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This paper demonstrates a super-resolution method for improving the resolution in clinical positron emission tomography (PET) scanners. Super-resolution images were obtained by combining four data sets with spatial shifts between consecutive acquisitions and applying an iterative algorithm. Super-resolution attenuation corrected PET scans of a phantom were obtained using the two-dimensional and three-dimensional (3-D) acquisition modes of a clinical PET/computed tomography (CT) scanner (Discovery LS, GEMS). In a patient study, following a standard [sup.18]F--FDG PET/CT scan, a super-resolution scan around one small lesion was performed using axial shifts without increasing the patient radiation exposure. In the phantom study, smaller features (3 mm) could be resolved axially with the super-resolution method than without (6 mm). The super-resolution images had better resolution than the original images and provided higher contrast ratios in coronal images and in 3-D acquisition transaxial images. The coronal super-resolution images had superior resolution and contrast ratios compared to images reconstructed by merely interleaving the data to the proper axial location. In the patient study, super-resolution reconstructions displayed a more localized [sup.18]F--FDG uptake. A new approach for improving the resolution of PET images using a super-resolution method has been developed and experimentally confirmed, employing a clinical scanner. The improvement in axial resolution requires no changes in hardware. Index Terms--Image resolution, oncology, PET, super-resolution, 18F--FDG.

We show an iterative reconstruction framework for diffraction ultrasound tomography. The use of broad-band illumination allows significant reduction of the number of projections compared to straight ray tomography. The proposed algorithm makes use of forward nonuniform fast Fourier transform (NUFFT) for iterative Fourier inversion. Incorporation of total variation regularization allows the reduction of noise and Gibbs phenomena while preserving the edges. The complexity of the NUFFT-based reconstruction is comparable to the frequency-domain interpolation (gridding) algorithm, whereas the reconstruction accuracy (in sense of the [L.sup.2] and the [L.sup.[infinity]] norm) is better. Index Terms--Acoustic diffraction tomography, image reconstruction, nonuniform fast Fourier transform (NUFFT).

Many methods for mapping ischemic myocardial regions by functional analysis have been suggested. However, the complicated relationship between myocardial function and perfusion, and the inherent limitations of the imaging techniques used, have led to a generally low mapping accuracy. We show herein, that highly accurate mapping can be obtained by combining tagged magnetic resonance imaging (MRI), three-dimensional (3-D) analysis, and artificial neural networks. Nine canine hearts with acute ischemia were studied using multiplanar tagged MRI. Twenty-four myocardial cuboids were tagged in each heart and reconstructed in 3-D at end diastole (ED) and end systole (ES). The cuboids were arranged in three slices approximately 1 cm thick and covered most of the left ventricle (LV). Transmural thickening and endocardial area strain were calculated for each cuboid. Applying a post-mortem (PM) analysis, the percent ischemia in each cuboid was estimated using monastral blue dye; the PM analysis served as a 'gold standard.' An artificial neural network (ANN), designed to estimate the percent ischemia in each cuboid from the functional indexes, was then created. The ANN 'learned' the function-ischemia relationship in 192 cuboids taken from eight of the hearts and was asked to estimate the percent ischemia in the 24 cuboids of the ninth heart. The process was repeated nine times, each time using a different heart as test case. The average accuracy of mapping, i.e., the accuracy with which the ANN has mapped the normal and ischemic cuboids using the functional parameters, was 87.5 % [+ or -] 7.8 (s.d.). This accuracy was superior to the accuracy obtained by optimal thresholding of the same thickening (80.1%) and endocardial strain (76.9%) data.

Thermal monitoring during focused ultrasound (FUS) transcranial procedures is mandatory and commonly performed by MRI. Transcranial ultrasonic thermal monitoring is an attractive alternative. Furthermore, using the therapeutic FUS transducer itself for this task is highly desirable. Nonetheless, such application is challenged by massive skull-induced signal attenuation and aberrations. This study examined the feasibility of implementing the Golay-coded excitations (CoE) for temperature monitoring in bovine brain samples in the range of 35 °C–43 °C (hyperthermia). Feasibility was assessed using computer simulations, water-based phantoms, and ex vivo bovine brain white-matter samples. The samples were gradually heated to about 45 °C and sonicated during cool down with a 1-MHz therapeutic FUS implementing Golay CoE. Initially, a calibration curve correlating the normalized time-of-flight (TOF) changes and the temperature was generated. Next, a bovine bone was positioned between the FUS and the brain samples, and the scanning process was repeated for different fresh samples. The calibration curve was then used as a mean for estimating the temperature, which was compared to thermocouple measurements. The simulations demonstrated a substantial improvement in signal-to-noise ratio (SNR) and suggested that the implementation of 4-bit sequences is advantageous. The experimental measurements with bone demonstrated good temperature estimation with an average absolute error for the water phantoms and brains of 1.46 °C ± 1.22 °C and 1.23 °C ± 0.99 °C, respectively. In conclusion, a novel noninvasive method utilizing the Golay CoE for ultrasonic thermal monitoring using a therapeutic FUS transducer is introduced. This method can lead to the development of an acoustic tool for brain thermal monitoring. [ABSTRACT FROM AUTHOR]