cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.7517612
title: Three-dimensional geometric model of the middle segment of the thoracic spine based on graphical images for finite element analysis

description: Abstract Introduction: Biomedical studies involve complex anatomical structures, which require specific methodology to generate their geometric models. The middle segment of the thoracic spine (T5-T10) is the site of the highest incidence of vertebral deformity in adolescents. Traditionally, its geometries are derived from computed tomography or magnetic resonance imaging data. However, this approach may restrict certain studies. The study aimed to generate two 3D geometric model of the T5-T10 thoracic spine segment, obtained from graphical images, and to create mesh for finite element studies. Methods A 3D geometric model of T5-T10 was generated using two anatomical images of T6 vertebra (side and top). The geometric model was created in Autodesk® Maya® 3D 2013, and the mesh process in HiperMesh and MeshMixer (v11.0.544 Autodesk). Results The T5-T10 thoracic segment model is presented with its passive components, bones, intervertebral discs and flavum, intertransverse and supraspinous ligaments, in different views, as well as the volumetric mesh. Conclusion The 3D geometric model generated from graphical images is suitable for application in non-patient-specific finite element model studies or, with restrictions, in the use of computed tomography or magnetic resonance imaging. This model may be useful for biomechanical studies related to the middle thoracic spine, the most vulnerable site for vertebral deformations.
DOI: 10.6084/m9.figshare.7518923
title: Study on patient dosimetry and image quality in digital mammography

description: Abstract Introduction Digital mammography present many advantages in comparison to conventional mammography, such as high dynamic range and the post-processing of acquired images. One problem is that protocols may not be optimized, resulting in higher absorbed doses to patients. The objective of this work is to evaluate image quality and to estimate mean glandular doses (MGD) in patients submitted to mammography examinations with three digital systems and one screen-film system in Recife, Brazil. Methods To estimate the MGD, the parameters used to acquire images of 5475 patients, with ages between 40 and 64 years and compressed breasts between 2 and 9 cm, were registered. The MGD was calculated by multiplying the incident air kerma with conversion coefficients depending on the anode/filter, breast glandularity and half-value layer. The image quality evaluation of the digital systems was made using objective and subjective European criteria. Results The results showed MGDs in the range of 0.4-10.3 mGy and the higher values were observed with digital systems. It was also observed that in the digital systems the use of compression force is not adequate and the irradiation parameters are not optimized. The images failed to reproduce the pectoral muscle and the contrast-to-noise ratio was not adequate for one system, indicating the need to improve the patient’s positioning and the exposure parameters. Conclusion It can be concluded that the use of non-optimized irradiation parameters is causing the higher doses with digital systems, highlighting the insufficient compression force.
DOI: 10.6084/m9.figshare.7518923.v1
title: Study on patient dosimetry and image quality in digital mammography

description: Abstract Introduction Digital mammography present many advantages in comparison to conventional mammography, such as high dynamic range and the post-processing of acquired images. One problem is that protocols may not be optimized, resulting in higher absorbed doses to patients. The objective of this work is to evaluate image quality and to estimate mean glandular doses (MGD) in patients submitted to mammography examinations with three digital systems and one screen-film system in Recife, Brazil. Methods To estimate the MGD, the parameters used to acquire images of 5475 patients, with ages between 40 and 64 years and compressed breasts between 2 and 9 cm, were registered. The MGD was calculated by multiplying the incident air kerma with conversion coefficients depending on the anode/filter, breast glandularity and half-value layer. The image quality evaluation of the digital systems was made using objective and subjective European criteria. Results The results showed MGDs in the range of 0.4-10.3 mGy and the higher values were observed with digital systems. It was also observed that in the digital systems the use of compression force is not adequate and the irradiation parameters are not optimized. The images failed to reproduce the pectoral muscle and the contrast-to-noise ratio was not adequate for one system, indicating the need to improve the patient’s positioning and the exposure parameters. Conclusion It can be concluded that the use of non-optimized irradiation parameters is causing the higher doses with digital systems, highlighting the insufficient compression force.
DOI: 10.6084/m9.figshare.7517366
title: Automated and observer based light field indicator edge evaluation in diagnostic X-ray equipment

description: Abstract Introduction To analyze edge detection and optical contrast calculation of light field-indicators used in X-ray via automated- and observer-based methods, and comparison with current standard approaches, which do not give exact definition for light field edge determination. Methods Automated light sensor array was used to measure the penumbra zone of the edge in the standard X-ray equipment, while trained and naïve human observers were asked to mark the light field edge according to their own determination. Different interpretations of the contrast were then calculated and compared. Results In contrast to automated measurements of edge definition and detection, measurements by human observers showed large inter-observer variation independent of their training with X-ray equipment. Different contrast calculations considering the different edge definitions gave very different contrast values. Conclusion As the main conclusion, we propose a more exact edge definition of the X-ray light field, corresponding well to the average human observer’s edge determination. The new edge definition method with automated systems would reduce human variability in edge determination. Such errors could potentially affect the approval of X-ray equipment, and also increase the radiation dose. The automated measurement based on human observers’ edge definition and the corresponding contrast calculation may lead to a more precise light field calibration, which enables reduced irradiation doses on radiology patients.
DOI: 10.6084/m9.figshare.7517366.v1
title: Automated and observer based light field indicator edge evaluation in diagnostic X-ray equipment

