Medical images of a patient may have a significantly different appearance depending on imaging modality (e.g. MRI vs. CT), sequence type (e.g., T1-weighted MRI vs. T2-weighted MRI), and even manufacturer/model of equipment used for the same modality and sequence type (e.g. SIEMENS vs GE). Since in the context of deep learning training and test data often come from different institutions, it is important to determine how well neural networks generalize when image appearance varies. There is currently no systematic answer to this question. In this study, we investigate how deep neural networks trained for segmentation generalize. Our analysis is based on synthesizing a series of datasets of images with the target object of the same shape but with varying pixel intensity of the foreground object and the background. This simulates basic effects of changing equipment models and sequence types. We also consider scenarios when datasets with different image properties are combined to determine whether generalizability of the network to other scenarios is improved. We found that the generalizability of segmentation networks to changing intensities is poor. We also found that the generalizability is somewhat improved when different datasets are combined but that generalizability is typically limited to data similar to the two types of datasets included in training and not to datasets with different image intensities.
KEYWORDS: Magnetic resonance imaging, 3D printing, Brain, Xenon, Neuroimaging, Signal to noise ratio, Additive manufacturing, 3D acquisition, Tumors, Copper
Three-dimensional (3D) printing has significantly impacted the quality, efficiency, and reproducibility of preclinical magnetic resonance imaging. It has vastly expanded the ability to produce MR-compatible parts that readily permit customization of animal handling, achieve consistent positioning of anatomy and RF coils promptly, and accelerate throughput. It permits the rapid and cost-effective creation of parts customized to a specific imaging study, animal species, animal weight, or even one unique animal, not routinely used in preclinical research. We illustrate the power of this technology by describing five preclinical studies and specific solutions enabled by different 3D printing processes and materials. We describe fixtures, assemblies, and devices that were created to ensure the safety of anesthetized lemurs during an MR examination of their brain or to facilitate localized, contrast-enhanced measurements of white blood cell concentration in a mouse model of pancreatitis. We illustrate expansive use of 3D printing to build a customized birdcage coil and components of a ventilator to enable imaging of pulmonary gas exchange in rats using hyperpolarized Xe129. Finally, we present applications of 3D printing to create high-quality, dual RF coils to accelerate brain connectivity mapping in mouse brain specimens and to increase the throughput of brain tumor examinations in a mouse model of pituitary adenoma.
A four-dimensional magnetic resonance imaging (4-D-MRI) technique with Sagittal–Coronal–Diaphragm Point-of-Intersection (SCD-PoI) as a respiratory surrogate is proposed. To develop an image-based respiratory surrogate, the SCD-PoI motion tracking method is used for retrospective 4-D-MRI reconstruction. Single-slice sagittal MR cine was acquired at a location near the center of the diaphragmatic dome. Multiple-slice coronal MR cines were acquired for 4-D-MRI reconstruction. As a motion surrogate, the diaphragm motion was measured from the PoI among the sagittal MRI cine plane, coronal MRI cine planes, and the diaphragm surface. These points were defined as the SCD-PoI. This point is used as a one-dimensional diaphragmatic navigator in our study. The 4-D-MRI technique was evaluated on a 4-D digital extended cardiac-torso (XCAT) human phantom, a motion phantom, and seven human subjects (five healthy volunteers and two cancer patients). Motion trajectories of a selected region of interest were measured on 4-D-MRI and compared with the known XCAT motion that served as references. The mean absolute amplitude difference (D) and the cross-correlation coefficient (CC) of the comparisons were determined. 4-D-MRI of the XCAT phantom demonstrated highly accurate motion information (D=1.13 mm, CC=0.98). Motion trajectories of the motion phantom measured on 4-D-MRI matched well with the references (D=0.54 mm, CC=0.99). 4-D-MRI of human subjects showed minimal artifacts and clearly revealed the respiratory motion of organs and tumor (mean D=1.08±1.03 mm; mean CC=0.96). A 4-D-MRI technique with image-based respiratory surrogate has been developed and tested on phantoms and human subjects.
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