Sinogram Affirmed Iterative Reconstruction-enabled reconstruction provides abdominal CT images without loss in diagnostic value at 50% reduced dose and in some patients also at 75% reduced dose.

OBJECTIVE. The purpose of this study is to compare sinogram-affirmed iterative reconstruction (SAFIRE) and filtered back projection (FBP) reconstruction of chest CT acquired with 65% radiation dose reduction. MATERIALS AND METHODS. In this prospective study involving 24 patients (11 women and 13 men; mean [± SD] age, 66 ± 10 years), two scan series were acquired using 100 and 40 Quality Reference mAs over a 10-cm scan length in the chest with a 128-MDCT scanner. The 40 Quality Reference mAs CT projection data were reconstructed with FBP and four settings of SAFIRE (S1, S2, S3, and S4). Six image datasets (FBP with 100 and 40 Quality Reference mAs, and S1, S2, S3, S4 with 40 Quality Reference mAs) were displayed on a DICOM-compliant 55-inch 2-megapixel monitor for blinded evaluation by two thoracic radiologists for number and location of lesions, lesion size, lesion margins, visibility of small structures and fissures, and diagnostic confidence. Objective noise and CT values were measured in thoracic aorta for each image series, and the noise power spectrum was assessed. Data were analyzed with analysis of variance and Wilcoxon signed rank tests. RESULTS. All 186 lesions were seen on 40 Quality Reference mAs SAFIRE images. Diagnostic confidence on SAFIRE images was higher than that for FBP images. Except for the minor blotchy appearance on SAFIRE settings S3 and S4, no significant artifacts were noted. Objective noise with 40 Quality Reference mAs S1 images (21.1 ± 6.1 SD of HU) was significantly lower than that for 40 Quality Reference mAs FBP images (28.5 ± 8.1 SD of HU) (p < 0.001). Noise power spectra were identical for SAFIRE and FBP with progressive noise reduction with higher iteration SAFIRE settings. CONCLUSION. Iterative reconstruction (SAFIRE) allows reducing the radiation exposure by approximately 65% without losing diagnostic information in chest CT.

In elderly women with low-energy distal radius fractures, an association between IGF1 and lowest measures of BMD was found, indicating that low IGF1 could be an indirect risk factor for fractures. Fracture healing was associated with initial leukocytosis and a lower thrombocyte count, suggesting that inflammation and thrombocytes are important components in fracture healing.

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Artificial intelligence (AI) holds much promise for enabling highly desired imaging diagnostics improvements. One of the most limiting bottlenecks for the development of useful clinical-grade AI models is the lack of training data. One aspect is the large amount of cases needed and another is the necessity of high-quality ground truth annotation. The aim of the project was to establish and describe the construction of a database with substantial amounts of detail-annotated oncology imaging data from pathology and radiology. A specific objective was to be proactive, that is, to support undefined subsequent AI training across a wide range of tasks, such as detection, quantification, segmentation, and classification, which puts particular focus on the quality and generality of the annotations. The main outcome of this project was the database as such, with a collection of labeled image data from breast, ovary, skin, colon, skeleton, and liver. In addition, this effort also served as an exploration of best practices for further scalability of high-quality image collections, and a main contribution of the study was generic lessons learned regarding how to successfully organize efforts to construct medical imaging databases for AI training, summarized as eight guiding principles covering team, process, and execution aspects.

Background and purpose — A correct diagnosis is essential for the appropriate treatment of patients with atypical femoral fractures (AFFs). The diagnostic accuracy of radiographs with standard radiology reports is very poor. We derived a diagnostic algorithm that uses deep neural networks to enable clinicians to discriminate AFFs from normal femur fractures (NFFs) on conventional radiographs. Patients and methods — We entered 433 radiographs from 149 patients with complete AFF and 549 radiographs from 224 patients with NFF into a convolutional neural network (CNN) that acts as a core classifier in an automated pathway and a manual intervention pathway (manual improvement of image orientation). We tested several deep neural network structures (i.e., VGG19, InceptionV3, and ResNet) to identify the network with the highest diagnostic accuracy for distinguishing AFF from NFF. We applied a transfer learning technique and used 5-fold cross-validation and class activation mapping to evaluate the diagnostic accuracy. Results — In the automated pathway, ResNet50 had the highest diagnostic accuracy, with a mean of 91% (SD 1.3), as compared with 83% (SD 1.6) for VGG19, and 89% (SD 2.5) for InceptionV3. The corresponding accuracy levels for the intervention pathway were 94% (SD 2.0), 92% (2.7), and 93% (3.7), respectively. With regards to sensitivity and specificity, ResNet outperformed the other networks with a mean AUC (area under the curve) value of 0.94 (SD 0.01) and surpassed the accuracy of clinical diagnostics. Interpretation — Artificial intelligence systems show excellent diagnostic accuracies for the rare fracture type of AFF in an experimental setting.

Background: We aimed to compare two volumetric bone mineral density (vBMD) analysis programs, regarding (I) agreement of vBMD values based on mono-and dual-energy computed tomography (MECT and DECT) scans and (II) suitability for analyzing DECT data obtained at different energies. Methods:We retrospectively analyzed two abdominal CT datasets: one performed in a MECT scan (vertebrae L1-L3) and one in a DECT scan (vertebrae L1-L4). Each dataset included different individuals [MECT 15 patients (45 vertebrae) and DECT 12 patients (48 vertebrae), respectively]. vBMD analysis was conducted using Philips IntelliSpace (IP) and Mindways qCT Pro (MW). Regarding the DECT scans, vBMD analysis was done at three different energies: 80, 150 and synthetic 120 kVp and for MECT scan at 120 kVp. For comparison of vBMD results between different software (aim 1) MECT 120 kVp and DECT synthetic 120 kVp data was used. For analyzing suitability of using different DECT energies for vBMD assessment (aim 2) all three DECT energies were used and results from each software was analyzed separately.Results: vBMD assessed with MW and IP, respectively correlated significantly for both the MECT (r=0.876; P<0.001) and DECT (r=0.837; P<0.001) scans, but the vBMD values were lower in using IP for vBMD assessment (8% and 14% lower for MECT and DECT, respectively; P=0.001). Regarding the different DECT energies, using MW for vBMD assessment showed significant correlations in vBMD results between 120 kVp and the two other energies (r=0.988 and r=0.939) and no significant differences in absolute vBMD values (P>0.05). The IP analysis as well showed significant correlation between 120 kVp and the other energies (r=0.769 and r=0.713, respectively), but differences in absolute vBMD values between the energies (P≤0.001). Conclusions:We show that the correlations between the vBMD derived from the two investigated software solutions were generally good but that absolute vBMD value did differ and might impact the clinical diagnosis of osteoporosis. Though small, our study data indicate that vBMD might be assessed in energies other than 120 kVp when using MW but not when using IP.