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Microwave tomography (MWT) is exploited for the detection of haemorrhagic stroke by using a nonlinear iterative imaging algorithm. An anatomically realistic two-dimensional (2D) head model is simulated using a finite difference time-domain numerical solver. By using an iterative optimisation algorithm based on the GaussNewton approach, the head model with an artificially embedded stroke region modelled as blood is successfully reconstructed through a blind reconstruction procedure (i.e. no a priori information about the shape or the dielectric properties of the model is assumed). It is observed that beginning from a homogeneous guess similar to the background material, right after the first iteration the shapes of the layers are clearly distinguished and the values of the dielectric properties converge to the actual values after only 10 iterations.Introduction: A stroke is a rapid damage in the brain tissue due to an interruption in the blood flow. Stroke can be classified into ischaemic and haemorrhagic. The ischaemic stroke is caused by a blood vessel, whereas the haemorrhagic stroke is due to a vessel rupturing and bleeding into the surrounding area.Conventional imaging techniques for stroke detection are magnetic resonance imaging and computed tomography (CT-scan). Although they have proved to be efficient in detecting a stroke, there are drawbacks associated with both the techniques that have motivated the research for alternative or complementary techniques. Since a stroke is an emergency problem, to avoid loss of time, the favourite imaging system should have the potential of being implemented in a portable device available in emergency centres and even in ambulances. In this case, the diagnosis can be initiated while the patient is underway to a hospital.Microwave imaging offers a portable, cost-effective and safe imaging procedure for medical applications [1] and it can be regarded as an alternative or complementary imaging technique. Microwave tomography (MWT) is a promising imaging technique that provides a quantitative map of the dielectric properties of the object of interest, i.e. complex permittivity [2]. In fact, the tissues affected by disease exhibit other dielectric properties than the healthy tissues and this is the very basis MWT works upon.The human head is quite a high-contrast target consisting of both high-water-content (e.g. grey matter) and low-water-content tissues (e.g. skull) and therefore it is a complex imaging target for MWT. In [3], a simplified human head model has been imaged by the MWT approach. In this Letter, an anatomically realistic two-dimensional (2D) head model extracted from [4] with an artificially embedded haemorrhagic stroke is measured in an finite difference time-domain (FDTD) solver by using 24 ideal point source antennas surrounding the model in the form of a square array. By using a nonlinear iterative reconstruction program based on the Gauss-Newton optimisation implemented in C + + , the head model is successfully reconstructed after 10 iterations starting fr...

This paper is devoted to a comparison of early works of Kato and Yosida on the integration of non-autonomous linear evolution equationsẋ = A(t)x in Banach space, where the domain D of A(t) is independent of t. Our focus is on the regularity assumed of t → A(t) and our main objective is to clarify the meaning of the rather involved set of assumptions given in Yosida's classic and highly influential Functional Analysis. We prove Yosida's assumptions to be equivalent to Kato's condition that t → A(t)x is continuously differentiable for each x ∈ D.