Magnetic nanofibrous scaffolds of poly(caprolactone) (PCL) incorporating magnetic nanoparticles (MNP) were produced, and their effects on physico-chemical, mechanical and biological properties were extensively addressed to find efficacy for bone regeneration purpose. MNPs 12 nm in diameter were citrated and evenly distributed in PCL solutions up to 20% and then were electrospun into nonwoven nanofibrous webs. Incorporation of MNPs greatly improved the hydrophilicity of the nanofibers. Tensile mechanical properties of the nanofibers (tensile strength, yield strength, elastic modulus and elongation) were significantly enhanced with the addition of MNPs up to 15%. In particular, the tensile strength increase was as high as ∼25 MPa at 15% MNPs vs. ∼10 MPa in pure PCL. PCL-MNP nanofibers exhibited magnetic behaviors, with a high saturation point and hysteresis loop area, which increased gradually with MNP content. The incorporation of MNPs substantially increased the degradation of the nanofibers, with a weight loss of ∼20% in pure PCL, ∼45% in 10% MNPs and ∼60% in 20% MNPs. Apatite forming ability of the nanofibers tested in vitro in simulated body fluid confirmed the substantial improvement gained by the addition of MNPs. Osteoblastic cells favored the MNPs-incorporated nanofibers with significantly improved initial cell adhesion and subsequent penetration through the nanofibers, compared to pure PCL. Alkaline phosphatase activity and expression of genes associated with bone (collagen I, osteopontin and bone sialoprotein) were significantly up-regulated in cells cultured on PCL-MNP nanofibers than those on pure PCL. PCL-MNP nanofibers subcutaneously implanted in rats exhibited minimal adverse tissue reactions, while inducing substantial neoblood vessel formation, which however, greatly limited in pure PCL. In vivo study in radial segmental defects also signified the bone regeneration ability of the PCL-MNP nanofibrous scaffolds. The magnetic, bone-bioactive, mechanical, cellular and tissue attributes of MNP-incorporated PCL nanofibers make them promising candidate scaffolds for bone regeneration.

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For attenuation correction (AC) in PET/MRI systems, segmentation-based methods are most often used. However, the standardized uptake value (SUV) of lesions in the bone and liver, which have higher attenuation coefficients than other organs, can be underestimated, potentially leading to misinterpretation of clinical cases. Errors in SUV estimation are also dependent on the segmentation schemes used in the segmentation-based AC. In this study, this potential bias in SUV estimation using 4 different segmentation-based AC methods was evaluated for the PET/CT data of cancer patients with bone and liver lesions. Methods: Forty patients who had spine or liver lesions and underwent 18 F-FDG PET/CT participated (18 women and 22 men; 20 spine lesions and 20 liver lesions; mean age (6SD), 60.5 6 11.4 y; mean body weight, 57.7 6 10.4 kg). The patient body region was extracted from the CT image and categorized into 5 tissue groups (air, lungs, fat, water, and bone) using Hounsfield unit thresholds, which were determined from the CT histogram. Four segmentation-based AC methods (SLA [soft-tissue/lung/ air], WFLA [water/fat/lung/air], SLAB [soft-tissue/lung/air/bone], and WFLAB [water/fat/lung/air/bone]) were compared with CTbased AC. The mean attenuation coefficient for each group was calculated from 40 CT images and assigned to the attenuation maps. PET sinograms were reconstructed using segmentationand CT-based AC maps, and mean SUV in the lesions was compared. Results: Mean attenuation coefficients for air, lungs, fat, water, and bone were 0.0058, 0.0349, 0.0895, 0.0987, and 0.1178 cm 21 , respectively. In the spine lesions, the SUVs were underestimated by 16.4% 6 8.5% (SLA AC) and 14.7% 6 7.5% (WFLA AC) but not to a statistically significant extent for SLAB and WFLAB AC relative to CT AC. In the liver lesions, the SUVs were underestimated by 11.1% 6 2.6%, 8.1% 6 3.0%, 6.8% 6 3.8%, and 4.1% 6 3.8% with SLA, SLAB, WFLA, and WFLAB AC, respectively. Conclusion: Without bone segmentation, the SUVs of spine lesions were considerably underestimated; however, the bias was acceptable with bone segmentation. In liver lesions, the segmentation-based AC methods yielded a negative bias in SUV; however, inclusion of the bone and fat segments reduced the SUV bias. The results of this study will be useful for understanding organ-dependent bias in SUV between PET/CT and PET/MRI. Hybri d imaging devices such as PET/CT and SPECT/CT are now widely used in clinical and preclinical studies. Morphologic information provided by CT is particularly useful for the localization of abnormal uptake of radiotracers and for g-ray attenuation correction (AC) (1-3). Inaccurate AC of PET and SPECT images can lead to incorrect quantification and misinterpretation of lesions (4-9).MRI is another important morphologic imaging tool that can be combined with PET or SPECT. MRI has several advantages over CT, including better soft-tissue contrast, no additional radiation hazard, and true 4-dimensional simultaneous imaging (9-11). It is likely that combined PET/MR...

Personalized dosimetry with high accuracy is crucial owing to the growing interests in personalized medicine. The direct Monte Carlo simulation is considered as a state-of-art voxel-based dosimetry technique; however, it incurs an excessive computational cost and time. To overcome the limitations of the direct Monte Carlo approach, we propose using a deep convolutional neural network (CNN) for the voxel dose prediction. PET and CT image patches were used as inputs for the CNN with the given ground truth from direct Monte Carlo. The predicted voxel dose rate maps from the CNN were compared with the ground truth and dose rate maps generated voxel S-value (VSV) kernel convolution method, which is one of the common voxel-based dosimetry techniques. The CNN-based dose rate map agreed well with the ground truth with voxel dose rate errors of 2.54% ± 2.09%. The VSV kernel approach showed a voxel error of 9.97% ± 1.79%. In the whole-body dosimetry study, the average organ absorbed dose errors were 1.07%, 9.43%, and 34.22% for the CNN, VSV, and OLINDA/EXM dosimetry software, respectively. The proposed CNN-based dosimetry method showed improvements compared to the conventional dosimetry approaches and showed results comparable with that of the direct Monte Carlo simulation with significantly lower calculation time.