local and global information for improving the localization accuracy. Finally, by using Conditional Random Fields (specifically the CRF as Recurrent Neural Network model), we are able to account for relationships between the organs to further improve the segmentation results. Experiments demonstrate competitive performance on a dataset of 30 CT scans.

Cryogenic distillation facility for isotopic purification of protium and deuterium

Isotopic purification of the protium and deuterium is an important requirement of many physics experiments. A cryogenic facility for high-efficiency separation of hydrogen isotopes with a cryogenic distillation column as the main element is described. The instrument is portable, so that it can be used at the experimental site. It was designed and built at the Petersburg Nuclear Physics Institute, Gatchina, Russia. Fundamental operating parameters have been measured including a liquid holdup in the column packing, the pressure drops across the column and the purity of the product at different operating modes. A mathematical model describes expected profiles of hydrogen isotope concentration along the distillation column. An analysis of ortho-parahydrogen isomeric composition by gas chromatography was used for evaluation of the column performance during the tuning operations. The protium content during deuterium purification (≤100 ppb) was measured using gas chromatography with accumulation of the protium in the distillation column. A high precision isotopic measurement at the Institute of Particle Physics, ETH-Zurich, Switzerland, provided an upper bound of the deuterium content in protium (≤6 ppb), which exceeds all commercially available products.

A Dataset for Breast Cancer Histopathological Image Classification

Today, medical image analysis papers require solid experiments to prove the usefulness of proposed methods. However, experiments are often performed on data selected by the researchers, which may come from different institutions, scanners, and populations. Different evaluation measures may be used, making it difficult to compare the methods. In this paper, we introduce a dataset of 7909 breast cancer histopathology images acquired on 82 patients, which is now publicly available from http://web.inf.ufpr.br/vri/breast-cancer-database. The dataset includes both benign and malignant images. The task associated with this dataset is the automated classification of these images in two classes, which would be a valuable computer-aided diagnosis tool for the clinician. In order to assess the difficulty of this task, we show some preliminary results obtained with state-of-the-art image classification systems. The accuracy ranges from 80% to 85%, showing room for improvement is left. By providing this dataset and a standardized evaluation protocol to the scientific community, we hope to gather researchers in both the medical and the machine learning field to advance toward this clinical application.

Robust feature selection to predict tumor treatment outcome

OBJECTIVE

Recurrence of cancer after treatment increases the risk of death. The ability to predict the treatment outcome can help to design the treatment planning and can thus be beneficial to the patient. We aim to select predictive features from clinical and PET (positron emission tomography) based features, in order to provide doctors with informative factors so as to anticipate the outcome of the patient treatment.

METHODS

In order to overcome the small sample size problem of datasets usually met in the medical domain, we propose a novel wrapper feature selection algorithm, named HFS (hierarchical forward selection), which searches forward in a hierarchical feature subset space. Feature subsets are iteratively evaluated with the prediction performance using SVM (support vector machine). All feature subsets performing better than those at the preceding iteration are retained. Moreover, as SUV (standardized uptake value) based features have been recognized as significant predictive factors for a patient outcome, we propose to incorporate this prior knowledge into the selection procedure to improve its robustness and reduce its computational cost.

RESULTS

Two real-world datasets from cancer patients are included in the evaluation. We extract dozens of clinical and PET-based features to characterize the patient's state, including SUV parameters and texture features. We use leave-one-out cross-validation to evaluate the prediction performance, in terms of prediction accuracy and robustness. Using SVM as the classifier, our HFS method produces accuracy values of 100% and 94% on the two datasets, respectively, and robustness values of 89% and 96%. Without accuracy loss, the prior-based version (pHFS) improves the robustness up to 100% and 98% on the two datasets, respectively.

CONCLUSIONS

Compared with other feature selection methods, the proposed HFS and pHFS provide the most promising results. For our HFS method, we have empirically shown that the addition of prior knowledge improves the robustness and accelerates the convergence.

Joint tumor growth prediction and tumor segmentation on therapeutic follow-up PET images

Tumor response to treatment varies among patients. Patient-specific prediction of tumor evolution based on medical images during the treatment can help to build and adapt patient's treatment planning in a non-invasive way. Personalized tumor growth modeling allows patient-specific prediction by estimating model parameters based on individual's images. The model parameters are often estimated by optimizing a cost function constructed based on the tumor delineations. In this paper, we propose a joint framework for tumor growth prediction and tumor segmentation in the context of patient's therapeutic follow ups. Throughout the treatment, a series of sequential positron emission tomography (PET) images are acquired for tumor response monitoring. We propose to take into account the predicted information, which is used in combination with the random walks (RW) algorithm, to develop an automatic tumor segmentation method on PET images. Moreover, we propose an iterative scheme of RW, making the segmentation more performant. Furthermore, the obtained segmentation is applied to the process of model parameter estimation so as to get the model based prediction of tumor evolution. We evaluate our methods on 7 lung tumor patients, totaling 29 PET exams, under radiotherapy by comparing the obtained tumor prediction and tumor segmentation with manual tumor delineation by expert. Our system produces promising results when compared to the state-of-the-art methods.

