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[This corrects the article DOI 10.1117/1.JMI.7.4.044001.].Purpose Current phantoms used for the dose reconstruction of long-term childhood cancer survivors lack individualization. We design a method to predict highly individualized abdominal three-dimensional (3-D) phantoms automatically. Approach We train machine learning (ML) models to map (2-D) patient features to 3-D organ-at-risk (OAR) metrics upon a database of 60 pediatric abdominal computed tomographies with liver and spleen segmentations. Next, we use the models in an automatic pipeline that outputs a personalized phantom given the patient's features, by assembling 3-D imaging from the database. A step to improve phantom realism (i.e., avoid OAR overlap) is included. We compare five ML algorithms, in terms of predicting OAR left-right (LR), anterior-posterior (AP), inferior-superior (IS) positions, and surface Dice-Sørensen coefficient (sDSC). Furthermore, two existing human-designed phantom construction criteria and two additional control methods are investigated for comparison. Results Different ML algorithms result in similar test mean absolute errors ∼ 8    mm for liver LR, IS, and spleen AP, IS; ∼ 5    mm for liver AP and spleen LR; ∼ 80 % for abdomen sDSC; and ∼ 60 % to 65% for liver and spleen sDSC. One ML algorithm (GP-GOMEA) significantly performs the best for 6/9 metrics. The control methods and the human-designed criteria in particular perform generally worse, sometimes substantially ( + 5 - mm error for spleen IS, - 10 % sDSC for liver). The automatic step to improve realism generally results in limited metric accuracy loss, but fails in one case (out of 60). Conclusion Our ML-based pipeline leads to phantoms that are significantly and substantially more individualized than currently used human-designed criteria.Purpose Visual search using volumetric images is becoming the standard in medical imaging. However, we do not fully understand how eye movement strategies mediate diagnostic performance. A recent study on computed tomography (CT) images showed that the search strategies of radiologists could be classified based on saccade amplitudes and cross-quadrant eye movements [eye movement index (EMI)] into two categories drillers and scanners. Approach We investigate how the number of times a radiologist scrolls in a given direction during analysis of the images (number of courses) could add a supplementary variable to use to characterize search strategies. [https://www.selleckchem.com/products/actinomycin-d.html Dactinomycin activator] We used a set of 15 normal liver CT images in which we inserted 1 to 5 hypodense metastases of two different signal contrast amplitudes. Twenty radiologists were asked to search for the metastases while their eye-gaze was recorded by an eye-tracker device (EyeLink1000, SR Research Ltd., Mississauga, Ontario, Canada). Results We found that categorizing radiologists based on the number of courses (rather than EMI) could better predict differences in decision times, percentage of image covered, and search error rates. Radiologists with a larger number of courses covered more volume in more time, found more metastases, and made fewer search errors than those with a lower number of courses. Our results suggest that the traditional definition of drillers and scanners could be expanded to include scrolling behavior. Drillers could be defined as scrolling back and forth through the image stack, each time exploring a different area on each image (low EMI and high number of courses). Scanners could be defined as scrolling progressively through the stack of images and focusing on different areas within each image slice (high EMI and low number of courses). Conclusions Together, our results further enhance the understanding of how radiologists investigate three-dimensional volumes and may improve how to teach effective reading strategies to radiology residents.Significance Stem cell therapies are of interest for treating a variety of neurodegenerative diseases and injuries of the spinal cord. However, the lack of techniques for longitudinal monitoring of stem cell therapy progression is inhibiting clinical translation. Aim The goal of this study is to demonstrate an intraoperative imaging approach to guide stem cell injection to the spinal cord in vivo. Results may ultimately support the development of an imaging tool that spans intra- or postoperative environments to guide therapy throughout treatment. Approach Stem cells were labeled with Prussian blue nanocubes (PBNCs) to facilitate combined ultrasound and photoacoustic (US/PA) imaging to visualize stem cell injection and delivery to the spinal cord in vivo. US/PA results were confirmed by magnetic resonance imaging (MRI) and histology. Results Real-time intraoperative US/PA image-guided injection of PBNC-labeled stem cells and three-dimensional volumetric images of injection provided feedback necessary for successful delivery of therapeutics into the spinal cord. Postoperative MRI confirmed delivery of PBNC-labeled stem cells. Conclusions The nanoparticle-augmented US/PA approach successfully detected injection and delivery of stem cells into the spinal cord, confirmed by MRI. Our work demonstrated in vivo feasibility, which is a critical step toward the development of a US/PA/MRI platform to monitor regenerative spinal cord therapies.<br /><br /> Clinical manifestation and neonatal outcomes of pregnant women with coronavirus disease 2019 (COVID-19) were unclear in Wuhan, China.<br /><br /> We retrospectively analyzed clinical characteristics of pregnant and nonpregnant women with COVID-19 aged from 20 to 40, admitted between January 15 and March 15, 2020 at Union Hospital, Wuhan, and symptoms of pregnant women with COVID-19 and compared the clinical characteristics and symptoms to historic data previously reported for H1N1.<br /><br /> Among 64 patients, 34 (53.13%) were pregnant, with higher proportion of exposure history (29.41% vs 6.67%) and more pulmonary infiltration on computed tomography test (50% vs 10%) compared to nonpregnant women. Of pregnant patients, 27 (79.41%) completed pregnancy, 5 (14.71%) had natural delivery, 18 (52.94%) had cesarean section, and 4 (11.76%) had abortion; 5 (14.71%) patients were asymptomatic. All 23 newborns had negative reverse-transcription polymerase chain results, and an average 1-minute Apgar score was 8-9 points. Pregnant and nonpregnant patients show differences in symptoms such as fever, expectoration, and fatigue and on laboratory tests such as neurophils, fibrinogen, D-dimer, and erythrocyte sedimentation rate.