Abstract
Assessment of disease burden and drug efficacy is achieved preclinically using high resolution micro computed tomography (CT). However, micro-CT is not applicable to clinical human imaging due to operating at high dose. In addition, the technology differences between micro-CT and standard clinical CT prevent direct translation of preclinical applications. The current proof-of-concept study presents spectral photon-counting CT as a clinically translatable, molecular imaging tool by assessing contrast uptake in an ex-vivo mouse model of pulmonary tuberculosis (TB). Iodine, a common contrast used in clinical CT imaging, was introduced into a murine model of TB. The excised mouse lungs were imaged using a standard micro-CT subsystem (SuperArgus) and the contrast enhanced TB lesions quantified. The same lungs were imaged using a spectral photoncounting CT system (MARS small-bore scanner). Iodine and soft tissues (water and lipid) were materially separated, and iodine uptake quantified. The volume of the TB infection quantified by spectral CT and micro-CT was found to be 2.96 mm3 and 2.83 mm3, respectively. This proof-of-concept study showed that spectral photon-counting CT could be used as a predictive preclinical imaging tool for the purpose of facilitating drug discovery and development. Also, as this imaging modality is available for human trials, all applications are translatable to human imaging. In conclusion, spectral photon-counting CT could accelerate a deeper understanding of infectious lung diseases using targeted pharmaceuticals and intrinsic markers, and ultimately improve the efficacy of therapies by measuring drug delivery and response to treatment in animal models and later in humans.
Highlights
According to the World Health Organization, mycobacterium tuberculosis (TB) is a worldwide health problem, with 10 million new TB cases and 1.3 million deaths recorded in 2019 alone [1]
The K-edge of iodine was detected as a peak in attenuation in the third energy bin (34-45 keV) for every concentration
For micro-computed tomography (CT), a 5% margin was established by previous research which examined multiple healthy lung tissue samples [34]
Summary
According to the World Health Organization, mycobacterium tuberculosis (TB) is a worldwide health problem, with 10 million new TB cases and 1.3 million deaths recorded in 2019 alone [1]. Extensive efforts to curb TB and TB drug resistance via novel therapies have resulted in a decline in TB incidence of approximately 2% per year. Clinical trials examining new TB treatments are lengthy and complex, suffer from poor patient compliance, and require long periods of follow-up to assess the patient’s response to treatment [1], [5], [6]. Uncertainty surrounding the optimal combination of drugs and doses to effectively treat patients is a major cause of efficacy assessment failure [5], [6]. Predictive preclinical imaging plays a vital role in facilitating the development of novel drug regimens and preclinical efficacy studies using animal models of disease is key to supporting the rationale of clinical trials involving patients [7]
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