Dataset Information

Technical Note: Characterization of the new microSilicon diode detector.

ABSTRACT:

Purpose

Dosimetric 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).

Methods

Dose 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.

Results

MicroSilicon 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 s_eff = 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.

Conclusions

Compared 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.

SUBMITTER: Schonfeld AB

PROVIDER: S-EPMC6852691 | biostudies-literature | 2019 Sep

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Technical Note: Characterization of the new microSilicon diode detector.

Schönfeld Ann-Britt AB Poppinga Daniela D Kranzer Rafael R De Wilde Rudy Leon RL Willborn Kay K Poppe Björn B Looe Hui Khee HK

Medical physics 20190731 9

<h4>Purpose</h4>Dosimetric 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).<h4>Methods</h4>Dose linearity has been studied in the range from 0.01 to 8.55 Gy a ...[more]

PMID: 31309594

Similar Datasets

Project description:Deep semen deposition, avoiding retrograde flow, lesions and stress, has proved to be very important in the success of sheep AI. The objective of the present study was to develop a new, suitable anti-retrograde flow device for sheep cervical AI (DARIO) that enables deep deposition of semen into the cervix without any modifications to the procedures currently used, and to compare the fertility, fecundity, and prolificacy rates between DARIO and a traditional catheter. Field tests were performed on 16 farms actively participating in the non-profit National Association of Rasa Aragonesa Breeders´ genetic selection scheme and where sheep management was similar. A total of 242 AI lots were considered, including 1,299 ewes; 126 lots (662 ewes) were inseminated using DARIO, and 116 lots (637 ewes) using a traditional commercially-available catheter (control group). Several factors affecting AI results were included in the model for mean comparison between DARIO and control groups (farm and ram as random factors; catheter, year and photoperiod as fixed effects; catheter × photoperiod interaction). The type of catheter had a significant effect on fertility ( < 0.01) and fecundity rates ( < 0.01) but no significant effect was detected on the prolificacy rate ( = 0.45). For fertility rate (percentage of ewes lambing after AI), means ± SE for DARIO and control groups were 59.44 ± 2.13% and 49.60 ± 2.48%, respectively; for fecundity rates, means ± SE for DARIO and control groups were 0.99 ± 0.04 and 0.82 ± 0.05 lambs/inseminated ewe, respectively, and, for prolificacy rates, means ± SE for DARIO and control groups were 1.68 ± 0.04 and 1.63 ± 0.04 lambs/ewe lambing, respectively. Fertility rate was greater in the decreasing photoperiod ( = 0.01). Significant effects were found for both year ( < 0.05) and farm ( < 0.01) on fertility, fecundity, and prolificacy rates. Neither ram nor catheter × photoperiod showed any significant effects on the variables investigated ( > 0.05). Overall, the use of DARIO instead of the traditional commercially-available catheter increased both fertility and fecundity rates; the marginal mean differences were 9.05 pregnant ewes per 100 inseminated and 0.15 lambs per inseminated ewe, respectively.

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.

Dataset Information

Technical Note: Characterization of the new microSilicon diode detector.

Purpose

Methods

Results

Conclusions

Publications

Technical Note: Characterization of the new microSilicon diode detector.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure