MR Imaging-Based In Vivo Macrophage Imaging to Monitor Immune Response after Radiofrequency Ablation of the Liver.

PURPOSE: To establish molecular magnetic resonance (MR) imaging instruments for in vivo characterization of the immune response to hepatic radiofrequency (RF) ablation using cell-specific immunoprobes. MATERIALS AND METHODS: Seventy-two C57BL/6 wild-type mice underwent standardized hepatic RF ablation (70 °C for 5 minutes) to generate a coagulation area measuring 6-7 mm in diameter. CD68+ macrophage periablational infiltration was characterized with immunohistochemistry 24 hours, 72 hours, 7 days, and 14 days after ablation (n = 24). Twenty-one mice were subjected to a dose-escalation study with either 10, 15, 30, or 60 mg/kg of rhodamine-labeled superparamagnetic iron oxide nanoparticles (SPIONs) or 2.4, 1.2, or 0.6 mg/kg of gadolinium-160 (160Gd)-labeled CD68 antibody for assessment of the optimal in vivo dose of contrast agent. MR imaging experiments included 9 mice, each receiving 10-mg/kg SPIONs to visualize phagocytes using T2∗-weighted imaging in a horizontal-bore 9.4-T MR imaging scanner, 160Gd-CD68 for T1-weighted MR imaging of macrophages, or 0.1-mmol/kg intravenous gadoterate (control group). Radiological-pathological correlation included Prussian blue staining, rhodamine immunofluorescence, imaging mass cytometry, and immunohistochemistry. RESULTS: RF ablation-induced periablational infiltration (206.92 µm ± 12.2) of CD68+ macrophages peaked at 7 days after ablation (P < .01) compared with the untreated lobe. T2∗-weighted MR imaging with SPION contrast demonstrated curvilinear T2∗ signal in the transitional zone (TZ) (186 µm ± 16.9), corresponsing to Iron Prussian blue staining. T1-weighted MR imaging with 160Gd-CD68 antibody showed curvilinear signal in the TZ (164 µm ± 3.6) corresponding to imaging mass cytometry. CONCLUSIONS: Both SPION-enhanced T2∗-weighted and 160Gd-enhanced T1-weighted MR imaging allow for in vivo monitoring of macrophages after RF ablation, demonstrating the feasibility of this model to investigate local immune responses.

PET Image Denoising using a Deep-Learning Method for Extremely Obese Patients.

The image quality in clinical PET scan can be severely degraded due to high noise levels in extremely obese patients. Our work aimed to reduce the noise in clinical PET images of extremely obese subjects to the noise level of lean subject images, to ensure consistent imaging quality. The noise level was measured by normalized standard deviation (NSTD) derived from a liver region of interest. A deep learning-based noise reduction method with a fully 3D patch-based U-Net was used. Two U-Nets, U-Nets A and B, were trained on datasets with 40% and 10% count levels derived from 100 lean subjects, respectively. The clinical PET images of 10 extremely obese subjects were denoised using the two U-Nets. The results showed the noise levels of the images with 40% counts of lean subjects were consistent with those of the extremely obese subjects. U-Net A effectively reduced the noise in the images of the extremely obese patients while preserving the fine structures. The liver NSTD improved from 0.13±0.04 to 0.08±0.03 after noise reduction (p = 0.01). After denoising, the image noise level of extremely obese subjects was similar to that of lean subjects, in terms of liver NSTD (0.08±0.03 vs. 0.08±0.02, p = 0.74). In contrast, U-Net B over-smoothed the images of extremely obese patients, resulting in blurred fine structures. In a pilot reader study comparing extremely obese patients without and with U-Net A, the difference was not significant. In conclusion, the U-Net trained by datasets from lean subjects with matched count level can provide promising denoising performance for extremely obese subjects while maintaining image resolution, though further clinical evaluation is needed.

Diagnostic approach to primary retroperitoneal pathologies: what the radiologist needs to know.

Retroperitoneal soft tissue lesions represent a wide range of disease processes with overlapping imaging findings. Familiarity with the CT and MR characteristics of these conditions is important to guide clinical evaluation. We review the tissue types, characteristic clinical, demographic, and imaging features of retroperitoneal tumors and tumor-like non-neoplastic conditions with CT and MR correlation, including anatomic and imaging clues, and provide a diagnostic approach to aide the radiologist in making a specific diagnosis.

Molecular MRI of the Immuno-Metabolic Interplay in a Rabbit Liver Tumor Model: A Biomarker for Resistance Mechanisms in Tumor-targeted Therapy?

Background The immuno-metabolic interplay has gained interest for determining and targeting immunosuppressive tumor micro-environments that remain a barrier to current immuno-oncologic therapies in hepatocellular carcinoma. Purpose To develop molecular MRI tools to reveal resistance mechanisms to immuno-oncologic therapies caused by the immuno-metabolic interplay in a translational liver cancer model. Materials and Methods A total of 21 VX2 liver tumor-bearing New Zealand white rabbits were used between October 2018 and February 2020. Rabbits were divided into three groups. Group A (n = 3) underwent intra-arterial infusion of gadolinium 160 (160Gd)-labeled anti-human leukocyte antigen-DR isotope (HLA-DR) antibodies to detect antigen-presenting immune cells. Group B (n = 3) received rhodamine-conjugated superparamagnetic iron oxide nanoparticles (SPIONs) intravenously to detect macrophages. These six rabbits underwent 3-T MRI, including T1- and T2-weighted imaging, before and 24 hours after contrast material administration. Group C (n = 15) underwent extracellular pH mapping with use of MR spectroscopy. Of those 15 rabbits, six underwent conventional transarterial chemoembolization (TACE), four underwent conventional TACE with extracellular pH-buffering bicarbonate, and five served as untreated controls. MRI signal intensity distribution was validated by using immunohistochemistry staining of HLA-DR and CD11b, Prussian blue iron staining, fluorescence microscopy of rhodamine, and imaging mass cytometry (IMC) of gadolinium. Statistical analysis included Mann-Whitney U and Kruskal-Wallis tests. Results T1-weighted MRI with 160Gd-labeled antibodies revealed localized peritumoral ring enhancement, which corresponded to gadolinium distribution detected with IMC. T2-weighted MRI with SPIONs showed curvilinear signal intensity representing selective peritumoral deposition in macrophages. Extracellular pH-specific MR spectroscopy of untreated liver tumors showed acidosis (mean extracellular pH, 6.78 ± 0.09) compared with liver parenchyma (mean extracellular pH, 7.18 ± 0.03) (P = .008) and peritumoral immune cell exclusion. Normalization of tumor extracellular pH (mean, 6.96 ± 0.05; P = .02) using bicarbonate during TACE increased peri- and intratumoral immune cell infiltration (P = .002). Conclusion MRI in a rabbit liver tumor model was used to visualize resistance mechanisms mediated by the immuno-metabolic interplay that inform susceptibility and response to immuno-oncologic therapies, providing a therapeutic strategy to restore immune permissiveness in liver cancer. © RSNA, 2020 Online supplemental material is available for this article.