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A technical note on variable inter-frame interval as a cause of non-physiological experimental artefacts in ultrasound.

ABSTRACT: Ultrasound (US) imaging is a well-recognized technique for the study of static tissues but its suitability for studying tissue dynamics depends upon accurate frame time information, which may not always be available to users. Here we present methods to quantify the inter-frame interval (IFI) variability, and evaluate different procedures for collecting temporal information from two US-imaging devices. The devices tested exhibited variable IFIs that could only be confirmed by direct measures of timing signals, available by means of electrical signals (triggers) and/or temporal information contained in the software used for the US data collection. Interpolating frame-by-frame measures of dynamic changes within image sequences using individual IFI values provided improved synchronization between measures of skeletal muscle movement and activation; validating US as a valuable technique for the study of musculoskeletal tissue dynamics, when correctly implemented.

SUBMITTER: Miguez D

PROVIDER: S-EPMC5451835 | biostudies-literature | 2017 May

REPOSITORIES: biostudies-literature

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A technical note on variable inter-frame interval as a cause of non-physiological experimental artefacts in ultrasound.

Miguez D D Hodson-Tole E F EF Loram I I Harding P J PJ

Royal Society open science 20170531 5

Ultrasound (US) imaging is a well-recognized technique for the study of static tissues but its suitability for studying tissue dynamics depends upon accurate frame time information, which may not always be available to users. Here we present methods to quantify the inter-frame interval (IFI) variability, and evaluate different procedures for collecting temporal information from two US-imaging devices. The devices tested exhibited variable IFIs that could only be confirmed by direct measures of t ...[more]

PMID: 28573034

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Project description:PURPOSE:Radiomics is the process to automate tumor feature extraction from medical images. This has shown potential for quantifying the tumor phenotype and predicting treatment response. The three major challenges of radiomics research and clinical adoption are: (a) lack of standardized methodology for radiomics analyses, (b) lack of a universal lexicon to denote features that are semantically equivalent, and (c) lists of feature values alone do not sufficiently capture the details of feature extraction that might nonetheless strongly affect feature values (e.g. image normalization or interpolation parameters). These barriers hamper multicenter validation studies applying subtly different imaging protocols, preprocessing steps and radiomics software. We propose an open-source ontology-guided radiomics analysis workflow (O-RAW) to address the above challenges in the following manner: (a) distributing a free and open-source software package for radiomics analysis, (b) deploying a standard lexicon to uniquely describe features in common usage and (c) provide methods to publish radiomic features as a semantically interoperable data graph object complying to FAIR (findable accessible interoperable reusable) data principles. METHODS:O-RAW was developed in Python, and has three major modules using open-source component libraries (PyRadiomics Extension and PyRadiomics). First, PyRadiomics Extension takes standard DICOM-RT (Radiotherapy) input objects (i.e. a DICOM series and an RTSTRUCT file) and parses them as arrays of voxel intensities and a binary mask corresponding to a volume of interest (VOI). Next, these arrays are passed into PyRadiomics, which performs the feature extraction procedure and returns a Python dictionary object. Lastly, PyRadiomics Extension parses this dictionary as a W3C-compliant Semantic Web "triple store" (i.e., list of subject-predicate-object statements) with relevant semantic meta-labels drawn from the radiation oncology ontology and radiomics ontology. The output can be published on an SPARQL endpoint, and can be remotely examined via SPARQL queries or to a comma separated file for further analysis. RESULTS:We showed that O-RAW executed efficiently on four datasets with different modalities, RIDER (CT), MMD (CT), CROSS (PET) and THUNDER (MR). The test was performed on an HP laptop running Windows 7 operating system and 8GB RAM on which we noted execution time including DICOM images and associated RTSTRUCT matching, binary mask conversion of a single VOI, batch-processing of feature extraction (105 basic features in PyRadiomics), and the conversion to an resource description framework (RDF) object. The results were (RIDER) 407.3, (MMD) 123.5, (CROSS) 513.2 and (THUNDER) 128.9 s for a single VOI. In addition, we demonstrated a use case, taking images from a public repository and publishing the radiomics results as FAIR data in this study on www.radiomics.org. Finally, we provided a practical instance to show how a user could query radiomic features and track the calculation details based on the RDF graph object created by O-RAW via a simple SPARQL query. CONCLUSIONS:We implemented O-RAW for FAIR radiomics analysis, and successfully published radiomic features from DICOM-RT objects as semantic web triples. Its practicability and flexibility can greatly increase the development of radiomics research and ease transfer to clinical practice.

Project description:PurposeDosimetric properties of the new microSilicon diode detector (60023) have been studied with focus on application in small-field dosimetry. The influences of the dimensions of the sensitive volume and the density of the epoxy layer surrounding the silicon chip of microSilicon have been quantified and compared to its predecessor (Diode E 60017) and the microDiamond (60019, all PTW-Freiburg, Germany).MethodsDose linearity has been studied in the range from 0.01 to 8.55 Gy and dose-per-pulse dependence from 0.13 to 0.86 mGy/pulse. The effective point of measurement (EPOM) was determined by comparing measured percentage depth dose curves with a reference curve (Roos chamber). Output ratios were measured for nominal field sizes from 0.5 × 0.5 cm2 to 4 × 4 cm2 . The corresponding small-field output correction factors, k, were derived with a plastic scintillation detector as reference. The lateral dose-response function, K(x), was determined using a slit beam geometry.ResultsMicroSilicon shows linear dose response (R2 = 1.000) in both low and high dose range up to 8.55 Gy with deviations of only up to 1% within the dose-per-pulse values investigated. The EPOM was found to lie (0.7 ± 0.2) mm below the front detector's surface. The derived k for microSilicon (0.960 at seff = 0.55 cm) is similar to that of microDiamond (0.956), while Diode E requires larger corrections (0.929). This improved behavior of microSilicon in small-fields is reflected in the slightly wider K(x) compared to Diode E. Furthermore, the amplitude of the negative values in K(x) at the borders of the sensitive volume has been reduced.ConclusionsCompared to its predecessor, microSilicon shows improved dosimetric behavior with higher sensitivity and smaller dose-per-pulse dependence. Profile measurements demonstrated that microSilicon causes less perturbation in off-axis measurements. It is especially suitable for the applications in small-field output factors and profile measurements.

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A technical note on variable inter-frame interval as a cause of non-physiological experimental artefacts in ultrasound.

Publications

A technical note on variable inter-frame interval as a cause of non-physiological experimental artefacts in ultrasound.

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