Practical applications of deep learning: classifying the most common categories of plain radiographs in a PACS using a neural network

Objectives The goal of the present study was to classify the most common types of plain radiographs using a neural network and to validate the network’s performance on internal and external data. Such a network could help improve various radiological workflows. Methods All radiographs from the year 2017 (n = 71,274) acquired at our institution were retrieved from the PACS. The 30 largest categories (n = 58,219, 81.7% of all radiographs performed in 2017) were used to develop and validate a neural network (MobileNet v1.0) using transfer learning. Image categories were extracted from DICOM metadata (study and image description) and mapped to the WHO manual of diagnostic imaging. As an independent, external validation set, we used images from other institutions that had been stored in our PACS (n = 5324). Results In the internal validation, the overall accuracy of the model was 90.3% (95%CI: 89.2–91.3%), whereas, for the external validation set, the overall accuracy was 94.0% (95%CI: 93.3–94.6%). Conclusions Using data from one single institution, we were able to classify the most common categories of radiographs with a neural network. The network showed good generalizability on the external validation set and could be used to automatically organize a PACS, preselect radiographs so that they can be routed to more specialized networks for abnormality detection or help with other parts of the radiological workflow (e.g., automated hanging protocols; check if ordered image and performed image are the same). The final AI algorithm is publicly available for evaluation and extension

Virtual Noncontrast Images From Portal Venous Phase Spectral-Detector CT Acquisitions for Adrenal Lesion Characterization

Objective The aim of this study was to investigate if Hounsfield unit (HU) values from virtual noncontrast (VNC) images derived from portal venous phase spectral-detector computed tomography can help to differentiate adrenal adenomas and metastases. Methods Spectral-detector computed tomography datasets of 33 patients with presence of adrenal lesions and standard of reference for lesion origin by follow-up/prior examinations or dedicated magnetic resonance imaging were included. Conventional and VNC images were reconstructed from the same scan. Region of interest-based image analysis was performed in adrenal lesions and contralateral healthy adrenal tissue. Results The 33 lesions consisted of 23 adenomas and 10 metastases. Hounsfield unit values of all lesions in VNC images were significantly lower compared with conventional images (18.2 +/- 12.6 HU vs 59.6 +/- 21.7 HU, P < 0.001). Hounsfield unit values in adenomas were significantly lower in VNC images (11.3 +/- 6.5 HU vs 34.1 +/- 9.1 HU, P < 0.001). Conclusions Virtual noncontrast HU values differed significantly between adrenal adenomas and metastases and can therefore be used for improved characterization of incidental adrenal lesions and definition of adrenal adenomas

Metal artifacts in patients with large dental implants and bridges: combination of metal artifact reduction algorithms and virtual monoenergetic images provides an approach to handle even strongest artifacts

ObjectivesThis study compares reduction of strong metal artifacts from large dental implants/bridges using spectral detector CT-derived virtual monoenergetic images (VMI), metal artifact reduction algorithms/reconstructions (MAR), and a combination of both methods (VMIMAR) to conventional CT images (CI).MethodsForty-one spectral detector CT (SDCT) datasets of patients that obtained additional MAR reconstructions due to strongest artifacts from large oral implants were included. CI, VMI, MAR, and VMIMAR ranging from 70 to 200keV (10keV increment) were reconstructed. Objective image analyses were performed ROI-based by measurement of attenuation (HU) and standard deviation in most pronounced hypo-/hyperdense artifacts as well as artifact impaired soft tissue (mouth floor/soft palate). Extent of artifact reduction, diagnostic assessment of soft tissue, and appearance of new artifacts were rated visually by two radiologists.ResultsThe hypo-/hyperattenuating artifacts showed an increase and decrease of HU values in MAR and VMIMAR (CI/MAR/VMIMAR-200keV: -369.8239.6/-37.3109.6/-46.271.0 HU, p<0.001 and 274.8170.2/51.3 +/- 150.8/36.6 +/- 56.0, p<0.001, respectively). Higher keV values in hyperdense artifacts allowed for additional artifact reduction; however, this trend was not significant. Artifacts in soft tissue were reduced significantly by MAR and VMIMAR. Visually, high-keV VMI, MAR, and VMIMAR reduced artifacts and improved diagnostic assessment of soft tissue. Overcorrection/new artifacts were reported that mostly did not hamper diagnostic assessment. Overall interrater agreement was excellent (ICC=0.85).ConclusionsIn the presence of strong artifacts due to large oral implants, MAR is a powerful mean for artifact reduction. For hyperdense artifacts, MAR should be supplemented by VMI ranging from 140 to 200keV. This combination yields optimal artifact reduction and improves the diagnostic image assessment in imaging of the head and neck

Body composition on low dose chest CT is a significant predictor of poor clinical outcome in COVID-19 disease - A multicenter feasibility study

