Entropy-based analysis for diffusion anisotropy mapping of healthy and myelopathic spinal cord

The present study utilized diffusion MR imaging and fractional anisotropy (FA) mapping to delineate the microstructure of spinal cord. The concept of Shannon entropy was introduced to analyze the complex microstructure of healthy and injured spinal cords based on FA map. A total of 30 volunteers were recruited in this study with informed consent, including 13 healthy adult subjects (group A, 25±3 years), 12 healthy elderly subjects (group B, 53±7 years) and 5 cervical spondylotic myelopathy (CSM) patients (group C, 53±15 years). Diffusion MRI images of cervical spinal cord were taken using pulsed gradient spin-echo-echo-planar imaging (SE-EPI) sequence with a 3T MR system. The region of interest was defined to cover the spinal cord in FA maps. The Shannon entropy of FA values of voxels in the cord was calculated as well as the average FA values. The significant differences were determined among three groups using one-way ANOVA and post-hoc test. As compared with adult and elderly healthy subjects, the entropy of whole spinal cord was significantly lower in CSM patients (group A: 6.07±0.18; B: 6.01±0.23; C: 5.32±0.44; p<0.05). Whereas there were no significant difference in FA values among groups (group A: 0.62±0.08; B: 0.64±0.09; C: 0.64±0.12). In CSM patients, there was a loss of architectural structural complexity in the cervical spinal cord tissue as noted by the lower Shannon entropy value. It indicated the potential application of entropy-based analysis for the diagnosis of the severity of chronic compressive spinal cord injuries, i.e. CSM.     

Left ventricular shape variation in asymptomatic populations: the multi-ethnic study of atherosclerosis

Background

Although left ventricular cardiac geometric indices such as size and sphericity characterize adverse remodeling and have prognostic value in symptomatic patients, little is known of shape distributions in subclinical populations. We sought to quantify shape variation across a large number of asymptomatic volunteers, and examine differences among sub-cohorts.

Methods

An atlas was constructed comprising 1,991 cardiovascular magnetic resonance (CMR) cases contributed from the Multi-Ethnic Study of Atherosclerosis baseline examination. A mathematical model describing regional wall motion and shape was used to establish a coordinate map registered to the cardiac anatomy. The model was automatically customized to left ventricular contours and anatomical landmarks, corrected for breath-hold mis-registration between image slices. Mathematical techniques were used to characterize global shape distributions, after removal of translations, rotations, and scale due to height. Differences were quantified among ethnicity, sex, smoking, hypertension and diabetes sub-cohorts.

Results

The atlas construction process yielded accurate representations of global shape (errors between manual and automatic surface points in 244 validation cases were less than the image pixel size). After correction for height, the dominant shape component was associated with heart size, explaining 32% of the total shape variance at end-diastole and 29% at end-systole. After size, the second dominant shape component was sphericity at end-diastole (13%), and concentricity at end-systole (10%). The resulting shape components distinguished differences due to ethnicity and risk factors with greater statistical power than traditional mass and volume indices.

Conclusions

We have quantified the dominant components of global shape variation in the adult asymptomatic population. The data and results are available at cardiacatlas.org. Shape distributions were principally explained by size, sphericity and concentricity, which are known correlates of adverse outcomes. Atlas-based global shape analysis provides a powerful method for quantifying left ventricular shape differences in asymptomatic populations.

Trial registration

ClinicalTrials.gov {"type":"clinical-trial","attrs":{"text":"NCT00005487","term_id":"NCT00005487"}}NCT00005487     

Robust colonoscope tracking method for colon deformations utilizing coarse-to-fine correspondence findings

Purpose

Polyps found during CT colonography can be removed by colonoscopic polypectomy. A colonoscope navigation system that navigates a physician to polyp positions while performing the colonoscopic polypectomy is required. Colonoscope tracking methods are essential for implementing colonoscope navigation systems. Previous colonoscope tracking methods have failed when the colon deforms during colonoscope insertions. This paper proposes a colonoscope tracking method that is robust against colon deformations.

Method

The proposed method generates a colon centerline from a CT volume and a curved line representing the colonoscope shape (colonoscope line) by using electromagnetic sensors. We find correspondences between points on a deformed colon centerline and colonoscope line by a landmark-based coarse correspondence finding and a length-based fine correspondence finding processes. Even if the coarse correspondence finding process fails to find some correspondences, which occurs with colon deformations, the fine correspondence finding process is able to find correct correspondences by using previously recorded line lengths.

