This course will address the steps that a beginner should follow to develop a molecular imaging experiment in small animals: after setting the target and choosing the appropriate animal model, we will evaluate the pros and cons of existing imaging modalities, with a focus on MRI data acquisition and integration during post-processing. Examples will be taken from oncology and neuroscience.
1. Setting the target
You will come with one or several questions in mind. Then, after discussing with imaging people, you may reformulate your questions. It is very important to follow this path so that you can actually come with questions that can be effectively addressed by molecular imaging. You will have to consider technical aspects (imaging modality (may be several modalities), imaging technical details, possibly contrast agents (1,2) (type, dose), regulation issues, post-processing issues, data integration and statistics…). Be sure to cover these aspects when designing your experiments. If you do not find an existing study that describes what you want to do, you may need to conduct preliminary tests to evaluate whether molecular imaging can answer your specific question(s). One major issue is to handle this set-up phase within your specific regulations. Do not hesitate to contact the authors of publications similar to the study you have in mind to discuss limits or ask for a data sample. You can also try to obtain sample data during already authorized research. For example, you may want to obtain an extra image during an existing protocol to evaluate if a particular spatial resolution may be reached.2. Some issues about animal models
Based on examples taken in the field of oncology and neuroscience, we will briefly illustrate the potentials and the limits of animal models, including the impact of anesthetics and physiological monitoring.3. Overview of animal imaging modalities
Several imaging modalities are available. We will briefly review the pros and cons of the main preclinical imaging modalities, including X-ray imaging, optical imaging (3,4), nuclear imaging (5), ultrasound (6), and MRI (7-9). Multi-modal (such as PET-MRI) systems are also emerging. Main items to consider are spatial and temporal resolutions and sensitivity4. Overview of MRI options
MRI is a technique well-suited to image several information during a single session. We will briefly review how to organize your MRI experiment and what estimates may be obtained with MRI. Several types of map may be obtained without any contrast agent such as diffusion based estimates (related to edema or tissue microstructure), blood flow, MR spectroscopy. Contrast agents may also be used to map other information such as tissue oxygenation. At a preclinical level, many contrast agents have also been developed to reach specific targets. Imaging protocols should be adapted to the pharmacokinetic of the contrast agent. Depending on the desired quantification level, additional sequences may be required (e.g. measure a reference signal, acquire a T1 map…).5. Data processing, integration, and interpretation
From the acquired images, one need to estimate values. There is no standard software to post-process this type. One can extract information image per image, either using standard contrasts (signal difference between an area that received contrast agent and an area that did not) or using texture analysis (sometimes call Radiomics) (10). In case several types of MRI images are acquired per animal, one may also consider an integrated analysis (11-13). This ability to derive tissue information across several images – potentially across several imaging modalities – appears very powerful.1. James ML, Gambhir SS. A molecular imaging primer: modalities, imaging agents, and applications. Physiol Rev 2012;92(2):897-965.
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7. de Backer ME, Nabuurs RJ, van Buchem MA, van der Weerd L. MR-based molecular imaging of the brain: the next frontier. AJNR Am J Neuroradiol 2010;31(9):1577-1583.
8. Bartelle BB, Barandov A, Jasanoff A. Molecular fMRI. J Neurosci 2016;36(15):4139-4148.
9. Haris M, Yadav SK, Rizwan A, Singh A, Wang E, Hariharan H, Reddy R, Marincola FM. Molecular magnetic resonance imaging in cancer. J Transl Med 2015;13:313. 10. Fan M, Li H, Wang S, Zheng B, Zhang J, Li L. Radiomic analysis reveals DCE-MRI features for prediction of molecular subtypes of breast cancer. PLoS One 2017;12(2):e0171683.
11. Coquery N, Francois O, Lemasson B, Debacker C, Farion R, Remy C, Barbier EL. Microvascular MRI and unsupervised clustering yields histology-resembling images in two rat models of glioma. J Cereb Blood Flow Metab 2014;34(8):1354-1362.
12. Arnaud A, Forbes F, Coquery N, Collomb N, Lemasson B, Barbier EL. Fully Automatic Lesion Localization and Characterization: Application to Brain Tumors using Multiparametric Quantitative MRI Data. IEEE Transactions on Medical Imaging Early view.
13. Katiyar P, Divine MR, Kohlhofer U, Quintanilla-Martinez L, Scholkopf B, Pichler BJ, Disselhorst JA. A Novel Unsupervised Segmentation Approach Quantifies Tumor Tissue Populations Using Multiparametric MRI: First Results with Histological Validation. Mol Imaging Biol 2017;19(3):391-397.