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Remote sensing offers a low-cost method for estimating yields at large spatio-temporal scales. Here, we examined the ability of Sentinel-2 satellite imagery to map field-level maize yields across smallholder farms in two regions in Oromia district, Ethiopia. We evaluated how effectively different indices, the MTCI, GCVI, and NDVI, and different models, linear regression and random forest regression, can be used to map field-level yields. We also examined if models improved by adding weather and soil data and how generalizable our models were if trained in one region and applied to another region, where no data were used for model calibration. We found that random forest regression models that used monthly MTCI composites led to the highest yield prediction accuracies (R2 up to 0.63), particularly when using only localized data for training the model. These models were not very generalizable, especially when applied to regions that had significant haze remaining in the imagery. We also found that adding soil and weather data did little to improve model fit. Our results highlight the ability of Sentinel-2 imagery to map field-level yields in smallholder systems, though accuracies are limited in regions with high cloud cover and haze. [ABSTRACT FROM AUTHOR]

Monitoring smallholder agricultural productivity growth, one of the targets of the Sustainable Development Goals, rests on accurate measures of crop production and land area. Existing methods and protocols for measuring smallholder production and plot size are prone to various sources and forms of mismeasurement. Inaccuracies in production and land area measurement are likely to distort descriptive and predictive inferences. We examine the sensitivity of empirical assessments of the relationship between agricultural productivity and land area to alternative measurement protocols. We implement six production and six land area measurement protocols, and show that most of these protocols differ systematically in their accuracy. We find that an apparent inverse size–productivity relationship in our data is fully explained by measurement error in both production and plot size. Moreover, we show that some of the previously used "gold standard" measures are themselves prone to nonclassical measurement error, and hence can generate spurious inverse size–productivity findings. Our results also show that slight improvements in the precision of objective measures significantly reduce the inferential bias associated with the size–productivity relationship. [ABSTRACT FROM AUTHOR]

Soil acidity is a major constraint to crop production in tropical regions. Although agricultural lime is one option to remediate acid soils, there is limited information on the potential returns on investments to liming by smallholders. Using survey data collected from 261 households in Rwanda, we estimated the crop-specific yield response to lime application and associated financial benefits. The estimated average yield gain from lime ranged from 941 kg/ha to 1 579 kg/ha for Irish potato, 562 kg/ha to 709 kg/ha for maize, and 453 kg/ha to 520 kg/ha for beans. With the existing lime and farmgate crop prices, reliable returns on investment from lime were observed for Irish potato, while applying lime to maize and bean was only profitable at a 50% lime price subsidy. As maize and beans are the major staple crops in Rwanda, the subsidy for ag-lime use in improving crop productivity is highly justifiable. The results inform policy decisions in considering market-oriented crops and subsidies when promoting agricultural lime in acid soils under smallholder conditions. [ABSTRACT FROM AUTHOR]

Rationale and Objectives: Misregistration artifacts between the PET and attenuation correction CT (CTAC) exams can degrade image quality and cause diagnostic errors. Deep learning (DL)-warped elastic registration methods have been proposed to improve misregistration errors., Materials and Methods: 30 patients undergoing routine oncologic examination (20 18 F-FDG PET/CT and 10 64 Cu-DOTATATE PET/CT) were retrospectively identified and compared using unmodified CTAC, and a DL-augmented spatial transformation CT attenuation map. Primary endpoints included differences in subjective image quality and standardized uptake values (SUV). Exams were randomized to reduce reader bias, and three radiologists rated image quality across six anatomic sites using a modified Likert scale. Measures of local bias and lesion SUV were also quantitatively evaluated., Results: The DL attenuation correction methods were associated with higher image quality and reduced misregistration artifacts (Mean 18 F-FDG quality rating=3.5-3.8 for DL vs 3.2-3.5 for standard reconstruction (STD); Mean 64 Cu-DOTATATE quality rating= 3.2-3.4 for DL vs 2.1-3.3; P < 0.05 for STD, for all except 64 Cu-DOTATATE inferior spleen). Percent change in superior liver SUV mean for 18 F-FDG and 64 Cu-DOTATATE were 5.3 ± 4.9 and 8.2 ± 4.1%, respectively. Measures of signal-to-noise ratio were significantly improved for the DL over STD (Hepatopulmonary index (HPI) [ 18 F-FDG] = 4.5 ± 1.2 vs 4.0 ± 1.1, P < 0.001; HPI [ 64 Cu-DOTATATE] = 16.4 ± 16.9 vs 12.5 ± 5.5, P = 0.039)., Conclusion: Deep learning elastic registration for CT attenuation correction maps on routine oncology PET/CT decreases misregistration artifacts, with a greater impact on PET scans with longer acquisition times., Competing Interests: Declaration of Competing Interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Joshua Schaefferkoetter reports a relationship with Siemens Medical Solutions USA Inc that includes: employment. James Hamill reports a relationship with Siemens Medical Solutions USA Inc that includes: employment. Jim O Doherty reports a relationship with Siemens Medical Solutions USA Inc that includes: employment. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.)

