| 1. |
- Candefjord, Stefan, et al.
(författare)
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Evaluating the use of a Raman fiberoptic probe in conjunction with a resonance sensor for measuring porcine tissue in vitro
- 2009
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Ingår i: IFMBE Proceedings of the World Congress on Medical Physics and Biomedical Engineering. - Heidelberg : Springer. ; , s. 414-417, s. 414-417
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Konferensbidrag (refereegranskat)abstract
- Prostate cancer is the most common form of cancer and is the third leading cause of cancer-related death in European men. There is a need for new methods that can accurately localize and diagnose prostate cancer. In this study a new approach is presented: a combination of resonance sensor technology and Raman spectroscopy. Both methods have shown promising results for prostate cancer detection in vitro. The aim of this study was to evaluate the combined information from measurements with a Raman fiberoptic probe and a resonance sensor system. Pork belly tissue was used as a model system. A three-dimensional translation table was equipped with an in-house developed software, allowing measurements to be performed at the same point using two separate instruments. The Raman data was analyzed using principal component analysis and hierarchical clustering analysis. The spectra were divided into 5 distinct groups. The mean stiffness of each group was calculated from the resonance sensor measurements. One of the groups differed significantly (p < 0.05) from the others. A regression analysis, with the stiffness parameter as response variable and the principal component scores of the Raman data as the predictor variables, explained 67% of the total variability. The use of a smaller resonance sensor tip would probably increase the degree of correlation. In conclusion, Raman spectroscopy provides additional discriminatory power to the resonance sensor.
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| 2. |
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| 3. |
- Jalkanen, Ville, 1978-, et al.
(författare)
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Explanatory models for a tactile resonance sensor system-elastic and density-related variations of prostate tissue in vitro
- 2008
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Ingår i: Physiological Measurement. - Bristol : IOP Publ. Ltd. - 0967-3334 .- 1361-6579. ; 29:7, s. 729-745
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Tidskriftsartikel (refereegranskat)abstract
- Tactile sensors based on piezoelectric resonance have been adopted for medical applications. The sensor consists of an oscillating piezoelectric sensor–circuit system, and a change in resonance frequency is observed when the sensor tip contacts a measured object such as tissue. The frequency change at a constant applied force or mass load is used as a stiffness-sensitive parameter in many applications. Differential relations between force and frequency have also been used for monitoring intraocular pressure and stiffness variations in prostate tissue in vitro. The aim of this study was to relate the frequency change (Δf), measured force (F) and the material properties, density and elasticity to an explanatory model for the resonance sensor measurement principle and thereby to give explanatory models for the stiffness parameters used previously. Simulations of theoretical equations were performed to investigate the relation between frequency change and contact impedance. Measurements with a resonance sensor system on prostate tissue in vitro were used for experimental validation of the theory. Tissue content was quantified with a microscopic-based morphometrical method. Simulation results showed that the frequency change was dependent upon density (ρ) and contact area (S) according to Δf ∝ ρS3/2. The experiments followed the simulated theory at small impression depths. The measured contact force followed a theoretical model with the dependence of the elastic modulus (E) and contact area, F ∝ ES3/2. Measured density variations related to histological variations were statistically weak or non-significant. Elastic variations were statistically significant with contributions from stroma and cancer relative to normal glandular tissue. The theoretical models of frequency change and force were related through the contact area, and a material-dependent explanatory model was found as Δf ∝ ρE−1F. It explains the measurement principle and the previously established stiffness parameters from the material properties point of view.
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| 4. |
- Jalkanen, Ville, 1978-, et al.
