Detection of lung tumors in mice using a 1-tesla compact magnetic resonance imaging system

Tiny golden angle ultrashort echo-time lung imaging in mice

Imaging the lung parenchyma with MRI is particularly difficult in small animals due to the high respiratory and heart rates, and ultrashort T2* at high magnetic field strength caused by the high susceptibilities induced by the air-tissue interfaces. In this study, a 2D ultrashort echo-time (UTE) technique was combined with tiny golden angle (tyGA) ordering. Data were acquired continuously at 11.7 T and retrospective center-of-k-space gating was applied to reconstruct respiratory multistage images. Lung (proton) density (f_P ), T2*, signal-to-noise ratio (SNR), fractional ventilation (FV) and perfusion (f) were quantified, and the application to dynamic contrast agent (CA)-enhanced (DCE) qualitative perfusion assessment tested. The interobserver and intraobserver and interstudy reproducibility of the quantitative parameters were investigated. High-quality images of the lung parenchyma could be acquired in all animals. Over all lung regions a mean T2* of 0.20 ± 0.05 ms was observed. FV resulted as 0.31 ± 0.13, and a trend towards lower SNR values during inspiration (EX: SNR = 12.48 ± 6.68, IN: SNR = 11.79 ± 5.86) and a significant (P < 0.001) decrease in lung density (EX: f_P  = 0.69 ± 0.13, IN: f_P  = 0.62 ± 0.13) were observed. Quantitative perfusion results as 34.63 ± 9.05 mL/cm³ /min (systole) and 32.77 ± 8.55 mL/cm³ /min (diastole) on average. The CA dynamics could be assessed and, because of the continuous nature of the data acquisition, reconstructed at different temporal resolutions. Where a good to excellent interobserver reproducibility and an excellent intraobserver reproducibility resulted, the interstudy reproducibility was only fair to good. In conclusion, the combination of tiny golden angles with UTE (2D tyGA UTE) resulted in a reliable imaging technique for lung morphology and function in mice, providing uniform k-space coverage and thus low-artefact images of the lung parenchyma after gating.

Cardio-Respiratory synchronized bSSFP MRI for high throughput in vivo lung tumour quantification

The identification and measurement of tumours is a key requirement in the study of tumour development in mouse models of human cancer. Disease burden in autochthonous tumours, such as those arising in the lung, can be seen with non-invasive imaging, but cannot be accurately measured using standard tools such as callipers. Lung imaging is further complicated in the mouse due to instabilities arising from the rapid but cyclic cardio-respiratory motions, and the desire to use free-breathing animals. Female A/JOlaHsd mice were either injected (i.p.) with PBS 0.1ml/10g body weight (n = 6), or 10% urethane/PBS 0.1ml/10g body weight (n = 12) to induce autochthonous lung tumours. Cardio-respiratory synchronised bSSFP MRI, at 200 μm isotropic resolution was performed at 8, 13 and 18 weeks post induction. Images from the same mouse at different time points were aligned using threshold-based segmented masks of the lungs (ITK-SNAP and MATLAB) and tumour volumes were determined via threshold-based segmentation (ITK-SNAP).Scan times were routinely below 10 minutes and tumours were readily identifiable. Image registration allowed serial measurement of tumour volumes as small as 0.056 mm³. Repetitive imaging did not lead to mouse welfare issues. We have developed a motion desensitised scan that enables high sensitivity MRI to be performed with high throughput capability of greater than 4 mice/hour. Image segmentation and registration allows serial measurement of individual, small tumours. This allows fast and highly efficient volumetric lung tumour monitoring in cohorts of 30 mice per imaging time point. As a result, adaptive trial study designs can be achieved, optimizing experimental and welfare outcomes.

