Search Results (4)

A pathogenic mutation in presenilin-1 (PSEN1), His214Asn, was found in a male patient with memory decline at the age of 41 in Korea for the first time. The proband patient was associated with a positive family history from his father, paternal aunt, and paternal grandmother without genetic testing. He was diagnosed with early onset Alzheimer’s disease (EOAD). PSEN1 His214Asn was initially reported in an Italian family, where the patient developed phenotypes similar to the current proband patient. Magnetic resonance imaging (MRI) scans revealed a mild hippocampal atrophy. The amyloid positron emission tomography (amyloid-PET) was positive, along with the positive test results of the increased amyloid ß (Aβ) oligomerization tendency with blood. The PSEN1 His214 amino acid position plays a significant role in the gamma–secretase function, especially from three additional reported mutations in this residue: His214Asp, His214Tyr, and His214Arg. The structure prediction model revealed that PSEN1 protein His214 may interact with Trp215 of His-Trp cation-π interaction, and the mutations of His214 would destroy this interaction. The His-Trp cation-π interaction between His214 and Trp215 would play a crucial structural role in stabilizing the 4th transmembrane domain of PSEN1 protein, especially when aromatic residues were often reported in the membrane interface of the lipid–extracellular region of alpha helices or beta sheets. The His214Asn would alter the cleavage dynamics of gamma–secretase from the disappeared interactions between His214 and Trp215 inside of the helix, resulting in elevated amyloid production. Hence, the increased Aβ was reflected in the increased Aβ oligomerization tendency and the accumulations of Aβ in the brain from amyloid-PET, leading to EOAD. Full article

Gamma imagers play a key role in both industrial and medical applications. Modern gamma imagers typically employ iterative reconstruction methods in which the system matrix (SM) is a key component to obtain high-quality images. An accurate SM could be acquired from an experimental calibration step with a point source across the FOV, but at a cost of long calibration time to suppress noise, posing challenges to real-world applications. In this work, we propose a time-efficient SM calibration approach for a 4π-view gamma imager with short-time measured SM and deep-learning-based denoising. The key steps include decomposing the SM into multiple detector response function (DRF) images, categorizing DRFs into multiple groups with a self-adaptive K-means clustering method to address sensitivity discrepancy, and independently training separate denoising deep networks for each DRF group. We investigate two denoising networks and compare them against a conventional Gaussian filtering method. The results demonstrate that the denoised SM with deep networks faithfully yields a comparable imaging performance with the long-time measured SM. The SM calibration time is reduced from 1.4 h to 8 min. We conclude that the proposed SM denoising approach is promising and effective in enhancing the productivity of the 4π-view gamma imager, and it is also generally applicable to other imaging systems that require an experimental calibration step. Full article

(1) Background: Gamma cameras have wide applications in industry, including nuclear power plant monitoring, emergency response, and homeland security. The desirable properties of a gamma camera include small weight, good resolution, large field of view (FOV), and wide imageable source energy range. Compton cameras can have a 4π FOV but have limited sensitivity at low energy. Coded-aperture gamma cameras are operatable at a wide photon energy range but typically have a limited FOV and increased weight due to the thick heavy metal collimators and shielding. In our lab, we previously proposed a 4π-view gamma imaging approach with a 3D position-sensitive detector, with which each detector element acts as the collimator for other detector elements. We presented promising imaging performance for ^99mTc, ¹⁸F, and ¹³⁷Cs sources. However, the imaging performance for middle- and high-energy sources requires further improvement. (2) Methods: In this study, we present a new gamma camera design to achieve satisfactory imaging performance in a wide gamma energy range. The proposed gamma camera consists of interspaced bar-shaped GAGG (Ce) crystals and tungsten absorbers. The metal bars enhance collimation for high-energy gamma photons without sacrificing the FOV. We assembled a gamma camera prototype and conducted experiments to evaluate the gamma camera’s performance for imaging ⁵⁷Co, ¹³⁷Cs, and ⁶⁰Co point sources. (3) Results: Results show that the proposed gamma camera achieves a positioning accuracy of <3° for all gamma energies. It can clearly resolve two ¹³⁷Cs point sources with 10° separation, two ⁵⁷Co and two ⁶⁰Co point sources with 20° separation, as well as a 2 × 3 ¹³⁷Cs point-source array with 20° separation. (4) Conclusions: We conclude that the proposed gamma camera design has comprehensive merits, including portability, 4π-view FOV, and good angular resolution across a wide energy range. The presented approach has promising potential in nuclear security applications. Full article

We developed a path-planning system for radiation source identification devices using 4π gamma imaging. The estimated source location and activity were calculated by an integrated simulation model by using 4π gamma images at multiple measurement positions. Using these calculated values, a prediction model to estimate the probability of identification at the next measurement position was created by via random forest analysis. The path-planning system based on the prediction model was verified by integrated simulation and experiment for a ¹³⁷Cs point source. The results showed that ¹³⁷Cs point sources were identified using the few measurement positions suggested by the path-planning system. Full article