Advances in active acoustic ranging

Recent advances in the processing and interpretation of sonic imaging surveys warrant a fresh look at the performance of active acoustic ranging for locating wellbores. The interpretation of results from sonic imaging surveys typically has been done in workflows similar to classic seismic interpretation, where the data are projected into a 2D plane and reflective features are picked. These sonic imaging workflows require significant time and expertise to execute. The reflected arrival events typically are obscured by higher amplitude borehole modes, and the migration workflow needs numerous critical parameter choices that require interpreting the raypath type and azimuth of the reflected arrivals. When used for acoustic ranging, additional challenges are present, particularly in situations where the logging tool rotates and the relative position of the target well changes with depth. This may occur when the logging or target well trajectories have a curved shape, since determining the direction and distance to the target well then requires careful interpretation of migration image amplitudes. We demonstrate how a newly developed automated approach to the interpretation of sonic imaging data helps improve accuracy and removes interpreter bias while simplifying the processing chain and reducing turnaround time. We compare our results to what has been obtained previously by using the same data set. We achieve a marked improvement in accuracy and consistency using this new technique.

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ESR white paper: blockchain and medical imaging

Insights into Imaging ◽

10.1186/s13244-021-01029-y ◽

2021 ◽

Vol 12 (1) ◽

Author(s):

◽

Elmar Kotter ◽

Luis Marti-Bonmati ◽

Adrian P. Brady ◽

Nandita M. Desouza

Keyword(s):

Medical Imaging ◽

Image Data ◽

Distributed Database ◽

Relevant Information ◽

White Paper ◽

Imaging Data ◽

Data Set ◽

Blockchain Technology ◽

Potential Applications ◽

Basic Technology

AbstractBlockchain can be thought of as a distributed database allowing tracing of the origin of data, and who has manipulated a given data set in the past. Medical applications of blockchain technology are emerging. Blockchain has many potential applications in medical imaging, typically making use of the tracking of radiological or clinical data. Clinical applications of blockchain technology include the documentation of the contribution of different “authors” including AI algorithms to multipart reports, the documentation of the use of AI algorithms towards the diagnosis, the possibility to enhance the accessibility of relevant information in electronic medical records, and a better control of users over their personal health records. Applications of blockchain in research include a better traceability of image data within clinical trials, a better traceability of the contributions of image and annotation data for the training of AI algorithms, thus enhancing privacy and fairness, and potentially make imaging data for AI available in larger quantities. Blockchain also allows for dynamic consenting and has the potential to empower patients and giving them a better control who has accessed their health data. There are also many potential applications of blockchain technology for administrative purposes, like keeping track of learning achievements or the surveillance of medical devices. This article gives a brief introduction in the basic technology and terminology of blockchain technology and concentrates on the potential applications of blockchain in medical imaging.

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Generation of a Complete Profile for Porosity Log While Drilling Complex Lithology by Employing the Artificial Intelligence

10.2118/208642-ms ◽

2021 ◽

Author(s):

Ahmed Al-Sabaa ◽

Hany Gamal ◽

Salaheldin Elkatatny

Keyword(s):

Artificial Intelligence ◽

Prediction Model ◽

Real Time ◽

Storage Capacity ◽

Data Set ◽

Drilling Parameters ◽

Unseen Data ◽

Rock Porosity ◽

Data Points ◽

Logging Tool

Abstract The formation porosity of drilled rock is an important parameter that determines the formation storage capacity. The common industrial technique for rock porosity acquisition is through the downhole logging tool. Usually logging while drilling, or wireline porosity logging provides a complete porosity log for the section of interest, however, the operational constraints for the logging tool might preclude the logging job, in addition to the job cost. The objective of this study is to provide an intelligent prediction model to predict the porosity from the drilling parameters. Artificial neural network (ANN) is a tool of artificial intelligence (AI) and it was employed in this study to build the porosity prediction model based on the drilling parameters as the weight on bit (WOB), drill string rotating-speed (RS), drilling torque (T), stand-pipe pressure (SPP), mud pumping rate (Q). The novel contribution of this study is to provide a rock porosity model for complex lithology formations using drilling parameters in real-time. The model was built using 2,700 data points from well (A) with 74:26 training to testing ratio. Many sensitivity analyses were performed to optimize the ANN model. The model was validated using unseen data set (1,000 data points) of Well (B), which is located in the same field and drilled across the same complex lithology. The results showed the high performance for the model either for training and testing or validation processes. The overall accuracy for the model was determined in terms of correlation coefficient (R) and average absolute percentage error (AAPE). Overall, R was higher than 0.91 and AAPE was less than 6.1 % for the model building and validation. Predicting the rock porosity while drilling in real-time will save the logging cost, and besides, will provide a guide for the formation storage capacity and interpretation analysis.

