Excitation-resolved multispectral method for imaging pharmacokinetic parameters in dynamic fluorescent molecular tomography

Dynamic fluorescence molecular tomography metabolic parameters solution based on problem decomposition and prior refactor

Dynamic fluorescence molecular tomography (DFMT), as a noninvasive optical imaging method, can quantify metabolic parameters of living animal organs and assist in the diagnosis of metabolic diseases. However, existing DFMT methods do not have a high capacity to reconstruct abnormal metabolic regions, and require additional prior information and complicated solution methods. This paper introduces a problem decomposition and prior refactor (PDPR) method. The PDPR decomposes the metabolic parameters into two kinds of problems depending on their temporal coupling, which are solved using regularization and parameter fitting. Moreover, PDPR introduces the idea of divide-and-conquer to refactor prior information to ensure discrimination between metabolic abnormal regions and normal tissues. Experimental results show that PDPR is capable of separating abnormal metabolic regions of the liver and has the potential to quantify metabolic parameters and diagnose liver metabolic diseases in small animals.

High-sensitivity dynamic diffuse fluorescence tomography system for fluorescence pharmacokinetics

SIGNIFICANCE

Dynamic diffuse fluorescence tomography (DFT) can recover the static distribution of fluorophores and track dynamic temporal events related to physiological and disease progression. Dynamic imaging indocyanine green (ICG) approved by the food and drug administration is still under-exploited because of its characteristics of low quantum yield and relatively rapid tissue metabolism.

AIM

In order to acquire the ICG tomographic image sequences for pharmacokinetic analysis, a dynamic DFT system was proposed.

APPROACH

A fiber-based dynamic DFT system adopts square-wave modulation lock-in photon-counting scheme and series-parallel measurement mode, which possesses high sensitivity, large dynamic range, high anti-ambient light ability in common knowledge, as well as good cost performance. In order to investigate the effectiveness of the proposed system, the measurement stability and the anti-crosstalk-a crucial factor affecting the system parallelization-were assessed firstly, then a series of static phantoms, dynamic phantoms and in vivo mice experiments were conducted to verify the imaging capability.

RESULTS

The system has the limited dynamic range of 100 dB, the fluctuation of photon counting within 3%, and channel-to-channel crosstalk ratio better than 1.35. Under the condition of a sufficient signal-to-noise ratio, a complete measurement time for one frame image was 10.08 s. The experimental results of static phantoms with a single target and three targets showed that this system can accurately obtain the positions, sizes, and shapes of the targets and the reconstructed images exhibited a high quantitativeness. Further, the self-designed dynamic phantom experiments demonstrated the capability of the system to capture fast changing fluorescence signals. Finally, the in vivo experiments validated the practical capability of the system to effectively track the ICG metabolism in living mice.

CONCLUSIONS

These results demonstrate that our proposed system can be utilized for assessing ICG pharmacokinetics, which may provide a valuable tool for tumor detection, drug assessment, and liver function evaluation.

Method for improving the accuracy of fluorescence molecular tomography based on multi-wavelength concurrent reconstruction

A Concurrent-wavelength Reconstruction Algorithm (CRA) based on multi-wavelength information fusion is proposed in this paper that aims to further improve the accuracy of Fluorescence Molecular Tomography (FMT) reconstruction. Combining multi-spectral data with FMT technology, the information of 650 and 750 nm wavelengths near-infrared was used to increase the feature information of the dominant 850 nm wavelength near-infrared effectively. Principal component analysis, which can remove redundant information and achieve data dimensionality reduction, was then utilized to extract the feature information. Finally, tomographic reconstruction of the anomalies was performed based on the stacked auto-encoder neural network model. The comparison results of numerical experiments showed that the reconstruction effect of CRA was superior to the performance of the single wavelength model. The correlation coefficient between CRA reconstructed anomalies' fluorescence yield values and the real fluorescence yield values remained at 0.95 or more under the noise of different levels of signal-to-noise ratios. Therefore, the CRA proposed in this paper could effectively improve on the ill-posedness of the inverse problem, which could further enhance the accuracy of FMT reconstruction.