Demands for in-vivo human molecular imaging with high resolution and high sensitivity in positron emission tomography (PET) require several new design formulae. A classical problem of the PET design, however, was the trade-off between sensitivity and resolution. To satisfy both requirements, the brain-body convertible PET with wobbling and zooming is proposed. The features of this new proposed system are wobble sampling for high-resolution imaging and zooming mode for high sensitivity, especially for the brain dedicated imaging. For the high resolution, wobbling with a linear interpolation and line spread function (LSF) deconvolution reconstruction algorithm was introduced. The result of the proposed system provided resolution up to 1.56 mm full width at half maximum (FWHM) in the brain mode and resulting in the detector-to-resolution ratio (DRR) was 2.47. For both brain phantom and in-vivo rat brain imaging, the proposed system demonstrated superior image quality compared with the commercial PET systems. The newly designed PET with wobbling and zooming also demonstrated the possibility of developing practically usable high-resolution human brain PET-MRI fusion system, especially for the neuroscience research.

We propose a novel minimal path method for the segmentation of 2D and 3D line structures. Minimal path methods perform propagation of a wavefront emanating from a start point at a speed derived from image features, followed by path extraction using backtracing. Usually, the computation of the speed and the propagation of the wave are two separate steps, and point features are used to compute a static speed. We introduce a new continuous minimal path method which steers the wave propagation progressively using dynamic speed based on path features. We present three instances of our method, using an appearance feature of the path, a geometric feature based on the curvature of the path, and a joint appearance and geometric feature based on the tangent of the wavefront. These features have not been used in previous continuous minimal path methods. We compute the features dynamically during the wave propagation, and also efficiently using a fast numerical scheme and a low-dimensional parameter space. Our method does not suffer from discretization or metrication errors. We performed qualitative and quantitative evaluations using 2D and 3D images from different application areas.

We propose a novel hybrid approach for automatic 3D segmentation and quantification of high-resolution 7 Tesla magnetic resonance angiography (MRA) images of the human cerebral vasculature. Our approach consists of two main steps. First, a 3D model-based approach is used to segment and quantify thick vessels and most parts of thin vessels. Second, remaining vessel gaps of the first step in low-contrast and noisy regions are completed using a 3D minimal path approach, which exploits directional information. We present two novel minimal path approaches. The first is an explicit approach based on energy minimization using probabilistic sampling, and the second is an implicit approach based on fast marching with anisotropic directional prior. We conducted an extensive evaluation with over 2300 3D synthetic images and 40 real 3D 7 Tesla MRA images. Quantitative and qualitative evaluation shows that our approach achieves superior results compared with a previous minimal path approach. Furthermore, our approach was successfully used in two clinical studies on stroke and vascular dementia.

Scatter correction is very important in 3-D PET reconstruction due to a large scatter contribution in measurements. Currently, one of the most popular methods is the so-called single scatter simulation (SSS), which considers single Compton scattering contributions from many randomly distributed scatter points. The SSS enables a fast calculation of scattering with a relatively high accuracy; however, the accuracy of SSS is dependent on the accuracy of tail fitting to find a correct scaling factor, which is often difficult in low photon count measurements. To overcome this drawback as well as to improve accuracy of scatter estimation by incorporating multiple scattering contribution, we propose a multiple scatter simulation (MSS) based on a simplified Monte Carlo (MC) simulation that considers photon migration and interactions due to photoelectric absorption and Compton scattering. Unlike the SSS, the MSS calculates a scaling factor by comparing simulated prompt data with the measured data in the whole volume, which enables a more robust estimation of a scaling factor. Even though the proposed MSS is based on MC, a significant acceleration of the computational time is possible by using a virtual detector array with a larger pitch by exploiting that the scatter distribution varies slowly in spatial domain. Furthermore, our MSS implementation is nicely fit to a parallel implementation using graphic processor unit (GPU). In particular, we exploit a hybrid CPU-GPU technique using the open multiprocessing and the compute unified device architecture, which results in 128.3 times faster than using a single CPU. Overall, the computational time of MSS is 9.4 s for a high-resolution research tomograph (HRRT) system. The performance of the proposed MSS is validated through actual experiments using an HRRT.

BACKGROUND: Hypertension may increase tortuosity or twistedness of arteries. We applied a centerline extraction algorithm and tortuosity metric to magnetic resonance angiography (MRA) brain images to quantitatively measure the tortuosity of arterial vessel centerlines. The most commonly used arterial tortuosity measure is the distance factor metric (DFM). This study tested a DFM based measurement's ability to detect increases in arterial tortuosity of hypertensives using existing images. Existing images presented challenges such as different resolutions which may affect the tortuosity measurement, different depths of the area imaged, and different artifacts of imaging that require filtering. METHODS: The stability and accuracy of alternative centerline algorithms was validated in numerically generated models and test brain MRA data. Existing images were gathered from previous studies and clinical medical systems by manually reading electronic medical records to identify hypertensives and negatives. Images of different resolutions were interpolated to similar resolutions. Arterial tortuosity in MRA images was measured from a DFM curve and tested on numerically generated models as well as MRA images from two hypertensive and three negative control populations. Comparisons were made between different resolutions, different filters, hypertensives versus negatives, and different negative controls. RESULTS: In tests using numerical models of a simple helix, the measured tortuosity increased as expected with more tightly coiled helices. Interpolation reduced resolution-dependent differences in measured tortuosity. The Korean hypertensive population had significantly higher arterial tortuosity than its corresponding negative control population across multiple arteries. In addition one negative control population of different ethnicity had significantly less arterial tortuosity than the other two. CONCLUSIONS: Tortuosity can be compared between images of different resolutions by interpolating from lower to higher resolutions. Use of a universal negative control was not possible in this study. The method described here detected elevated arterial tortuosity in a hypertensive population compared to the negative control population and can be used to study this relation in other populations.

Unlike computed tomographic images and magnetic resonance images (MRIs), sectioned images of the human body with real color and high resolution have certain advantages in learning and teaching anatomy. Comparisons between sectioned images of the brain and MRIs are useful in many ways. Therefore, we prepared 312 MRIs at ultrahigh field 7.0 T (axial direction 0.4 times 0.4 times 0.4 mm3voxel size) of a cadaver brain, 2343 sectioned images (axial direction, 0.1 mm intervals, 0.1 times 0.1 mm2pixel size, and 48 bits color) by serial-sectioning the cadaver head, 234 segmented images in which brain regions were separately delineated (1 mm intervals and 0.1 times 0.1 mm2pixel size) by outlining 64 head structures in sectioned images. Three-dimensional images of 64 head structures were made by volume reconstruction from sectioned images. In this research, advanced techniques and equipment enabled us to prepare quality 7.0-T MRIs, sectioned images, and segmented images of the head. These images are expected to contribute to our understanding of the topographic neuroanatomy of the head and to aid interpretations of MRIs and CTs of the human brain.