Molecular Imaging of Inflammatory Disease

Abstract

Inflammatory diseases include a wide variety of highly prevalent conditions with high mortality rates in severe cases ranging from cardiovascular disease, to rheumatoid arthritis, to chronic obstructive pulmonary disease, to graft vs. host disease, to a number of gastrointestinal disorders. Many diseases that are not considered inflammatory per se are associated with varying levels of inflammation. Imaging of the immune system and inflammatory response is of interest as it can give insight into disease progression and severity. Clinical imaging technologies such as computed tomography (CT) and magnetic resonance imaging (MRI) are traditionally limited to the visualization of anatomical information; then, the presence or absence of an inflammatory state must be inferred from the structural abnormalities. Improvement in available contrast agents has made it possible to obtain functional information as well as anatomical. In vivo imaging of inflammation ultimately facilitates an improved accuracy of diagnostics and monitoring of patients to allow for better patient care. Highly specific molecular imaging of inflammatory biomarkers allows for earlier diagnosis to prevent irreversible damage. Advancements in imaging instruments, targeted tracers, and contrast agents represent a rapidly growing area of preclinical research with the hopes of quick translation to the clinic.

Keywords: cardiovascular disease; chronic obstructive pulmonary disease; graft vs. host disease; image analysis; inflammation; machine learning; molecular imaging; rheumatoid arthritis.

Figure 1

¹⁸F-Flourodeoxyglucose (¹⁸F-FDG) positron emission tomography (PET)/computed tomography (CT) imaging of activated macrophages to visualize vulnerable plaques through increase in glucose metabolism. Higher ¹⁸F-FDG update is seen in the right common carotid artery (arrow) [47].

Figure 2

Confirmation of rheumatoid arthritis (RA) in the wrists/hands of patients using ⁸⁹Zr-rituximab PET imaging to target B-cell accumulation [105].

Figure 3

Coronal (left) and transversal (right) SPECT/CT imaging of matrix metalloproteases (MMPs). (A) Wild-type mice injected with ^99mTc-labeled RP805 (B) IL-13 transgenic mice injected with ^99mTc-labeled RP805 (C) IL-13 transgenic mice injected with an amide analog tracer as a control. (D) Quantification of uptake in SPECT images. * p < 0.01 ** p < 0.001 [136].

Figure 4

Images of a patient with history of chronic diarrhea that is occasionally bloody. CT ordered for unrelated reasons incidentally showed non-specific inflammation. Correlation with endoscopy showed substantial chronic inflammation. (A) CT with arrows showing inflammation of transverse colon. (B) Endoscopic images of transverse colon with diffuse pseudopolyps. (C) CT with arrows showing inflammation of sigmoid colon. (D) Endoscopic images of sigmoid colon with diffuse pseudopolyps. The lack of an inflammation or cancer specific contrast agent for the CT or endoscopic evaluation required a biopsy to confirm a lack of neoplasia.

Figure 5

(A) ¹⁸F-FDG PET image of human patient with Crohn’s disease. (B) T1-weighted magnetic resonance imaging (MRI) image of the same patient. (C) Merged PET/MRI. White arrows indicate locations of acute inflammation while red arrows highlight damage resulting from earlier disease action. The asterisk (*) shows a site of proliferation of fibrofatty compounds in the mesentery. SUVmax of ¹⁸F-FDG 5.6–9.2 vs. SUVmax of background bowel 1.5–2.8 [147].

Figure 6

(A) Green arrowheads indicate the location of inflammation; red arrowheads indicate the location of A431 tumors. Images are taken after glucose-functionalized gold nanoparticles (2GF-GNP) injection. (B) ¹⁸F-FDG-PET/CT slice images of a representative mouse at 40–60 min post-injection. (C) CT surface-rendered images of the same mouse at 3.5 h post IV injection of 2GF-GNP. Quantification of ¹⁸F-FDG uptake and 2GF-GNP is shown in figures (D,E), respectively. ¹⁸F-FDG cannot differentiate between inflammatory lesions and tumor, while 2GF-GNP can [161].

Figure 7

Schematic of the convolutional neural network that scores disease severity of RA based on an ultrasound color Doppler image of the wrist. Synovitis activity is evaluated and scored from 0–3, based on the OMERACT-EULAR Synovitis Scoring (OESS) System. After passing through each layer of the neural network, the classification neuron will map the resulting information to probability scores for each OESS score. The score with the highest probability is assigned to the US image [189].