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Thermal properties of biological tissues play a critical role in the study of tumor angiogenesis and the design and monitoring of thermal therapies. To map thermal parameters noninvasively, we propose temperature-change-based thermal tomography (TTT) that relies on relative temperature mapping using magnetic resonance imaging (MRI). Our approach is unique in two aspects: (1) the steady-state body temperature in thermal equilibrium is not restricted to be spatially invariant, and (2) absolute temperature mapping is not required. These two features are physiologically realistic and technically convenient. Our numerical simulation indicates that a

Traditional thermal tomography is based on using an infrared camera to measure the surface temperature of a tissue and then solving the inverse heat transfer problem to reconstruct the interior tissue thermal parameters [

Assuming absolute temperature within a biological tissue is described by the Pennes’ bioheat transfer equation, we have

Having established the general framework of TTT, we consider an important practical application: the imaging and detection of breast cancer. It has been shown that blood flow in tumors and in normal tissues can differ dramatically [_{B}

Schematic of the breast phantom.

Now we use a finite-difference (FD) method [

(a) Relative temperature field plotted along the dashed line in Figure

We now treat the noise-added simulation results as MRI measurements data and use them to test our thermal reconstruction algorithm. In this letter, we assume that

In the absence of noise, the spatial distribution of

Reconstructed thermal coefficients

We emphasize that the advantages of TTT are derived primarily from its relative-temperature basis. By formulating a differential equation based on temperature changes (i.e., (

The algorithm presented in this paper serves as a proof-of-concept validation and can be improved in future studies. For example, the method of five-point polynomial fitting can be replaced by other spatial filtering techniques that can effectively reduce the measurement noises. The exponential temporal fitting can also be replaced by more sophisticated filtering techniques such as the Wiener filter. The assumption of an initial Gaussian temperature distribution is also not necessary: We have verified the reconstruction algorithm (i.e., (

Compared with other methods of thermal tomography in current literature, the method presented in this paper is significantly simpler. For example, the method presented in [

In conclusion, we have developed a temperature-change-based thermal tomography and established a simple and straightforward thermal reconstruction algorithm. Using realistic thermal parameters and assuming a temperature measurement accuracy of 0.5°C, we have successfully reconstructed the spatial distribution of a (9 mm

The authors gratefully acknowledge the support of the Institute for Critical Technology and Applied Science (ICTAS) at Virginia Tech. They would also like to thank Professor Robert Kraft of Wake Forest University Health Sciences for helpful discussions on MRI.