Drug-induced acute lung injury is a significant yet often underrecognized clinical challenge, associated with a wide range of therapeutic agents, including chemotherapy drugs, antibiotics, anti-inflammatory drugs, and immunotherapies. This comprehensive review examines the pathophysiology, clinical manifestations, and radiologic findings of drug-induced acute lung injury across different drug categories. Common imaging findings are highlighted to aid radiologists and clinicians in early recognition and diagnosis. The review emphasizes the importance of immediate cessation of the offending drug and supportive care, which may include corticosteroids. Understanding these patterns is crucial for prompt diagnosis and management, potentially improving patient outcomes., Competing Interests: The authors declare no conflict of interest., (Copyright © 2024 Wolters Kluwer Health, Inc. All rights reserved.)

Purpose: The aim of this study was to find the optimal strength level of QIR for ultra-high-resolution (UHR) PCCT of the lung., Materials and Methods: This retrospective study included 24 patients who had unenhanced chest CT with the novel UHR scan protocol on the PCCT scanner between March 24, 2023 and May 18, 2023. Two sets of reconstructions were made using different slice thicknesses: standard resolution (SR, 1 mm) and ultra-high-resolution (UHR, 0.2 mm), reconstructed with all strength levels of QIR (0 to 4). Attenuation of the lung parenchyma, noise, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) were assessed as objective criteria of image quality. Two fellowship-trained radiologists compared image quality and noise level, sharpness of the images, and the airway details using a 5-point Likert scale. Wilcoxon signed-rank test was used for statistical analysis of reader scores, and one-way repeated measures analysis of variance for comparing the objective image quality scores., Results: Objective image quality linearly improved with higher strength levels of QIR, reducing image noise by 66% from QIR-0 to QIR-4 (P<0.001). Subjective image noise was best for QIR-4 (P<0.001). Readers rated QIR-1 and QIR-2 best for SR, and QIR-2 and QIR-3 best for UHR in terms of subjective image sharpness and airway detail, without significant differences between them (P=0.48 and 0.56, respectively)., Conclusions: Higher levels of QIR provided excellent objective image quality, but readers' preference was for intermediate levels. Considering all metrics, we recommend QIR-3 for ultra-high-resolution PCCT of the lung., Competing Interests: J.O. was an employee of Siemens Medical Solutions USA Inc. U.J.S. received institutional research support and/or personal fees from Bayer, Bracco, Elucid Bioimaging, Guerbet, HeartFlow, Keya Medical, and Siemens. The remaining authors declare no conflict of interest., (Copyright © 2024 Wolters Kluwer Health, Inc. All rights reserved.)

Objective: The purpose of this project was to develop a formal visual arts training curriculum and evaluate if there was improvement in the observational and descriptive skills of first- and second-year medical students for radiologic images.  Materials and methods: A demographic survey and an initial pre-test of 12 radiologic images were administered asking an open-ended question to describe the image and to identify the abnormality in their own words. Three virtual one-hour sessions of visual thinking strategy (VTS) training occurred, and an immediate post-test and a six-month post-test were administered, each with images different from the pre-test, as well as a final questionnaire. All tests were independently graded by two graders with a previously established grading rubric. Differences in scores were analyzed using paired T-tests., Results: Thirty-nine medical students participated. The mean pre-test score was 62.2 +/- 18.6, and the mean post-test score improved by 41.7 +/- 17.9 points (p<0.01) to an average score of 103.9 +/- 20.4. Nine participants were lost to follow-up at six months, and the average six-month post-test score was 110.2 +/- 29.1 for a mean improvement of 9.3 +/- 13.1 points (p=0.320) from the initial post-test.  Conclusion: There was a significant improvement in observational and descriptive skills in first- and second-year medical students when describing radiologic images, which was retained after six months. A formal VTS curriculum could play a beneficial role in medical student and radiology training programs not only to improve observational skills but also to address perceptual errors in diagnostic imaging., Competing Interests: Human subjects: Consent was obtained or waived by all participants in this study. Medical University of South Carolina (MUSC) Institutional Review Board (IRB) issued approval Pro00086970. Animal subjects: All authors have confirmed that this study did not involve animal subjects or tissue. Conflicts of interest: In compliance with the ICMJE uniform disclosure form, all authors declare the following: Payment/services info: This study was funded by an internal MUSC Scholars of Humanities and Arts Research and Education (SHARE) Grant. . Financial relationships: All authors have declared that they have no financial relationships at present or within the previous three years with any organizations that might have an interest in the submitted work. Other relationships: All authors have declared that there are no other relationships or activities that could appear to have influenced the submitted work., (Copyright © 2024, Wulfeck et al.)