description: Abstract Introduction To analyze edge detection and optical contrast calculation of light field-indicators used in X-ray via automated- and observer-based methods, and comparison with current standard approaches, which do not give exact definition for light field edge determination. Methods Automated light sensor array was used to measure the penumbra zone of the edge in the standard X-ray equipment, while trained and naïve human observers were asked to mark the light field edge according to their own determination. Different interpretations of the contrast were then calculated and compared. Results In contrast to automated measurements of edge definition and detection, measurements by human observers showed large inter-observer variation independent of their training with X-ray equipment. Different contrast calculations considering the different edge definitions gave very different contrast values. Conclusion As the main conclusion, we propose a more exact edge definition of the X-ray light field, corresponding well to the average human observer’s edge determination. The new edge definition method with automated systems would reduce human variability in edge determination. Such errors could potentially affect the approval of X-ray equipment, and also increase the radiation dose. The automated measurement based on human observers’ edge definition and the corresponding contrast calculation may lead to a more precise light field calibration, which enables reduced irradiation doses on radiology patients.
DOI: 10.6084/m9.figshare.6944204.v1
title: Pattern recognition of abscesses and brain tumors through MR spectroscopy: Comparison of experimental conditions and radiological findings

description: Abstract Introduction The interpretation of brain tumors and abscesses MR spectra is complex and subjective. In clinical practice, different experimental conditions such as field strength or echo time (TE) reveal different metabolite information. Our study aims to show in which scenarios magnetic resonance spectroscopy can differentiate among brain tumors, normal tissue and abscesses using classification algorithms. Methods Pairwise classification between abscesses, brain tumor classes, and healthy subjects tissue spectra was performed, also the multiclass classification between meningiomas, grade I-II-III gliomas, and glioblastomas and metastases, in 1.5T short TE (n = 195), 1.5T long TE (n = 231) and 3.0T long TE (n = 59) point resolved spectroscopy setups, using LCModel metabolite concentration as input to classifiers. Results Areas under the curve of the Receiver Operating Characteristic above 0.9 were obtained for the classification between abscesses and all classes except glioblastomas, reaching 0.947 when classifying against metastases, grade I-II gliomas and glioblastomas (0.980), meningiomas and glioblastomas (0.956), grade I-II gliomas and metastases (0.989), meningiomas and metastases (0.990), and between healthy tissue and all other classes in both conditions except for anaplastic astrocytomas in short TE 1.5T setup. When the multiclass classification agrees with radiological diagnosis the accuracy reaches 96.8% for short TE and 98.9% for long TE. Conclusions The results in the three conditions were similar, highlighting comparable quality, robust quantification and good regularization and flexibility in either algorithm. Multiclass classification provides useful information to the radiologist. These findings show the potential of the development of decision support systems as well as tools for the accompaniment of treatments.
DOI: 10.6084/m9.figshare.6944204
title: Pattern recognition of abscesses and brain tumors through MR spectroscopy: Comparison of experimental conditions and radiological findings

description: Abstract Introduction The interpretation of brain tumors and abscesses MR spectra is complex and subjective. In clinical practice, different experimental conditions such as field strength or echo time (TE) reveal different metabolite information. Our study aims to show in which scenarios magnetic resonance spectroscopy can differentiate among brain tumors, normal tissue and abscesses using classification algorithms. Methods Pairwise classification between abscesses, brain tumor classes, and healthy subjects tissue spectra was performed, also the multiclass classification between meningiomas, grade I-II-III gliomas, and glioblastomas and metastases, in 1.5T short TE (n = 195), 1.5T long TE (n = 231) and 3.0T long TE (n = 59) point resolved spectroscopy setups, using LCModel metabolite concentration as input to classifiers. Results Areas under the curve of the Receiver Operating Characteristic above 0.9 were obtained for the classification between abscesses and all classes except glioblastomas, reaching 0.947 when classifying against metastases, grade I-II gliomas and glioblastomas (0.980), meningiomas and glioblastomas (0.956), grade I-II gliomas and metastases (0.989), meningiomas and metastases (0.990), and between healthy tissue and all other classes in both conditions except for anaplastic astrocytomas in short TE 1.5T setup. When the multiclass classification agrees with radiological diagnosis the accuracy reaches 96.8% for short TE and 98.9% for long TE. Conclusions The results in the three conditions were similar, highlighting comparable quality, robust quantification and good regularization and flexibility in either algorithm. Multiclass classification provides useful information to the radiologist. These findings show the potential of the development of decision support systems as well as tools for the accompaniment of treatments.
DOI: 10.6084/m9.figshare.7515878
title: Phantoms for diffusion-weighted imaging and diffusion tensor imaging quality control: a review and new perspectives

