Non-Invasive Imaging of Acute Renal Allograft Rejection in Rats Using Small Animal 18F-FDG-PET

BACKGROUND: At present, renal grafts are the most common solid organ transplants world-wide. Given the importance of renal transplantation and the limitation of available donor kidneys, detailed analysis of factors that affect transplant survival are important. Despite the introduction of new and effective immunosuppressive drugs, acute cellular graft rejection (AR) is still a major risk for graft survival. Nowadays, AR can only be definitively by renal biopsy. However, biopsies carry a risk of renal transplant injury and loss. Most important, they can not be performed in patients taking anticoagulant drugs. METHODOLOGY/PRINCIPAL FINDINGS: We present a non-invasive, entirely image-based method to assess AR in an allogeneic rat renal transplantation model using small animal positron emission tomography (PET) and (18)F-fluorodeoxyglucose (FDG). 3 h after i.v. injection of 30 MBq FDG into adult uni-nephrectomized, allogeneically transplanted rats, tissue radioactivity of renal parenchyma was assessed in vivo by a small animal PET-scanner (post operative day (POD) 1,2,4, and 7) and post mortem dissection. The mean radioactivity (cps/mm(3) tissue) as well as the percent injected dose (%ID) was compared between graft and native reference kidney. Results were confirmed by histological and autoradiographic analysis. Healthy rats, rats with acute CSA nephrotoxicity, with acute tubular necrosis, and syngeneically transplanted rats served as controls. FDG-uptake was significantly elevated only in allogeneic grafts from POD 1 on when compared to the native kidney (%ID graft POD 1: 0.54+/-0.06; POD 2: 0.58+/-0.12; POD 4: 0.81+/-0.06; POD 7: 0.77+/-0.1; CTR: 0.22+/-0.01, n = 3-28). Renal FDG-uptake in vivo correlated with the results obtained by micro-autoradiography and the degree of inflammatory infiltrates observed in histology. CONCLUSIONS/SIGNIFICANCE: We propose that graft FDG-PET imaging is a new option to non-invasively, specifically, early detect, and follow-up acute renal rejection. This method is potentially useful to improve post-transplant rejection monitoring

What scans we will read: imaging instrumentation trends in clinical oncology

Oncological diseases account for a significant portion of the burden on public healthcare systems with associated costs driven primarily by complex and long-lasting therapies. Through the visualization of patient-specific morphology and functional-molecular pathways, cancerous tissue can be detected and characterized non- invasively, so as to provide referring oncologists with essential information to support therapy management decisions. Following the onset of stand-alone anatomical and functional imaging, we witness a push towards integrating molecular image information through various methods, including anato-metabolic imaging (e.g., PET/ CT), advanced MRI, optical or ultrasound imaging. This perspective paper highlights a number of key technological and methodological advances in imaging instrumentation related to anatomical, functional, molecular medicine and hybrid imaging, that is understood as the hardware-based combination of complementary anatomical and molecular imaging. These include novel detector technologies for ionizing radiation used in CT and nuclear medicine imaging, and novel system developments in MRI and optical as well as opto-acoustic imaging. We will also highlight new data processing methods for improved non-invasive tissue characterization. Following a general introduction to the role of imaging in oncology patient management we introduce imaging methods with well-defined clinical applications and potential for clinical translation. For each modality, we report first on the status quo and point to perceived technological and methodological advances in a subsequent status go section. Considering the breadth and dynamics of these developments, this perspective ends with a critical reflection on where the authors, with the majority of them being imaging experts with a background in physics and engineering, believe imaging methods will be in a few years from now. Overall, methodological and technological medical imaging advances are geared towards increased image contrast, the derivation of reproducible quantitative parameters, an increase in volume sensitivity and a reduction in overall examination time. To ensure full translation to the clinic, this progress in technologies and instrumentation is complemented by progress in relevant acquisition and image-processing protocols and improved data analysis. To this end, we should accept diagnostic images as “data”, and – through the wider adoption of advanced analysis, including machine learning approaches and a “big data” concept – move to the next stage of non-invasive tumor phenotyping. The scans we will be reading in 10 years from now will likely be composed of highly diverse multi- dimensional data from multiple sources, which mandate the use of advanced and interactive visualization and analysis platforms powered by Artificial Intelligence (AI) for real-time data handling by cross-specialty clinical experts with a domain knowledge that will need to go beyond that of plain imaging