Analysing Magnetism Using Scanning SQUID Microscopy

Scanning superconducting quantum interference device microscopy (SSM) is a scanning probe technique that images local magnetic flux, which allows for mapping of magnetic fields with high field and spatial accuracy. Many studies involving SSM have been published in the last decades, using SSM to make qualitative statements about magnetism. However, quantitative analysis using SSM has received less attention. In this work, we discuss several aspects of interpreting SSM images and methods to improve quantitative analysis. First, we analyse the spatial resolution and how it depends on several factors. Second, we discuss the analysis of SSM scans and the information obtained from the SSM data. Using simulations, we show how signals evolve as a function of changing scan height, SQUID loop size, magnetization strength and orientation. We also investigated 2-dimensional autocorrelation analysis to extract information about the size, shape and symmetry of magnetic features. Finally, we provide an outlook on possible future applications and improvements.Comment: 16 pages, 10 figure

Assessment of Vessel Density on Non-Contrast Computed Tomography to Detect Basilar Artery Occlusion

Introduction: Basilar artery occlusion (BAO) may be clinically occult due to variable and non-specific symptomatology. We evaluated the qualitative and quantitative determination of a hyperdense basilar artery (HDBA) on non-contrast computed tomography (NCCT) brain for the diagnosis of BAO.Methods: We conducted a case control study of patients with confirmed acute BAO vs a control group of suspected acute stroke patients without BAO. Two EM attending physicians, one third-year EM resident, and one medical student performed qualitative and quantitative assessments for the presence of a HDBA on axial NCCT images. Our primary outcome measures were sensitivity and specificity for BAO. Our secondary outcomes were inter-rater and intra-rater reliability of the qualitative and quantitative assessments.Results: We included 60 BAO and 65 control patients in our analysis. Qualitative assessment of the hyperdense basilar artery sign was poorly sensitive (54%–72%) and specific (55%–89%). Quantitative measurement improved the specificity of hyperdense basilar artery assessment for diagnosing BAO, with a threshold of 61.0–63.8 Hounsfield units demonstrating relatively high specificity of 85%–94%. There was moderate inter-rater agreement for the qualitative assessment of HDBA (Fleiss’ kappa statistic 0.508, 95% confidence interval: 0.435–0.581). Agreement improved for quantitative assessments, but still fell in the moderate range (Shrout-Fleiss intraclass correlation coefficient: 0.635). Intra-rater reliability for the quantitative assessments of the two attending physician reviewers demonstrated substantial consistency.Conclusion: Our results highlight the importance of carefully examining basilar artery density when interpreting the NCCT of patients with altered consciousness or other signs and symptoms concerning for an acute basilar artery occlusion. If the Hounsfield unit density of the basilar artery exceeds 61 Hounsfield units, BAO should be highly suspected

A New Mutual Information based Similarity Measure for Medical Image Registration

Medical image registration (IR) is the systematic process of aligning spate images, often involving different modalities with common reference framework, so complementary information can be combined and compared. This paper presents a new similarity measure which uses Expectation Maximization for Principal Component Analysis allied with mutual information (EMPCA-MI) for medical IR. The new measure has been analysed on multimodal, three band magnetic resonance images (MRI) T1, T2 and PD weighted, in the presence of both intensity non-uniformities (INU) and noise. Both quantitative and qualitative experimental results clearly demonstrate both improved robustness and lower computational complexity of the new EMPCA-MI paradigm compared with existing MI-based similarity measures, for various MRI test datasets

CT Enhancement and 3D Texture Analysis of Pancreatic Neuroendocrine Neoplasms

To evaluate pancreatic neuroendocrine neoplasms (panNENs) grade prediction by means of qualitative and quantitative CT evaluation, and 3D CT-texture analysis. Patients with histopathologically-proven panNEN, availability of Ki67% values and pre-treatment CT were included. CT images were retrospectively reviewed, and qualitative and quantitative images analysis were done; for quantitative analysis four enhancement-ratios and three permeability-ratios were created. 3D CT-texture imaging analysis was done (Mean Value; Variance; Skewness; Kurtosis; Entropy). Subsequently, these features were compared among the three grading (G) groups. 304 patients affected by panNENs were considered, and 100 patients were included. At qualitative evaluation, frequency of irregular margins was significantly different between tumor G groups. At quantitative evaluation, for all ratios, comparisons resulted statistical significant different between G1 and G3 groups and between G2 and G3 groups. At 3D CT-texture analysis, Kurtosis resulted statistical significant different among three G groups and Entropy resulted statistical significant different between G1 and G3 and between G2 and G3 groups. Quantitative CT evaluation of panNENs can predict tumor grade, discerning G1 from G3 and G2 from G3 tumors. CT-texture analysis can predict panNENs tumor grade, distinguishing G1 from G3 and G2 from G3, and G1 from G2 tumors

Optimally Stabilized PET Image Denoising Using Trilateral Filtering

Low-resolution and signal-dependent noise distribution in positron emission tomography (PET) images makes denoising process an inevitable step prior to qualitative and quantitative image analysis tasks. Conventional PET denoising methods either over-smooth small-sized structures due to resolution limitation or make incorrect assumptions about the noise characteristics. Therefore, clinically important quantitative information may be corrupted. To address these challenges, we introduced a novel approach to remove signal-dependent noise in the PET images where the noise distribution was considered as Poisson-Gaussian mixed. Meanwhile, the generalized Anscombe's transformation (GAT) was used to stabilize varying nature of the PET noise. Other than noise stabilization, it is also desirable for the noise removal filter to preserve the boundaries of the structures while smoothing the noisy regions. Indeed, it is important to avoid significant loss of quantitative information such as standard uptake value (SUV)-based metrics as well as metabolic lesion volume. To satisfy all these properties, we extended bilateral filtering method into trilateral filtering through multiscaling and optimal Gaussianization process. The proposed method was tested on more than 50 PET-CT images from various patients having different cancers and achieved the superior performance compared to the widely used denoising techniques in the literature.Comment: 8 pages, 3 figures; to appear in the Lecture Notes in Computer Science (MICCAI 2014

