The ill-posedness of the FMT inverse problem is mainly due to the lack of
information and uncertainty due to the high scattering of photons. In order to
overcome the ill-posedness of reverse problems, researchers started from the light
source prior information and combined it with a variety of a priori information
related to the light source and photon transmission to reduce the uncertainty of the
information so as to improve the accuracy of inverse problem-solving [83–
89]. Researchers usually combined the prior information of the structure into the
FMT reconstruction and proposed a nonhomogeneous imaging space model and a
priori reconstruction method, which greatly improved the reconstruction accuracy.
The structure of imaging space prior information can usually be obtained by highresolution structural imaging modalities such as CT and MRI [90–94]. The optical
parameters of various organs and tissues can be obtained by other imaging techniques such as DOT. The imaging technique that combines imaging modalities to
increase imaging prior information is also known as multimodality imaging and is
the focus of current medical imaging research [95].
Although researchers have put forward prior knowledge such as feasible regions,
structural prior information to augment the information needed for reconstruction,
the morbidity of the FMT reconstruction equation remains unresolved. Moreover,
the actual FMT acquisition data usually contain a certain amount of noise, which has
a great impact on the reconstruction of the pathological equation. A small signal
disturbance may lead to a large reconstruction error. Therefore, researchers apply
regularization techniques to FMT reconstruction to constrain the reconstruction
process and reduce morbidity [96–104]. The classical regularization term is
Lp-norm regularization. The Lp-norm regularization ( p ¼ 0.5–2) usually obtains a
smoother reconstructed result of a large reconstructed area and has a good reconstruction effect for a large light source volume in an imaging space. Another
available regularization method is total variation (TV) [105]. The main idea of TV
norm regularization is to constrain the variation terms of the distribution of the
fluorescent light sources while preserving the boundaries of the light source zones
(Fig. 13).
4 Medical Application
Precision medicine has promoted the development of treatment modalities that are
developed to specifically kill tumor cells but not normal cells. The traditional
methods of drug discovery have many disadvantages, such as a long research period
and the antitumor drug treatment effects in situ cannot be monitored in real time.
Therefore, the use of new technologies such as FMI for drug discovery is urgently
needed. It seems likely that FMI will meet this challenge for the evaluation of
therapeutic effects. The results were more accurate and reliable than the traditional
measurement of tumor size. In this chapter, the application of FMI will be described
in drug discovery, including identification of therapeutic targets, candidate drug
screening, pharmacokinetics of drugs, and prodrug development.
Fluorescence Molecular Imaging of Medicinal Chemistry in Cancer
15
information and uncertainty due to the high scattering of photons. In order to
overcome the ill-posedness of reverse problems, researchers started from the light
source prior information and combined it with a variety of a priori information
related to the light source and photon transmission to reduce the uncertainty of the
information so as to improve the accuracy of inverse problem-solving [83–
89]. Researchers usually combined the prior information of the structure into the
FMT reconstruction and proposed a nonhomogeneous imaging space model and a
priori reconstruction method, which greatly improved the reconstruction accuracy.
The structure of imaging space prior information can usually be obtained by highresolution structural imaging modalities such as CT and MRI [90–94]. The optical
parameters of various organs and tissues can be obtained by other imaging techniques such as DOT. The imaging technique that combines imaging modalities to
increase imaging prior information is also known as multimodality imaging and is
the focus of current medical imaging research [95].
Although researchers have put forward prior knowledge such as feasible regions,
structural prior information to augment the information needed for reconstruction,
the morbidity of the FMT reconstruction equation remains unresolved. Moreover,
the actual FMT acquisition data usually contain a certain amount of noise, which has
a great impact on the reconstruction of the pathological equation. A small signal
disturbance may lead to a large reconstruction error. Therefore, researchers apply
regularization techniques to FMT reconstruction to constrain the reconstruction
process and reduce morbidity [96–104]. The classical regularization term is
Lp-norm regularization. The Lp-norm regularization ( p ¼ 0.5–2) usually obtains a
smoother reconstructed result of a large reconstructed area and has a good reconstruction effect for a large light source volume in an imaging space. Another
available regularization method is total variation (TV) [105]. The main idea of TV
norm regularization is to constrain the variation terms of the distribution of the
fluorescent light sources while preserving the boundaries of the light source zones
(Fig. 13).
4 Medical Application
Precision medicine has promoted the development of treatment modalities that are
developed to specifically kill tumor cells but not normal cells. The traditional
methods of drug discovery have many disadvantages, such as a long research period
and the antitumor drug treatment effects in situ cannot be monitored in real time.
Therefore, the use of new technologies such as FMI for drug discovery is urgently
needed. It seems likely that FMI will meet this challenge for the evaluation of
therapeutic effects. The results were more accurate and reliable than the traditional
measurement of tumor size. In this chapter, the application of FMI will be described
in drug discovery, including identification of therapeutic targets, candidate drug
screening, pharmacokinetics of drugs, and prodrug development.
Fluorescence Molecular Imaging of Medicinal Chemistry in Cancer
15