description: Abstract: Introduction Diffusion-weighted imaging (DWI) and diffusion tensor imaging (DTI) combine magnetic resonance imaging (MRI) techniques and diffusion measures. In DWI, the contrast is defined by microscopic motion of water protons. Nowadays, DWI has become important for early diagnostic of acute stroke. DTI images are calculated from DWI images acquired in at least six directions, which give information of diffusion directionality, making it possible to reconstruct axonal or muscle fiber images. Both techniques have been applied to study body structures in healthy and pathological conditions. Currently, it is known that these images and derived parameters are quite sensitive to factors related to acquisition and processing. Magnetic field inhomogeneity, susceptibility, chemical shift, radiofrequency (RF) interference, eddy currents and low signal-to-noise ratio (SNR) can have a more harmful effect in diffusion data than in T1- or T2-weighted image data. However, even today there are not reference phantoms and guidelines for DWI or DTI quality control (QC). Review Proposals for construction and use of DWI and DTI QC phantoms can be found in literature. DWI have been evaluated using containers filled by gel or liquid with tissue-like MRI properties, as well as using microfabricated devices. DTI acquisitions also have been checked with these devices or using natural or artificial fiber structures. The head phantom from American College of Radiology (ACR) is also pointed out as an alternative for DTI QC. This article brings a discussion about proposed DWI and DTI phantoms, challenges involved and future perspectives for standardization of DWI and DTI QC.
DOI: 10.6084/m9.figshare.7515878.v1
title: Phantoms for diffusion-weighted imaging and diffusion tensor imaging quality control: a review and new perspectives

description: Abstract: Introduction Diffusion-weighted imaging (DWI) and diffusion tensor imaging (DTI) combine magnetic resonance imaging (MRI) techniques and diffusion measures. In DWI, the contrast is defined by microscopic motion of water protons. Nowadays, DWI has become important for early diagnostic of acute stroke. DTI images are calculated from DWI images acquired in at least six directions, which give information of diffusion directionality, making it possible to reconstruct axonal or muscle fiber images. Both techniques have been applied to study body structures in healthy and pathological conditions. Currently, it is known that these images and derived parameters are quite sensitive to factors related to acquisition and processing. Magnetic field inhomogeneity, susceptibility, chemical shift, radiofrequency (RF) interference, eddy currents and low signal-to-noise ratio (SNR) can have a more harmful effect in diffusion data than in T1- or T2-weighted image data. However, even today there are not reference phantoms and guidelines for DWI or DTI quality control (QC). Review Proposals for construction and use of DWI and DTI QC phantoms can be found in literature. DWI have been evaluated using containers filled by gel or liquid with tissue-like MRI properties, as well as using microfabricated devices. DTI acquisitions also have been checked with these devices or using natural or artificial fiber structures. The head phantom from American College of Radiology (ACR) is also pointed out as an alternative for DTI QC. This article brings a discussion about proposed DWI and DTI phantoms, challenges involved and future perspectives for standardization of DWI and DTI QC.
DOI: 10.6084/m9.figshare.6943643
title: Wavelet multiresolution analysis and dyadic scalogram for detection of epileptiform paroxysms in electroencephalographic signals

description: Abstract Introduction Early detection of epilepsy by the review of large electroencephalographic (EEG) recordings is very stressful, time-consuming, and subjective for neurologists. Several automatic seizure detection systems have been proposed in the literature to solve this problem. Methods This study proposes two complementary wavelet-based approaches for detecting epileptiform paroxysms in EEG signals. First methodology applied the wavelet multiresolution analysis (MRA) to filter non-epileptiform activity in long-term EEG. Second methodology used the wavelet dyadic scalogram to analyze which scales were related to the epileptiform paroxysms. For tests, 65 wavelet functions were selected between daubechies, biorthogonal, symlets, reverse biorthogonal and coiflet wavelet families in order to evaluate their performance. Results For MRA, it was noted a better performance by using the db4 function, by reaching 48.30% of energy with 8 wavelet coefficients, 0.717658 of correlation and 36.799 of root mean square error (RMSE). For wavelet dyadic scalograms, were chosen bior3.9 and rbio1.5 functions, by reaching 77.98% of sensitivity, 94.08% of specificity, 87.87% of efficiency and 0.9613 of area under the curve (AUC value) by using bior3.9. Conclusion The presented approaches are highly complementary for a whole automatic seizure detection system by using the MRA as pre-processing stage to filter non-epileptiform activity, and wavelet dyadic scalogram for extracting desired features from filtered EEG signals.
DOI: 10.6084/m9.figshare.6943643.v1
title: Wavelet multiresolution analysis and dyadic scalogram for detection of epileptiform paroxysms in electroencephalographic signals