Prediction of lung tumor evolution during radiotherapy in individual patients with PET

We propose a patient-specific model based on partial differential equation to predict the evolution of lung tumors during radiotherapy. The evolution of tumor cell density is formulated by three terms: 1) advection describing the advective flux transport of tumor cells, 2) proliferation representing the tumor cell proliferation modeled as Gompertz differential equation, and 3) treatment quantifying the radiotherapeutic efficacy from linear quadratic formulation. We consider that tumor cell density variation can be derived from positron emission tomography images, the novel idea is to model the advection term by calculating 3D optical flow field from sequential images. To estimate patient-specific parameters, we propose an optimization between the predicted and observed images, under a global constraint that the tumor volume decreases exponentially as radiation dose increases. A thresholding on the predicted tumor cell densities is then used to define tumor contours, tumor volumes and maximum standardized uptake values (SUVmax). Results obtained on seven patients show a satisfying agreement between the predicted tumor contours and those drawn by an expert.

Measurement of muon capture on the proton to 1% precision and determination of the pseudoscalar coupling gP

The MuCap experiment at the Paul Scherrer Institute has measured the rate Λ(S) of muon capture from the singlet state of the muonic hydrogen atom to a precision of 1%. A muon beam was stopped in a time projection chamber filled with 10-bar, ultrapure hydrogen gas. Cylindrical wire chambers and a segmented scintillator barrel detected electrons from muon decay. Λ(S) is determined from the difference between the μ(-) disappearance rate in hydrogen and the free muon decay rate. The result is based on the analysis of 1.2 × 10(10) μ(-) decays, from which we extract the capture rate Λ(S) = (714.9 ± 5.4(stat) ± 5.1(syst)) s(-1) and derive the proton's pseudoscalar coupling g(P)(q(0)(2) = -0.88 m(μ)(2)) = 8.06 ± 0.55.

Classification of endomicroscopic images of the lung based on random subwindows and extra-trees

Recently, the in vivo imaging of pulmonary alveoli was made possible thanks to confocal microscopy. For these images, we wish to aid the clinician by developing a computer-aided diagnosis system, able to discriminate between healthy and pathological subjects. The lack of expertise currently available on these images has first led us to choose a generic approach, based on pixel-value description of randomly extracted subwindows and decision tree ensemble for classification (extra-trees). In order to deal with the great complexity of our images, we adapt this method by introducing a texture-based description of the subwindows, based on local binary patterns. We show through our experimental protocol that this adaptation is a promising way to classify fibered confocal fluorescence microscopy images. In addition, we introduce a rejection mechanism on the classifier output to prevent nondetection errors.

Cardiac MRI assessment of right ventricular function in acquired heart disease: factors of variability

RATIONALE AND OBJECTIVES

To evaluate intra- and inter-observer variability of right ventricular (RV) functional parameters as evaluated by cardiac magnetic resonance imaging (MRI) in patients with acquired heart disease (AHD), and to identify factors associated with an increased variability.

MATERIALS AND METHODS

Sixty consecutive patients were enrolled. Right and left ventricular (LV) volumes, ejection fraction, and mass were determined from short-axis cine sequences. All analyzes were performed twice by three observers with various training-degree in cardiac MRI. Intra- and inter-observer variability was evaluated. The impact on variability of each of the following parameters was assessed: observer's experience, basal and apical slices selection, end-systolic phase selection, and delineation.

RESULTS

Mean segmentation time ranged 9.8-19.0 minutes for RV and 6.4-9.2 minutes for LV. Variability of RV functional parameters measurement was strongly influenced by previous observer's experience: it was two to three times superior to that of LV, even for the most experienced observer. High variability in the measurement of RV mass was observed. For both ventricles, selection of the basal slice and delineation were major determinants of variability.

CONCLUSION

As compared to LV, RV function assessment with cardiac MRI in AHD patients is much more variable and time-consuming. Observer's experience, selection of basal slice, and delineation are determinant.