(författare)
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Instrument towards faster diagnosis and treatment of prostate cancer : Resonance sensor stiffness measurements on human prostate tissue in vitro
- 2009
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Ingår i: IFMBE Proceedings of the World Congress on Medical Physics and Biomedical Engineering. - Heidelberg : Springer. ; , s. 145-148, s. 145-148
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Konferensbidrag (refereegranskat)abstract
- Prostate cancer is the most common cancer among men and the methods used to detect and diagnose prostate cancer are not sufficiently accurate. Radical prostatectomy is a surgical treatment of prostate cancer where the whole prostate is removed from the patient. Prostate tissue stiffness can be measured with a stiffness sensitive resonance sensor. The aim of this study was to measure the stiffness on the anterior and posterior side of fresh human prostate tissue in vitro and compare these two groups with each other and relate the findings with the prostate tissue histology. In a prostate tissue slice with mostly normal healthy tissue, the anterior side was significantly harder (p-value < 0.05) as expected. In a prostate tissue slice with areas of cancer tumors, no difference was found between the anterior and posterior sides. However, large stiffness variations were found within groups with measurements points on cancer tissue (coefficient of variation, CV = 42 and 85%), as opposed to groups without cancer tissue (CV = 27 and 28%). The large stiffness variations could be used as a sign for the presence of cancer. The results are promising for the development of an instrument and method for faster diagnosis on radical prostatectomy samples.
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| 5. |
- Jalkanen, Ville, 1978-, et al.
(författare)
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Prostate tissue stiffness as measured with a resonance sensor system : a study on silicone and human prostate tissue in vitro.
- 2006
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Ingår i: Medical and Biological Engineering and Computing. - : Springer Science and Business Media LLC. - 0140-0118 .- 1741-0444. ; 44:7, s. 593-603
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Tidskriftsartikel (refereegranskat)abstract
- Prostate cancer is the most common form of cancer in men in Europe and in the USA. Some prostate tumours are stiffer than the surrounding normal tissue, and it could therefore be of interest to measure prostate tissue stiffness. Resonance sensor technology based on piezoelectric resonance detects variations in tissue stiffness due to a change in the resonance frequency. An impression-controlled resonance sensor system was used to detect stiffness in silicone rubber and in human prostate tissue in vitro using two parameters, both combinations of frequency change and force. Variations in silicone rubber stiffness due to the mixing ratio of the two components could be detected (p<0.05) using both parameters. Measurements on prostate tissue showed that there existed a statistically significant (MANOVA test, p<0.001) reproducible difference between tumour tissue (n=13) and normal healthy tissue (n=98) when studying a multivariate parameter set. Both the tumour tissue and normal tissue groups had variations within them, which were assumed to be related to differences in tissue composition. Other sources of error could be uneven surfaces and different levels of dehydration for the prostates. Our results indicated that the resonance sensor could be used to detect stiffness variations in silicone and in human prostate tissue in vitro. This is promising for the development of a future diagnostic tool for prostate cancer.
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| 6. |
- Jalkanen, Ville, 1978-, et al.
(författare)
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Resonance sensor measurements of stiffness variations in prostate tissue in vitro : a weighted tissue proportion model
- 2006
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Ingår i: Physiological Measurement. - : IOP Publishing. - 0967-3334 .- 1361-6579. ; 27:12, s. 1373-86
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Tidskriftsartikel (refereegranskat)abstract
- Prostate cancer is the most common type of cancer in men in Europe and the US. The methods to detect prostate cancer are still precarious and new techniques are needed. A piezoelectric transducer element in a feedback system is set to vibrate with its resonance frequency. When the sensor element contacts an object a change in the resonance frequency is observed, and this feature has been utilized in sensor systems to describe physical properties of different objects. For medical applications it has been used to measure stiffness variations due to various patho-physiological conditions. In this study the sensor's ability to measure the stiffness of prostate tissue, from two excised prostatectomy specimens in vitro, was analysed. The specimens were also subjected to morphometric measurements, and the sensor parameter was compared with the morphology of the tissue with linear regression. In the probe impression interval 0.5-1.7 mm, the maximum R(2) > or = 0.60 (p < 0.05, n = 75). An increase in the proportion of prostate stones (corpora amylacea), stroma, or cancer in relation to healthy glandular tissue increased the measured stiffness. Cancer and stroma had the greatest effect on the measured stiffness. The deeper the sensor was pressed, the greater, i.e., deeper, volume it sensed. Tissue sections deeper in the tissue were assigned a lower mathematical weighting than sections closer to the sensor probe. It is concluded that cancer increases the measured stiffness as compared with healthy glandular tissue, but areas with predominantly stroma or many stones could be more difficult to differ from cancer.
