Breast cancer is a common and dreadful disease in women. Regular screening helps in its early detection. At present the most common methods of screening are by self examination and mammography. The surface temperature distribution of the breast can also provide some information on the presence of tumour. This distribution has a relation to the size and location of tumour and can be seen using thermography, where the infrared radiation emitted from the surface of the breast is recorded and a thermal pattern obtained. Thermography is a non-invasive and an inexpensive tool which could be used for early detection. In order to simulate the surface temperature distribution, a two-dimensional model of female breast with and without a carcinoma is considered. The breast is modelled with varying layer thickness close to the actual shape and numerically solved using finite element analysis. Temperature profiles are obtained for a normal breast and for a malignant one by varying the tumour size, location and the blood flow rates. The results show that the surface temperature for a malignant breast is higher than that of a normal one. In addition the size and location of the tumour do have an effect on the surface temperature distribution. It can also be seen that tumour of different sizes placed at the same location would yield the same maximum temperature depending on the blood perfusion rate.
An investigation into the interaction between an incident conical shock wave and a plane two-dimensional turbulent boundary layer is reported. The study has provided information on the interaction pattern and the surface ow eld under both attached and separated ow conditions. The existence of signi cant pressure gradients in the spanwise direction after the shock leads to strong cross ows. A horseshoe pattern of separation whose strength and size are reduced away from the plane of symmetry is identi ed. The interaction produces upstream in uence, which is curved as a result of decreasing in uence of the conical shock away from the plane of symmetry. Incipient separation is inferred, and it is shown that criteria for its existence are similar to other three-dimensional interactions.
Infrared (IR) Thermography is currently a supplementary technique for breast cancer diagnosis. There have been studies using IR thermography and numerical modeling in an attempt to detect tumor inside the breast. Most of these studies focused on either the “forward modeling” problem or only used idealized or population-averaged patients’ data, whereas identification of the tumor inside the breast based on the thermal pattern is an “inverse modeling” problem dependent on personalized information of the patient. Inverse modeling is based on the idea that the surface thermal pattern of the breast can be used to determine the tumor features based on physical and physiological principles. The current study aims to develop a well-validated inverse thermal modeling framework that could be used to determine the depth and size of tumor inside a breast based on personalized patients’ breast data, such as thermogram and 3D geometry using efficient design optimization techniques and Finite Element Modeling (FEM) to support the process. The numerical modeling was validated by the experiments, conducted using artificial breasts. Results show that although DIRECT Optimization method can be employed to find the depth and size of the tumor with good accuracy, the technique can be very time consuming. On the other hand, Response Surface Optimization method is also able to find the depth and size of the tumor with less accuracy but faster when compared with DIRECT Optimization. The last method tested, Nelder-Mead method, failed to detect the tumor. The study concludes that Response Surface Optimization method should be used first, and after the range of parameters are found, the DIRECT optimization method can be applied for more accurate results. However the GA method was found to be the only viable and efficient design optimization method for reverse modeling when blood perfusion was adopted in the breast model and many parameters were searched for with patient specific data input for breast tumor diagnosis.
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