Understanding deep features with computer-generated imagery

We introduce an approach for analyzing the variation of features generated by convolutional neural networks (CNNs) with respect to scene factors that occur in natural images. Such factors may include object style, 3D viewpoint, color, and scene lighting configuration. Our approach analyzes CNN feature responses corresponding to different scene factors by controlling for them via rendering using a large database of 3D CAD models. The rendered images are presented to a trained CNN and responses for different layers are studied with respect to the input scene factors. We perform a decomposition of the responses based on knowledge of the input scene factors and analyze the resulting components. In particular, we quantify their relative importance in the CNN responses and visualize them using principal component analysis. We show qualitative and quantitative results of our study on three CNNs trained on large image datasets: AlexNet, Places, and Oxford VGG. We observe important differences across the networks and CNN layers for different scene factors and object categories. Finally, we demonstrate that our analysis based on computer-generated imagery translates to the network representation of natural images

Histomorphometric, fractal and lacunarity comparative analysis of sheep (Ovis aries), goat (Capra hircus) and roe deer (Capreolus capreolus) compact bone samples

Quantitative and qualitative studies dealing with histomorphometry of the bonetissue play a new role in modern legal medicine/forensic medicine and archaeozoologynowadays. This study deals with the differences found in case of humerusand metapodial bones of recent sheep (Ovis aries), goat (Capra hircus) and roedeer (Capreolus capreolus) specimens, both from a qualitative point of view, butmainly from a quantitative perspective. A novel perspective given by the fractalanalysis performed on the digital histological images is approached. This studyshows that the qualitative assessment may not be a reliable one due to theclose resemblance of the structures. From the quantitative perspective (severalmeasurements performed on osteonal units and statistical processing of data),some of the elements measured show significant differences among 3 species(the primary osteonal diameter, etc.). The fractal analysis and the lacunarity ofthe images show a great deal of potential, proving that this type of analysis canbe of great help in the separation of the material from this perspective

Measurement of Galactic Logarithmic Spiral Arm Pitch Angle Using Two-Dimensional Fast Fourier Transform Decomposition

A logarithmic spiral is a prominent feature appearing in a majority of observed galaxies. This feature has long been associated with the traditional Hubble classification scheme, but historical quotes of pitch angle of spiral galaxies have been almost exclusively qualitative. We have developed a methodology, utilizing two-dimensional fast Fourier transformations of images of spiral galaxies, in order to isolate and measure the pitch angles of their spiral arms. Our technique provides a quantitative way to measure this morphological feature. This will allow comparison of spiral galaxy pitch angle to other galactic parameters and test spiral arm genesis theories. In this work, we detail our image processing and analysis of spiral galaxy images and discuss the robustness of our analysis techniques.Comment: 23 pages, 22 figures, and 3 Tables; published in ApJS 199, 33 http://iopscience.iop.org/0067-0049/199/2/33/; software available for download at http://dafix.uark.edu/~ages/downloads.html and http://astro.host.ualr.edu/2DFFT

Quantitative analysis of dipyridamole-thallium images for the detection of coronary artery disease

To determine if the detection of coronary artery disease by dipyridamole-thallium imaging is improved by 1) quantitative versus qualitative analysis, and 2) combining quantitative variables, 80 patients with chest pain (53 with and 27 without coronary artery disease) who underwent cardiac catheterization were studied. Segmental thallium initial uptake, linear clearance, mono-exponential clearance and redistribution were measured from early, intermediate and delayed images acquired in three projections. Normal values were determined from 13 other clinically normal subjects.When five segments per view were used for quantitative analysis, sensitivity and specificity were 87 and 63%, respectively, for uptake, 77 and 67% for linear clearance, 60 and 60% for monoexponential clearance and 62 and 56% for redistribution. Of the four variables, uptake and linear clearance were the most sensitive (p < 0.01) and specificity did not differ significantly. Using three segments per view, the specificity of uptake increased (p < 0.05) to 78% without a significant change in sensitivity (85%). With this approach, sensitivity and specificity did not differ from those of qualitative analysis (85 and 78%, respectively).Stepwise logistic regression analysis demonstrated that the best quantitative thallium correlate of the presence of coronary artery disease was a combination variable of “either abnormal uptake or abnormal linear clearance, or both.” Using five segments per view, the model's specificity (85%) was greater than that of uptake alone (p < 0.02), with similar sensitivity (92%). Using three segments per view, the model's specificity (93%) was greater than that of uptake alone (p < 0.05) and of qualitative analysis (p < 0.05), with similar sensitivity (85%). Compared with qualitative analysis, the diagnostic accuracy of the model was greater using either five segments (90 versus 82%, p < 0.01) or three segments (88 versus 82%, p < 0.05) per view.Quantitative analysis of dipyridamole-thallium images using single individual variables provides results comparable with those of qualitative analysis and this can be further optimized when a combination of quantitative variables is used