An SVM-based distal lung image classification using texture descriptors

A novel imaging technique can now provide microscopic images of the distal lung in vivo, for which quantitative analysis tools need to be developed. In this paper, we present an image classification system that is able to discriminate between normal and pathological images. Different feature spaces for discrimination are investigated and evaluated using a support vector machine. Best classification rates reach up to 90% and 95% on non-smoker and smoker groups, respectively. A feature selection process is also implemented, that allows us to gain some insight about these images. Whereas further tests on extended databases are needed, these first results indicate that efficient computer based automated classification of normal vs. pathological images of the distal lung is feasible.

Diagnostic accuracy and variability of three semi-quantitative methods for assessing right ventricular systolic function from cardiac MRI in patients with acquired heart disease

OBJECTIVES

To evaluate the diagnostic accuracy and variability of 3 semi-quantitative (SQt) methods for assessing right ventricular (RV) systolic function from cardiac MRI in patients with acquired heart disease: tricuspid annular plane systolic excursion (TAPSE), RV fractional-shortening (RVFS) and RV fractional area change (RVFAC).

METHODS

Sixty consecutive patients were enrolled. Reference RV ejection fraction (RVEF) was determined from short axis cine sequences. TAPSE, RVFS and RVFAC were measured on a 4-chamber cine sequence. All SQt analyses were performed twice by 3 observers with various degrees of training in cardiac MRI. Correlation with RVEF, intra- and inter-observer variability, and receiver operating characteristic (ROC) curve analysis were performed for each SQt method.

RESULTS

Correlation between RVFAC and RVEF was good for all observers and did not depend on previous cardiac MRI experience (R range = 0.716-0.741). Conversely, RVFS (R range = 0.534-0.720) and TAPSE (R range = 0.482-0.646) correlated less with RVEF and depended on previous experience. Intra- and inter-observer variability was much lower for RVFAC than for RVFS and TAPSE. ROC analysis demonstrated that RVFAC <41% could predict a RVEF <45% with 90% sensitivity and 94% specificity.

CONCLUSIONS

RVFAC appears to be more accurate and reproducible than RVFS and TAPSE for SQt assessment of RV function by cardiac MRI.

A review of segmentation methods in short axis cardiac MR images

For the last 15 years, Magnetic Resonance Imaging (MRI) has become a reference examination for cardiac morphology, function and perfusion in humans. Yet, due to the characteristics of cardiac MR images and to the great variability of the images among patients, the problem of heart cavities segmentation in MRI is still open. This paper is a review of fully and semi-automated methods performing segmentation in short axis images using a cardiac cine MRI sequence. Medical background and specific segmentation difficulties associated to these images are presented. For this particularly complex segmentation task, prior knowledge is required. We thus propose an original categorization for cardiac segmentation methods, with a special emphasis on what level of external information is required (weak or strong) and how it is used to constrain segmentation. After reviewing method principles and analyzing segmentation results, we conclude with a discussion and future trends in this field regarding methodological and medical issues.

[Functional MR urography in children]

MR Urography (MRU) provides both morphologic and functional information without radiation exposure. It enables the assessment of split renal function, excretion, and quantification of obstruction. MRU is thus complementary to ultrasonography in the assessment of pre- and post-natal obstructive uropathies in children. If available, MRU should be definitely preferred to intravenous urography.

Measurement of the muon capture rate in hydrogen gas and determination of the proton's pseudoscalar coupling gP

The rate of nuclear muon capture by the proton has been measured using a new technique based on a time projection chamber operating in ultraclean, deuterium-depleted hydrogen gas, which is key to avoiding uncertainties from muonic molecule formation. The capture rate from the hyperfine singlet ground state of the microp atom was obtained from the difference between the micro(-) disappearance rate in hydrogen and the world average for the micro(+) decay rate, yielding Lambda(S)=725.0+/-17.4 s(-1), from which the induced pseudoscalar coupling of the nucleon, g(P)(q(2)=-0.88m(2)(micro))=7.3+/-1.1, is extracted.

Lyapunov generation of entanglement and the correspondence principle

We show how a classically vanishing interaction generates entanglement between two initially nonentangled particles, without affecting their classical dynamics. For chaotic dynamics, the rate of entanglement is shown to saturate at the Lyapunov exponent of the classical dynamics as the interaction strength increases. In the saturation regime, the one-particle Wigner function follows classical dynamics better and better as one goes deeper and deeper in the semiclassical limit. This demonstrates that quantum-classical correspondence at the microscopic level does not require coupling to a large number of external degrees of freedom.