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Inferring health conditions from fMRI-graph data

10.31219/osf.io/r2huz ◽

2018 ◽

Author(s):

PierGianLuca Porta Mana ◽

Claudia Bachmann ◽

Abigail Morrison

Keyword(s):

Binary Classification ◽

Disease Diagnosis ◽

Health Condition ◽

Automated Classification ◽

Imaging Data ◽

Data Set ◽

Sufficient Statistics ◽

Conjugate Priors ◽

Healthy Control ◽

Partial Exchangeability

Automated classification methods for disease diagnosis are currently in the limelight, especially for imaging data. Classification does not fully meet a clinician's needs, however: in order to combine the results of multiple tests and decide on a course of treatment, a clinician needs the likelihood of a given health condition rather than binary classification yielded by such methods. We illustrate how likelihoods can be derived step by step from first principles and approximations, and how they can be assessed and selected, using fMRI data from a publicly available data set containing schizophrenic and healthy control subjects, as a working example. We start from the basic assumption of partial exchangeability, and then the notion of sufficient statistics and the "method of translation" (Edgeworth, 1898) combined with conjugate priors. This method can be used to construct a likelihood that can be used to compare different data-reduction algorithms. Despite the simplifications and possibly unrealistic assumptions used to illustrate the method, we obtain classification results comparable to previous, more realistic studies about schizophrenia, whilst yielding likelihoods that can naturally be combined with the results of other diagnostic tests.

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Hunting for brown dwarfs in the globular cluster M4: second epoch HST NIR observations

Monthly Notices of the Royal Astronomical Society ◽

10.1093/mnras/stz996 ◽

2019 ◽

Vol 486 (2) ◽

pp. 2254-2264 ◽

Cited By ~ 3

Author(s):

A Dieball ◽

L R Bedin ◽

C Knigge ◽

M Geffert ◽

R M Rich ◽

...

Keyword(s):

Globular Cluster ◽

White Dwarf ◽

Near Infrared ◽

Magnitude Estimate ◽

Cluster Member ◽

Imaging Data ◽

Brown Dwarf ◽

Data Set ◽

Bona Fide ◽

Low Mass

ABSTRACT We present an analysis of the second epoch Hubble Space TelescopeWide Field Camera 3 F110W near-infrared (NIR) imaging data of the globular cluster M 4. The new data set suggests that one of the previously suggested four brown dwarf candidates in this cluster is indeed a high-probability cluster member. The position of this object in the NIR colour–magnitude diagrams (CMDs) is in the white dwarf/brown dwarf area. The source is too faint to be a low-mass main-sequence (MS) star, but, according to theoretical considerations, also most likely somewhat too bright to be a bona-fide brown dwarf. Since we know that the source is a cluster member, we determined a new optical magnitude estimate at the position the source should have in the optical image. This new estimate places the source closer to the white dwarf sequence in the optical–NIR CMD and suggests that it might be a very cool (Teff ≤ 4500 K) white dwarf at the bottom of the white dwarf cooling sequence in M 4, or a white dwarf/brown dwarf binary. We cannot entirely exclude the possibility that the source is a very massive, bright brown dwarf, or a very low-mass MS star, however, we conclude that we still have not convincingly detected a brown dwarf in a globular cluster, but we expect to be very close to the start of the brown dwarf cooling sequence in this cluster. We also note that the MS ends at F110W ≈ 22.5 mag in the proper-motion cleaned CMDs, where completeness is still high.