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| 7. |
- Lindberg, Peter L, et al.
(författare)
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An image analysis method for prostate tissue classification : preliminary validation with resonance sensor data
- 2009
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Ingår i: Journal of Medical Engineering & Technology. - : Informa healthcare. - 0309-1902 .- 1464-522X. ; 33:1, s. 18-24
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Tidskriftsartikel (refereegranskat)abstract
- Resonance sensor systems have been shown to be able to distinguish between cancerous and normal prostate tissue, in vitro. The aim of this study was to improve the accuracy of the tissue determination, to simplify the tissue classification process with computerized morphometrical analysis, to decrease the risk of human errors, and to reduce the processing time. In this article we present our newly developed computerized classification method based on image analysis. In relation to earlier resonance sensor studies we increased the number of normal prostate tissue classes into stroma, epithelial tissue, lumen and stones. The linearity between the impression depth and tissue classes was calculated using multiple linear regression (R(2) = 0.68, n = 109, p < 0.001) and partial least squares (R(2) = 0.55, n = 109, p < 0.001). Thus it can be concluded that there existed a linear relationship between the impression depth and the tissue classes. The new image analysis method was easy to handle and decreased the classification time by 80%.
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| 8. |
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| 9. |
- Seoane, Fernando, 1976-, et al.
(författare)
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Electrical Bioimpedance Cerebral Monitoring : A Study of the Current Density Distribution and Impedance Sensitivity Maps on a 3D Realistic Head Model
- 2007
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Ingår i: Proceedings of the 3rd IEEE-EMBS International Conference on Neural Engineering. - : IEEE. - 1424407923 ; , s. 256 - 260
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Konferensbidrag (refereegranskat)abstract
- There have been several studies of the application of electrical bioimpedance technology for brain monitoring in the past years. They have targeted a variety of events and injuries e.g. epilepsy or stroke. The current density distribution and the voltage lead field associated with an impedance measurement setup is of critical importance for the proper analysis of any dynamics in the impedance measurement or for an accurate reconstruction of an EIT image, specially a dynamic type. In this work for the first time, the current density distribution is calculated in a human head with anatomical accuracy and resolution down to 1 mm, containing up to 24 tissues and considering the frequency dependency of the conductivity of each tissue. The obtained current densities and the subsequent sensitivity maps are analyzed with a special focus on the dependency of the electrode arrangement and also the measurement frequency. The obtained results provide us with interesting and relevant information to consider in the design of any tool for electrical bioimpedance cerebral monitoring.
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| 10. |
- Löfhede, Johan, et al.
(författare)
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Comparing a Supervised and an Unsupervised Classification Method for Burst Detection in Neonatal EEG
- 2008
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Ingår i: Proceedings of Engineering in Medicine and Biology Society, EMBS 2008. 30th Annual International Conference of the IEEE, 20-24 August, 2008. - : IEEE. - 1557-170X. - 9781424418145 ; , s. 3836-3839
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Konferensbidrag (refereegranskat)abstract
- Hidden Markov Models (HMM) and Support Vector Machines (SVM) using unsupervised and supervised training, respectively, were compared with respect to their ability to correctly classify burst and suppression in neonatal EEG. Each classifier was fed five feature signals extracted from EEG signals from six full term infants who had suffered from perinatal asphyxia. Visual inspection of the EEG by an experienced electroencephalographer was used as the gold standard when training the SVM, and for evaluating the performance of both methods. The results are presented as receiver operating characteristic (ROC) curves and quantified by the area under the curve (AUC). Our study show that the SVM and the HMM exhibit similar performance, despite their fundamental differences.
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