Result

Experimental results using a colon phantom showed that the proposed method finds the colonoscope tip position with tracking errors smaller than 50 mm in most trials. A physician who specializes in gastroenterology commented that tracking errors smaller than 50 mm are acceptable. This is because polyps are observable from the colonoscope camera when positions of the colonoscope tip and polyps are closer than 50 mm.

Conclusions

We developed a colonoscope tracking method that is robust against deformations of the colon. Because the process was designed to consider colon deformations, the proposed method can track the colonoscope tip position even if the colon deforms.

     

An active shape model for the reconstruction of scoliotic deformities from back shape data

OBJECTIVE. The objective of the present study is to improve the accuracy and reliability of the spinal midline reconstructions in scoliosis from back shape data. Design. An active shape model which covers the variety of scoliotic curves with a minimum of adjustable parameters is designed for the reconstruction of the spinal midline based on rasterstereographic back surface measurements. BACKGROUND. To reduce the number of X-rays needed for patients with a scoliosis, an automatic method was developed for the reconstruction of spinal deformities from back shape data. METHODS. 264 digital X-rays of 264 patients with scoliosis were used as a training set. By examining the statistics of the vertebral body centres a point distribution model was derived. The model is used as a basis to reconstruct the spinal midline from back shape data (active shape model). RESULTS. 478 rasterstereographs of 114 scoliotic patients have been evaluated with this new procedure and with an existing procedure. Both procedures deliver a three-dimensional curve of the spinal midline. The frontal projections of these spinal midlines are compared with the vertebral centres of the corresponding 478 X-rays. The active shape model improved the results as compared to the existing procedure from sigma(x)=3.4mm to sigma(x)=3.0mm. CONCLUSION. The use of the active shape model improves the overall accuracy of the spinal midline reconstructions in scoliosis from back shape data.RELEVANCE. Improvements to the accuracy and the reliability of the three-dimensional spinal reconstruction based on back shape data, can lead to an additional reduction of X-rays for patients with a scoliosis.     

Pen pals: correspondence as a method for data generation in qualitative research

The study aimed to understand the impact of chronic illness on the lives of midlife women and explore and share the ways in which women adapt to and/or tolerate chronic illness in their lives. In 1998, 80 women participated in a study in which data were generated by corresponding with the researcher. Guided by feminist principles of collaboration, reciprocity and disclosure, we created rich stories about what it is like to live with a chronic illness. In this paper we will discuss the first phase of this inquiry which utilized correspondence between the researcher and the women. The issues posed by the use of correspondence as an innovative data generation process will be analysed. Correspondence, at first glance, may appear to be a rather impersonal communication medium. However, we are committed to this method of data generation and believe we have unlocked the doors to a viable qualitative research process. The literature to guide this process is scarce so we are keen to share work in progress. We will describe the preparation phase in setting up the study; discuss some practical issues, share some of the researcher's experiences in generating narratives from dialogues and hear from the women themselves what they consider to be significant about this research process.     

Heavy particle therapy for prostate cancer

Heavy particle (ion) beams are characterized by high relative biological effectiveness and improved dose distribution. To establish heavy ion therapy for prostate cancer, three trials have been conducted. For 247 patients with T1b-T3 cancer, carbon ion beam was irradiated 20 times/5 weeks with or without endocrine therapy. Overall and cause-specific survivals were excellent and local control was achieved in all patients except one. Grade 3 late morbidity of rectum and/or bladder/urethra was developed in 7 cases who received higher dose. Thus, total dose was decreased to 66 GyE and the radiation field was coned down during the treatment. In conclusion, carbon ion therapy is expected to exert excellent effect in the treatment of localized and locally advanced prostate cancer.     

A latex particle assay for platelet-associated IgG

A modified, sensitive, solid-phase assay for platelet-associated IgG is reported. Direct comparisons were made between a 125I monoclonal radioimmunoassay (RIA) and the polyclonal antibody latex particle assay. In 209 simultaneous comparisons with the RIA, the sensitivity of the latex test was 91 percent, specificity was 100 percent, and overall efficiency was 97 percent. Commencing with platelet-rich plasma, the direct latex particle test for platelet-bound IgG requires 45 minutes; 90 minutes are required to crossmatch one patient with 12 donors. The advantages of the latex assay are absence of radioactivity, stability of reagents, economy, speed, specificity, and sensitivity.     

Methodology for measuring diversity in human populations

A population is considered diverse if it contains individuals with a wide variety of demographic and cultural characteristics or attributes. However, it is often difficult to compare the relative diversity of two groups. It is even more difficult to specifically measure or quantify the diversity of any single group. In this paper a three step process for measuring and quantifying diversity in a human populations is described. The measurement methodologies illustrated in an example using this process are based upon fractionalization techniques and mathematical information theory.     