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Computational framework for detection of subtypes of neuropsychiatric disorders based on DTI-derived anatomical connectivity

The Neuroradiology Journal ◽

10.1177/1971400920950694 ◽

2020 ◽

Vol 33 (5) ◽

pp. 393-399

Author(s):

Rong Chen ◽

Kyunghun Lee ◽

Edward H Herskovits

Keyword(s):

Diffusion Tensor ◽

Inferior Frontal Gyrus ◽

Anterior Cingulate ◽

Impulsive Behavior ◽

Cingulate Gyrus ◽

Imaging Data ◽

Anatomical Connectivity ◽

Computational Framework ◽

Data Set ◽

Superior Frontal Gyrus

Many brain disorders – such as Alzheimer’s disease, Parkinson’s disease, schizophrenia and autism – are heterogeneous, that is, they may have several subtypes. Traditionally, clinicians have identified subtypes, such as subtypes of psychosis, using clinical criteria. Neuroimaging has the potential to detect subtypes based on objective biomarker-based criteria; however, there are no studies that evaluate the application of combining unsupervised machine learning and anatomical connectivity analysis to accomplish this goal. We propose a computational framework to detect subtypes based on anatomical connectivity computed from diffusion tensor imaging data, in a data-driven and fully automated way. The proposed method exhibits excellent performance on simulated data. We also applied this approach to a real-world dataset: the Nathan Kline Institute data set. The Nathan Kline Institute study consists of 137 normal adult subjects (mean age 41 years (standard deviation 18), male/female 85/52). We examined the association between detected subtypes and the impulsive behavior scale. We found that a subtype characterized by lower connectivity scores was associated with a higher positive urgency score; positive urgency is a vulnerability marker for drug addiction. The top-ranked connections characterizing subtypes involve several brain regions, including the anterior cingulate gyrus, median cingulate gyrus, thalamus, superior frontal gyrus (medial), middle frontal gyrus (orbital part), inferior frontal gyrus (triangular part), superior frontal gyrus, precuneus and putamen. The proposed framework is extendable, and can be used to detect subtypes from other features, including clinical and genomic biomarkers.

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Ground-truth uncertainty-aware metrics for machine learning applications on seismic image interpretation: Application to faults and horizon extraction

The Leading Edge ◽

10.1190/tle39100734.1 ◽

2020 ◽

Vol 39 (10) ◽

pp. 734-741

Author(s):

Sébastien Guillon ◽

Frédéric Joncour ◽

Pierre-Emmanuel Barrallon ◽

Laurent Castanié

Keyword(s):

Deep Learning ◽

Image Interpretation ◽

Ground Truth ◽

Learning Model ◽

Seismic Interpretation ◽

Data Set ◽

Seismic Image ◽

Machine Learning Applications ◽

Deep Learning Model

We propose new metrics to measure the performance of a deep learning model applied to seismic interpretation tasks such as fault and horizon extraction. Faults and horizons are thin geologic boundaries (1 pixel thick on the image) for which a small prediction error could lead to inappropriately large variations in common metrics (precision, recall, and intersection over union). Through two examples, we show how classical metrics could fail to indicate the true quality of fault or horizon extraction. Measuring the accuracy of reconstruction of thin objects or boundaries requires introducing a tolerance distance between ground truth and prediction images to manage the uncertainties inherent in their delineation. We therefore adapt our metrics by introducing a tolerance function and illustrate their ability to manage uncertainties in seismic interpretation. We compare classical and new metrics through different examples and demonstrate the robustness of our metrics. Finally, we show on a 3D West African data set how our metrics are used to tune an optimal deep learning model.

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Three-dimensional echocardiography

10.1093/med/9780198726012.003.0007 ◽

2016 ◽

Author(s):

Luigi P. Badano ◽

Roberto M. Lang ◽

Alexandra Goncalves

Keyword(s):

Three Dimensional ◽

Digital Processing ◽

Surface Rendering ◽

Imaging Data ◽

Data Set ◽

Clinical Scenarios ◽

Dimensional Echocardiography ◽

Three Dimensional Echocardiography ◽

Two Dimensional Echocardiography ◽

Echocardiographic Technique

The advent of fully-sampled matrix array transthoracic transducers has enabled advanced digital processing and improved image formation algorithms and brought three-dimensional echocardiography (3DE) technology into clinical practice. Currently, 3DE is recognized as an important echocardiographic technique, demonstrated to be superior to two-dimensional echocardiography in various clinical scenarios. This chapter focuses on the technology of 3DE matrix transducers, physics of 3D imaging, data set acquisition (multiplane, real-time, full-volume, zoom, and colour), and display (volume rendering, surface rendering and multislice) modalities. The chapter also addresses the issues of training in 3DE, and main clinical indications and reporting of transthoracic and transoesophageal 3DE.