Quantum reversibility and echoes in interacting systems

In echo experiments, imperfect time-reversal operations are performed on a subset of the total number of degrees of freedom. To capture the physics of these experiments, we introduce a partial fidelity M(B)(t), the Boltzmann echo, where only part of the system's degrees of freedom can be time reversed. We present a semiclassical calculation of M(B)(t). We show that, as the time-reversal operation is performed more and more accurately, the decay rate of M(B)(t) saturates at a value given by the decoherence rate of the controlled degrees of freedom due to their coupling to uncontrolled ones. We connect these results with NMR spin echo experiments.

Systemic allergy to human insulin and its rapid and long acting analogs: successful treatment by continuous subcutaneous insulin lispro infusion

Since the introduction of highly purified human recombinant insulin, allergy to insulin has become a very rare clinical situation, encountered in less than 1% of patients. It results in potentially life-threatening immediate or delayed, local and general manifestations. Different treatments of unequal efficiency have been proposed, the use of insulin analogs showing benefits in certain situations. We report the case of a type 2 diabetic patient who presented local reactions and then an anaphylactic shock after the introduction of insulin analog premixes. Intra-dermal reactions performed with porcine, human and insulin analogs preparations (aspart, lispro, glargine) were all positive, as well as the specific anti-insulin IgE measurement. Because we could not achieve normoglycaemia with maximal oral treatment and low caloric diet, we decided to attempt a desensitisation by continuous subcutaneous infusion of insulin lispro, since the lowest skin reaction was obtained with this insulin. We were able to induce a tolerance, by means of very low basal rate, very slowly increased, without any boluses, and maintaining antihistamine therapy. Six months later, the patient remains free of any symptom and has achieved a quite good glycaemic control. We describe for the first time a case of allergy to human insulin and to all available rapid and long acting analogs. We show the interest of a treatment with CSII of analogs in order to induce tolerance.

A non-rigid registration approach for quantifying myocardial contraction in tagged MRI using generalized information measures

We address the problem of quantitatively assessing myocardial function from tagged MRI sequences. We develop a two-step method comprising (i) a motion estimation step using a novel variational non-rigid registration technique based on generalized information measures, and (ii) a measurement step, yielding local and segmental deformation parameters over the whole myocardium. Experiments on healthy and pathological data demonstrate that this method delivers, within a reasonable computation time and in a fully unsupervised way, reliable measurements for normal subjects and quantitative pathology-specific information. Beyond cardiac MRI, this work redefines the foundations of variational non-rigid registration for information-theoretic similarity criteria with potential interest in multimodal medical imaging.

Assessment of myocardial function: a review of quantification methods and results using tagged MRI

Tagged MRI provides a noninvasive way to assess the regional function of the heart. Clinical use of myocardial strain measurements from tagged MRI requires identifying new normative values. As for cardiac motion estimation, a variety of methods for quantifying myocardial deformations have been proposed in the image analysis and medical literature, based on heart geometry and continuum mechanics. This article comparatively reviews existing quantification techniques, and synthesizes their results to establish confidence intervals for the standard deformation parameters.

Quantification of myocardial function using tagged MR and cine MR images

Magnetic Resonance Imaging (MRI) is recognized as a relevant modality for dynamically imaging the heart anatomy and function, and achieving satisfying qualitative diagnosis. Reliable methods for quantitatively analyzing cardiac motion from MR images remain, however, to be elaborated. This paper presents a novel approach for measuring myocardial deformations from tagged MRI sequences. Based on efficient pixel-based statistical non-rigid registration, it allows for automatically extracting local/global deformation parameters at the pixel and myocardial segment scales. Its performances for assessing the myocardial function are illustrated both for the normal heart and for pathologies of the ischemic and dilated cardiomyopathic type.

Measurement of the resonant d(mu)t molecular formation rate in solid HD

Measurements of muon-catalyzed dt fusion ( d(mu)t-->4He + n + mu(-)) in solid HD have been performed. The theory describing the energy dependent resonant molecular formation rate for the reaction (mu)t + HD-->[(d(mu)t)pee](*) is compared to experimental results in a pure solid HD target. Constraints on the rates are inferred through the use of a Monte Carlo model developed specifically for the experiment. From the time-of-flight analysis of fusion events in 16 and 37 microg x cm(-2) targets, an average formation rate consistent with 0.897+/-(0.046)(stat)+/-(0.166)(syst) times the theoretical prediction was obtained.

[Analgesia with oxygen-nitrous oxide mixture during percutaneous renal biopsy in children]

The efficacy of an inhaled equimolar mixture of nitrous oxide and oxygen (Entonox/MEOPA) to prevent procedural pain during renal percutaneous biopsies in children was assessed.

PATIENTS AND METHODS

One hundred and seven children who underwent 113 renal biopsies during a 17-month period were included in a prospective uncontrolled pediatric study. Efficacy was evaluated using patients' answers to a questionnaire and nurses' observations.

RESULTS

Pain was absent in 86.5% of the cases. Mild adverse events were noted in one-third of the procedures, and were always reversible within a few minutes when the inhalation stopped. Acceptability was good. The use of this gas is easy and safe provided a few precautions are observed.

CONCLUSION

Inhaled equimolar mixture of nitrous oxide and oxygen prevents procedural pain during renal percutaneous biopsies.

Resonant formation of d&mgr;t molecules in deuterium: An atomic beam measurement of muon catalyzed dt fusion

Resonant formation of d&mgr;t molecules in collisions of muonic tritium ( &mgr;t) on D2 was investigated using a beam of &mgr;t atoms, demonstrating a new direct approach in muon catalyzed fusion studies. Strong epithermal resonances in d&mgr;t formation were directly revealed for the first time. From the time-of-flight analysis of 2036+/-116 dt fusion events, a formation rate consistent with 0.73+/-(0.16)(meas)+/-(0.09)(model) times the theoretical prediction was obtained. For the largest peak at a resonance energy of 0.423+/-0.037 eV, this corresponds to a rate of (7.1+/-1.8)x10(9) s(-1), more than an order of magnitude larger than those at low energies.

Thrombotic carotid megabulb: fibromuscular dysplasia, septae, and ischemic stroke

The proximal internal carotid artery is most commonly spared in cerebral fibromuscular dysplasia. The authors report the cases of three young black patients with stroke and carotid megabulbs with fibrous components, two of whom had superimposed thrombi.

[Diaphragmatic or juxtadiaphragmatic schwannoma?]

The authors report one case of schwannoma of diaphragmatic topography. This lesion is exceptional and, to our knowledge, only seven cases have been published in the literature. The first diagnosis was a suprarenal tumour. Magnetic resonance imaging provided more detail about the juxta diaphragmatic topography of the tumour, which was subsequently confirmed by surgery. The diagnosis of benign schwannoma was made at the pathological and immuno-histochemical examination of the specimen. The schwannoma corresponded to the type B of the classification of Antoni. Pathogeny and origin of the tumour are discussed. The sympathetic nervous para-vertebral system, the phrenic or intercostal nerves could be the origin of the tumour. In the eight cases reported, the tumoural removal was performed through thoracotomy (n = 5) or laparotomy (n = 3). The preoperative exact location of the juxta diaphragmatic tumours remains difficult to specify.

[Magnetic resonance angiography: fields of exploration, main indications and limitations]

Magnetic resonance angiography (MRA) has become a widely accepted technique with regards to the other available noninvasive techniques in the diagnosis of vascular disease. This paper proposes a review of the different indications of this technique in arterial and venous diseases. Among several MRA pulse sequences, the most frequently used until today consisted of a time-of-flight technique which provided angiograms without any injection of intravascular contrast medium. It required to be performed in a plane perpendicular to the main axis of the vessel to be optimal. New techniques, such as contrast medium bolus-enhanced acquisitions allow examination of vascular segments in a plane parallel to their course (coronal for the aorta and lower limb arteries). An increasing number of clinical applications has raised since the implementation of MRA techniques on MR devices; some of them are widely accepted, whereas some others remain under the scope of extensive validation. With a high level of accuracy in grading carotid artery stenosis, MRA is now routinely used in cerebral arterial occlusive diseases and has in part replaced contrast angiography. MRA of the venous system of the brain plays a major role in the diagnosis and follow up of dural venous thrombosis. Other vascular brain diseases, such as vascular malformations, yet have limited uses. Carotid artery dissections are fairly demonstrated with MRA, which can be used for diagnosis as well as for follow-up. The accuracy of MRA in the diagnosis of venous thrombosis of the cervical/mediastinal veins has been reported as high as 100%. Moreover, MRA allows a precise assessment of collateral vessels in case of complete cervical/mediastinal venous thrombosis.

[Interruption of the inferior vena cava by the DIL filter. Experience apropos of 34 cases]

Endocaval filters are often used to prevent pulmonary embolism but they have a number of disadvantages. The DIL filter, made of a memory metal wire, is intended to male up for some of these disadvantages. It acts by modifying the shape of the inferior vena cava, which it filters through its meshed loops. It is inserted percutaneously, causes little trauma, and its release is progressive. However, it requires measuring the caliber of the inferior vena cava. This filter was inserted in thirty-four patients over a period of 13 months. One filter has migrated. No recurrence of pulmonary embolism and no thrombosis of the inferior vena cave occurred.