Comparative lung distribution of radiolabeled tobramycin between nebulized and intravenous administration in a mechanically-ventilated ovine model, an observational study

BACKGROUND

Ventilator-associated pneumonia is common and is treated using nebulized antibiotics. Although adequate pulmonary biodistribution is important for antibiotic effect, there is a lack of data for both intravenous (IV) and nebulized antibiotic administration during mechanical ventilation.

OBJECTIVE

To describe the comparative pulmonary regional distribution of IV and nebulized technetium-99m-labeled tobramycin (^99mTc-tobramycin) 400 mg in a mechanically-ventilated ovine model.

METHODS

The study was performed in a mechanically-ventilated ovine model. ^99mTc-tobramycin 400 mg was obtained using a radiolabeling process. Computed tomography (CT) was performed. Ten sheep were given ^99mTc-tobramycin 400 mg via either an IV (five sheep) or nebulized (five sheep) route. Planar images (dorsal, ventral, left lateral and right lateral) were obtained using a gamma camera. Blood samples were obtained every 15 min for 1 h (4 time points) and lung, liver, both kidney, and urine samples were obtained post-mortem.

RESULTS

Ten sheep were anesthetized and mechanically ventilated. Whole-lung deposition of nebulized ^99mTc-tobramycin 400 mg was significantly lower than with IV (8.8% vs. 57.1%, P<0.001). For both administration routes, there was significantly lower deposition in upper lung zones compared with the rest of the lungs. Dorsal deposition was significantly higher with nebulized ^99mTc-tobramycin 400 mg compared with IV (68.9% vs. 58.9%, P=0.003). Lung concentrations of ^99mTc-tobramycin were higher with IV compared with nebulized administration. There were significantly higher concentrations of ^99mTc-tobramycin in blood, liver and urine with IV administration compared with nebulized.

CONCLUSIONS

Nebulization resulted in lower whole and regional lung deposition of ^99mTc-tobramycin compared with IV administration and appeared to be associated with low blood and extra-pulmonary organ concentrations.

Deep Learning for Fast and Spatially Constrained Tissue Quantification From Highly Accelerated Data in Magnetic Resonance Fingerprinting

Magnetic resonance fingerprinting (MRF) is a quantitative imaging technique that can simultaneously measure multiple important tissue properties of human body. Although MRF has demonstrated improved scan efficiency as compared to conventional techniques, further acceleration is still desired for translation into routine clinical practice. The purpose of this paper is to accelerate MRF acquisition by developing a new tissue quantification method for MRF that allows accurate quantification with fewer sampling data. Most of the existing approaches use the MRF signal evolution at each individual pixel to estimate tissue properties, without considering the spatial association among neighboring pixels. In this paper, we propose a spatially constrained quantification method that uses the signals at multiple neighboring pixels to better estimate tissue properties at the central pixel. Specifically, we design a unique two-step deep learning model that learns the mapping from the observed signals to the desired properties for tissue quantification, i.e.: 1) with a feature extraction module for reducing the dimension of signals by extracting a low-dimensional feature vector from the high-dimensional signal evolution and 2) a spatially constrained quantification module for exploiting the spatial information from the extracted feature maps to generate the final tissue property map. A corresponding two-step training strategy is developed for network training. The proposed method is tested on highly undersampled MRF data acquired from human brains. Experimental results demonstrate that our method can achieve accurate quantification for T1 and T2 relaxation times by using only 1/4 time points of the original sequence (i.e., four times of acceleration for MRF acquisition).