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Data segmentation based on the local intrinsic dimension

Scientific Reports ◽

10.1038/s41598-020-72222-0 ◽

2020 ◽

Vol 10 (1) ◽

Author(s):

Michele Allegra ◽

Elena Facco ◽

Francesco Denti ◽

Alessandro Laio ◽

Antonietta Mira

Keyword(s):

High Dimensional Data ◽

Large Data ◽

Large Data Sets ◽

High Dimensional ◽

Data Sets ◽

Imaging Data ◽

Unsupervised Segmentation ◽

Real World Data ◽

Data Set ◽

Intrinsic Dimension

Abstract One of the founding paradigms of machine learning is that a small number of variables is often sufficient to describe high-dimensional data. The minimum number of variables required is called the intrinsic dimension (ID) of the data. Contrary to common intuition, there are cases where the ID varies within the same data set. This fact has been highlighted in technical discussions, but seldom exploited to analyze large data sets and obtain insight into their structure. Here we develop a robust approach to discriminate regions with different local IDs and segment the points accordingly. Our approach is computationally efficient and can be proficiently used even on large data sets. We find that many real-world data sets contain regions with widely heterogeneous dimensions. These regions host points differing in core properties: folded versus unfolded configurations in a protein molecular dynamics trajectory, active versus non-active regions in brain imaging data, and firms with different financial risk in company balance sheets. A simple topological feature, the local ID, is thus sufficient to achieve an unsupervised segmentation of high-dimensional data, complementary to the one given by clustering algorithms.

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A Study on Stereoscopic X-ray Imaging Data Set on the Accuracy of Real-Time Tumor Tracking in External Beam Radiotherapy

Technology in Cancer Research & Treatment ◽

10.1177/1533034616638803 ◽

2016 ◽

Vol 16 (2) ◽

pp. 167-177 ◽

Cited By ~ 2

Author(s):

Ahmad Esmaili Torshabi ◽

Leila Ghorbanzadeh

Keyword(s):

External Beam Radiotherapy ◽

Training Data ◽

Imaging Data ◽

Data Set ◽

X Ray ◽

Tumor Tracking ◽

X Ray Imaging ◽

Additional Imaging ◽

Data Points ◽

Imaging Dose

At external beam radiotherapy, stereoscopic X-ray imaging system is responsible as tumor motion information provider. This system takes X-ray images intermittently from tumor position (1) at pretreatment step to provide training data set for model construction and (2) during treatment to control the accuracy of correlation model performance. In this work, we investigated the effect of imaging data points provided by this system on treatment quality. Because some information is still lacking about (1) the number of imaging data points, (2) shooting time for capturing each data point, and also (3) additional imaging dose delivered by this system. These 3 issues were comprehensively assessed at (1) pretreatment step while training data set is gathered for prediction model construction and (2) during treatment while model is tested and reconstructed using new arrival data points. A group of real patients treated with CyberKnife Synchrony module was chosen in this work, and an adaptive neuro-fuzzy inference system was considered as consistent correlation model. Results show that a proper model can be constructed while the number of imaging data points is highly enough to represent a good pattern of breathing cycles. Moreover, a trade-off between the number of imaging data points and additional imaging dose is considered in this study. Since breathing phenomena are highly variable at different patients, the time for taking some of imaging data points is very important, while their absence at that critical time may yield wrong tumor tracking. In contrast, the sensitivity of another category of imaging data points is not high, while breathing is normal and in the control range. Therefore, an adaptive supervision on the implementation of stereoscopic X-ray imaging is proposed to intelligently accomplish shooting process, based on breathing motion variations.

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Wavefront sets analysis of limited aperture migration sections

Geophysics ◽

10.1190/1.1443095 ◽

1991 ◽

Vol 56 (6) ◽

pp. 778-784 ◽

Cited By ~ 4

Author(s):

P. M. Carrion ◽

H. K. Sato ◽

A. V. D. Buono

Keyword(s):

Relative Position ◽

Real Data ◽

Brazilian Coast ◽

Data Set ◽

Prestack Migration ◽

Normal Bundles

Although geophysical literature is quite rich in papers on migration, there are only a few papers that treat LAM (Limited Aperture Migration). Deterministic and stochastic criteria for the reconstruction of reflection interfaces in prestack migration were derived and demonstrated on synthetic and real data examples (a real data set was acquired offshore the Brazilian coast, courtesy of Petrobras). The reconstruction of reflection interfaces in LAM depends on the relative position of “normal bundles,” as defined here, and gradients to the reflection interfaces. The derived stochastic criterion relates the variance of “normal bundles” to the variance of slowness in the medium. These criteria will help the geophysicist to identify directly distorted regions caused by LAM and those regions that are correctly migrated.

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