description: Abstract Introduction Early detection of epilepsy by the review of large electroencephalographic (EEG) recordings is very stressful, time-consuming, and subjective for neurologists. Several automatic seizure detection systems have been proposed in the literature to solve this problem. Methods This study proposes two complementary wavelet-based approaches for detecting epileptiform paroxysms in EEG signals. First methodology applied the wavelet multiresolution analysis (MRA) to filter non-epileptiform activity in long-term EEG. Second methodology used the wavelet dyadic scalogram to analyze which scales were related to the epileptiform paroxysms. For tests, 65 wavelet functions were selected between daubechies, biorthogonal, symlets, reverse biorthogonal and coiflet wavelet families in order to evaluate their performance. Results For MRA, it was noted a better performance by using the db4 function, by reaching 48.30% of energy with 8 wavelet coefficients, 0.717658 of correlation and 36.799 of root mean square error (RMSE). For wavelet dyadic scalograms, were chosen bior3.9 and rbio1.5 functions, by reaching 77.98% of sensitivity, 94.08% of specificity, 87.87% of efficiency and 0.9613 of area under the curve (AUC value) by using bior3.9. Conclusion The presented approaches are highly complementary for a whole automatic seizure detection system by using the MRA as pre-processing stage to filter non-epileptiform activity, and wavelet dyadic scalogram for extracting desired features from filtered EEG signals.
DOI: 10.6084/m9.figshare.6943376
title: Dexterous hand gestures recognition based on low-density sEMG signals for upper-limb forearm amputees

description: Abstract Introduction Intuitive prosthesis control is one of the most important challenges in order to reduce the user effort in learning how to use an artificial hand. This work presents the development of a novel method for pattern recognition of sEMG signals able to discriminate, in a very accurate way, dexterous hand and fingers movements using a reduced number of electrodes, which implies more confidence and usability for amputees. Methods The system was evaluated for ten forearm amputees and the results were compared with the performance of able-bodied subjects. Multiple sEMG features based on fractal analysis (detrended fluctuation analysis and Higuchi’s fractal dimension) combined with traditional magnitude-based features were analyzed. Genetic algorithms and sequential forward selection were used to select the best set of features. Support vector machine (SVM), K-nearest neighbors (KNN) and linear discriminant analysis (LDA) were analyzed to classify individual finger flexion, hand gestures and different grasps using four electrodes, performing contractions in a natural way to accomplish these tasks. Statistical significance was computed for all the methods using different set of features, for both groups of subjects (able-bodied and amputees). Results The results showed average accuracy up to 99.2% for able-bodied subjects and 98.94% for amputees using SVM, followed very closely by KNN. However, KNN also produces a good performance, as it has a lower computational complexity, which implies an advantage for real-time applications. Conclusion The results show that the method proposed is promising for accurately controlling dexterous prosthetic hands, providing more functionality and better acceptance for amputees.
DOI: 10.6084/m9.figshare.6943376.v1
title: Dexterous hand gestures recognition based on low-density sEMG signals for upper-limb forearm amputees

description: Abstract Introduction Intuitive prosthesis control is one of the most important challenges in order to reduce the user effort in learning how to use an artificial hand. This work presents the development of a novel method for pattern recognition of sEMG signals able to discriminate, in a very accurate way, dexterous hand and fingers movements using a reduced number of electrodes, which implies more confidence and usability for amputees. Methods The system was evaluated for ten forearm amputees and the results were compared with the performance of able-bodied subjects. Multiple sEMG features based on fractal analysis (detrended fluctuation analysis and Higuchi’s fractal dimension) combined with traditional magnitude-based features were analyzed. Genetic algorithms and sequential forward selection were used to select the best set of features. Support vector machine (SVM), K-nearest neighbors (KNN) and linear discriminant analysis (LDA) were analyzed to classify individual finger flexion, hand gestures and different grasps using four electrodes, performing contractions in a natural way to accomplish these tasks. Statistical significance was computed for all the methods using different set of features, for both groups of subjects (able-bodied and amputees). Results The results showed average accuracy up to 99.2% for able-bodied subjects and 98.94% for amputees using SVM, followed very closely by KNN. However, KNN also produces a good performance, as it has a lower computational complexity, which implies an advantage for real-time applications. Conclusion The results show that the method proposed is promising for accurately controlling dexterous prosthetic hands, providing more functionality and better acceptance for amputees.