A pulmonary metastatic model of murine melanoma assessed by magnetic resonance imaging

Immune checkpoint inhibitors and kinase inhibitors have improved prognosis of malignant melanoma (MM) patients. However, these therapies cannot completely overcome the metastasis of MM. Thus, development of new therapy against metastasis should be required. A first step towards this goal, the aim of this study, is to establish a model of pulmonary metastasis from primary cutaneous MM and a monitoring system. B16-F10, a murine melanoma cell line, was subcutaneously injected into the pinna of mice. The pinna was excised when the lesion was detected. A metastatic nodule on T2-weighted imaging was detected 4 weeks after resection of the pinna. Lung metastases were observed in 37.5% (6/16) of the specimens. We established a novel murine model of the high pulmonary metastasis of MM. The MRI was useful for observations of the growth of the metastatic lesions in the lungs without dissection.

Longitudinal, in vivo assessment of invasive pulmonary aspergillosis in mice by computed tomography and magnetic resonance imaging

Invasive aspergillosis is an emerging threat to public health due to the increasing use of immune suppressive drugs and the emergence of resistance against antifungal drugs. To deal with this threat, research on experimental disease models provides insight into the pathogenesis of infections caused by susceptible and resistant Aspergillus strains and by assessing their response to antifungal drugs. However, standard techniques used to evaluate infection in a preclinical setting are severely limited by their invasive character, thereby precluding evaluation of disease extent and therapy effects in the same animal. To enable non-invasive, longitudinal monitoring of invasive pulmonary aspergillosis in mice, we optimized computed tomography (CT) and magnetic resonance imaging (MRI) techniques for daily follow-up of neutropenic BALB/c mice intranasally infected with A. fumigatus spores. Based on the images, lung parameters (signal intensity, lung tissue volume and total lung volume) were quantified to obtain objective information on disease onset, progression and extent for each animal individually. Fungal lung lesions present in infected animals were successfully visualized and quantified by both CT and MRI. By using an advanced MR pulse sequence with ultrashort echo times, pathological changes within the infected lung became visually and quantitatively detectable at earlier disease stages, thereby providing valuable information on disease onset and progression with high sensitivity. In conclusion, these non-invasive imaging techniques prove to be valuable tools for the longitudinal evaluation of dynamic disease-related changes and differences in disease severity in individual animals that might be readily applied for rapid and cost-efficient drug screening in preclinical models in vivo.

A comparative study of automatic image segmentation algorithms for target tracking in MR-IGRT

On-board magnetic resonance (MR) image guidance during radiation therapy offers the potential for more accurate treatment delivery. To utilize the real-time image information, a crucial prerequisite is the ability to successfully segment and track regions of interest (ROI). The purpose of this work is to evaluate the performance of different segmentation algorithms using motion images (4 frames per second) acquired using a MR image-guided radiotherapy (MR-IGRT) system. Manual con-tours of the kidney, bladder, duodenum, and a liver tumor by an experienced radiation oncologist were used as the ground truth for performance evaluation. Besides the manual segmentation, images were automatically segmented using thresholding, fuzzy k-means (FKM), k-harmonic means (KHM), and reaction-diffusion level set evolution (RD-LSE) algorithms, as well as the tissue tracking algorithm provided by the ViewRay treatment planning and delivery system (VR-TPDS). The performance of the five algorithms was evaluated quantitatively by comparing with the manual segmentation using the Dice coefficient and target registration error (TRE) measured as the distance between the centroid of the manual ROI and the centroid of the automatically segmented ROI. All methods were able to successfully segment the bladder and the kidney, but only FKM, KHM, and VR-TPDS were able to segment the liver tumor and the duodenum. The performance of the thresholding, FKM, KHM, and RD-LSE algorithms degraded as the local image contrast decreased, whereas the performance of the VP-TPDS method was nearly independent of local image contrast due to the reference registration algorithm. For segmenting high-contrast images (i.e., kidney), the thresholding method provided the best speed (< 1 ms) with a satisfying accuracy (Dice = 0.95). When the image contrast was low, the VR-TPDS method had the best automatic contour. Results suggest an image quality determination procedure before segmentation and a combination of different methods for optimal segmentation with the on-board MR-IGRT system.