Groupwise surface correspondence by optimization: representation and regularization

Groupwise optimization of correspondence across a set of unlabelled examples of shapes or images is a well-established technique that has been shown to produce quantitatively better models than other approaches. However, the computational cost of the optimization is high, leading to long convergence times. In this paper, we show how topologically non-trivial shapes can be mapped to regular grids, hence represented in terms of vector-valued functions defined on these grids (the shape image representation). This leads to an initial reduction in computational complexity. We also consider the question of regularization, and show that by borrowing ideas from image registration, it is possible to build a non-parametric, fluid regularizer for shapes, without losing the computational gain made by the use of shape images. We show that this non-parametric regularization leads to a further considerable gain, when compared to parametric regularization methods. Quantitative evaluation is performed on biological datasets, and shown to yield a substantial decrease in convergence time, with no loss of model quality.     

层级对应管理在血液透析护理中的应用效果

目的探讨层级对应管理在血液透析护理中的应用效果。方法2017年12月在我院血液透析护理管理中实施层级对应管理,将2017年9—11月收治的血液透析患者设为对照组,将2017年12月至2018年2月收治的血液透析患者60例设为观察组,对照组采用常规护理,观察组采用层级对应管理,比较两组患者护理质量及护理依从性。结果观察组基础护理、穿刺护理、内瘘护理、照护指导评分明显高于对照组(P<0.05);观察组护理依从性明显高于对照组(P<0.05)。结论层级对应管理在血液透析护理管理中的应用,能提高临床护理质量,可提高护理依从性。     

Towards robust and effective shape modeling: sparse shape composition

Organ shape plays an important role in various clinical practices, e.g., diagnosis, surgical planning and treatment evaluation. It is usually derived from low level appearance cues in medical images. However, due to diseases and imaging artifacts, low level appearance cues might be weak or misleading. In this situation, shape priors become critical to infer and refine the shape derived by image appearances. Effective modeling of shape priors is challenging because: (1) shape variation is complex and cannot always be modeled by a parametric probability distribution; (2) a shape instance derived from image appearance cues (input shape) may have gross errors; and (3) local details of the input shape are difficult to preserve if they are not statistically significant in the training data. In this paper we propose a novel Sparse Shape Composition model (SSC) to deal with these three challenges in a unified framework. In our method, a sparse set of shapes in the shape repository is selected and composed together to infer/refine an input shape. The a priori information is thus implicitly incorporated on-the-fly. Our model leverages two sparsity observations of the input shape instance: (1) the input shape can be approximately represented by a sparse linear combination of shapes in the shape repository; (2) parts of the input shape may contain gross errors but such errors are sparse. Our model is formulated as a sparse learning problem. Using L1 norm relaxation, it can be solved by an efficient expectation-maximization (EM) type of framework. Our method is extensively validated on two medical applications, 2D lung localization in X-ray images and 3D liver segmentation in low-dose CT scans. Compared to state-of-the-art methods, our model exhibits better performance in both studies.     

Posterior shape models

We present a method to compute the conditional distribution of a statistical shape model given partial data. The result is a “posterior shape model”, which is again a statistical shape model of the same form as the original model. This allows its direct use in the variety of algorithms that include prior knowledge about the variability of a class of shapes with a statistical shape model. Posterior shape models then provide a statistically sound yet easy method to integrate partial data into these algorithms. Usually, shape models represent a complete organ, for instance in our experiments the femur bone, modeled by a multivariate normal distribution. But because in many application certain parts of the shape are known a priori, it is of great interest to model the posterior distribution of the whole shape given the known parts. These could be isolated landmark points or larger portions of the shape, like the healthy part of a pathological or damaged organ. However, because for most shape models the dimensionality of the data is much higher than the number of examples, the normal distribution is singular, and the conditional distribution not readily available. In this paper, we present two main contributions: First, we show how the posterior model can be efficiently computed as a statistical shape model in standard form and used in any shape model algorithm. We complement this paper with a freely available implementation of our algorithms. Second, we show that most common approaches put forth in the literature to overcome this are equivalent to probabilistic principal component analysis (PPCA), and Gaussian Process regression. To illustrate the use of posterior shape models, we apply them on two problems from medical image analysis: model-based image segmentation incorporating prior knowledge from landmarks, and the prediction of anatomically correct knee shapes for trochlear dysplasia patients, which constitutes a novel medical application. Our experiments confirm that the use of conditional shape models for image segmentation improves the overall segmentation accuracy and robustness.