Purpose: Low-dose computed tomography (LDCT) of the chest is a recommended diagnostic tool in early stage of COVID-19 pneumonia. High age, several comorbidities as well as poor physical fitness can negatively influence the outcome within COVID-19 infection. We investigated whether the ratio of fat to muscle area, measured in initial LDCT, can predict severe progression of COVID-19 in the follow-up period. Method: We analyzed 58 individuals with confirmed COVID-19 infection that underwent an initial LDCT in one of two included centers due to COVID-19 infection. Using the ratio of waist circumference per paravertebral muscle circumference (FMR), the body composition was estimated. Patient outcomes were rated on an ordinal scale with higher numbers representing more severe progression or disease associated complications (hospitalization/intensive care unit (ICU)/tracheal intubation/death) within a follow-up period of 22 days after initial LDCT. Results: In the initial LDCT a significantly higher FMR was found in patients requiring intensive care treatment within the follow-up period. In multivariate logistic regression analysis, FMR (p < .001) in addition to age (p < .01), was found to be a significant predictor of the necessity for ICU treatment of COVID-19 patients. Conclusion: FMR as potential surrogate of body composition and obesity can be easily determined in initial LDCT of COVID-19 patients. Within the multivariate analysis, in addition to patient age, low muscle area in proportion to high fat area represents an additional prognostic information for the patient outcome and the need of an ICU treatment during the follow-up period within the next 22 days. This multicentric pilot study presents a method using an initial LDCT to screen opportunistically for obese patients who have an increased risk for the need of ICU treatment. While clinical capacities, such as ICU beds and ventilators, are more crucial than ever to help manage the current global corona pandemic, this work introduces an approach that can be used for a cost-effective way to help determine the amount of these rare clinical resources required in the near future

Inter-scan and inter-scanner variation of quantitative dual-energy CT: evaluation with three different scanner types

Objectives: To investigate inter-scan and inter-scanner variation of iodine concentration (IC) and attenuation in virtual monoenergetic images at 65 keV (HU65keV) in patients with repeated abdominal examinations on dual-source (dsDECT), rapid kV switching (rsDECT), and dual-layer detector DECT (dlDECT). Methods: We retrospectively included 131 patients who underwent two abdominal DECT examinations on the same scanner (dsDECT: n = 46, rsDECT: n = 45, dlDECT: n = 40). IC and HU65keV were measured by placing regions of interest in the liver, spleen, kidneys, aorta, portal vein, and inferior vena cava. Overall IC and HU65keV for each scanner, their inter-scan differences and proportional variation were calculated and compared between scanner types. Results: The three scanner-specific cohorts showed similar weight, body diameter, age, sex, and contrast media injection parameters as well as inter-scan differences hereof (p range: 0.23-0.99). Absolute inter-scan differences of HU65keV and IC were comparable between scanners (p range: 0.08-1.0). Overall inter-scan variation was significantly higher in IC than HU65keV (p < 0.05). For the liver, rsDECT showed significantly lower inter-scan variation of IC compared to dsDECT/dlDECT (p = 0.005/0.01), while for the spleen, this difference was only significant compared to dsDECT (p = 0.015). Normalizing IC of the liver to the portal vein and of the spleen to the aorta did not significantly reduce inter-scan variation (p = 0.97 and 0.50). Conclusions: Iodine measurements across different DECT scanners show inter-scan variation which is higher compared to variation of attenuation values. Inter-scanner differences in longitudinal variation and overall iodine concentration depend on the scanner pairs and organs assessed and should be acknowledged in clinical and scientific DECT applications

Use and Control of Artificial Intelligence in Patients Across the Medical Workflow: Single-Center Questionnaire Study of Patient Perspectives

Background: Artificial intelligence (AI) is gaining increasing importance in many medical specialties, yet data on patients' opinions on the use of AI in medicine are scarce. Objective: This study aimed to investigate patients' opinions on the use of AI in different aspects of the medical workflow and the level of control and supervision under which they would deem the application of AI in medicine acceptable. Methods: Patients scheduled for computed tomography or magnetic resonance imaging voluntarily participated in an anonymized questionnaire between February 10, 2020, and May 24, 2020. Patient information, confidence in physicians vs AI in different clinical tasks, opinions on the control of AI, preference in cases of disagreement between AI and physicians, and acceptance of the use of AI for diagnosing and treating diseases of different severity were recorded. Results: In total, 229 patients participated. Patients favored physicians over AI for all clinical tasks except for treatment planning based on current scientific evidence. In case of disagreement between physicians and AI regarding diagnosis and treatment planning, most patients preferred the physician's opinion to AI (96.2% [153/159] vs 3.8% [6/159] and 94.8% [146/154] vs 5.2% [8/154], respectively; P=.001). AI supervised by a physician was considered more acceptable than AI without physician supervision at diagnosis (confidence rating 3.90 [SD 1.20] vs 1.64 [SD 1.03], respectively; P=.001) and therapy (3.77 [SD 1.18] vs 1.57 [SD 0.96], respectively; P=.001). Conclusions: Patients favored physicians over AI in most clinical tasks and strongly preferred an application of AI with physician supervision. However, patients acknowledged that AI could help physicians integrate the most recent scientific evidence into medical care. Application of AI in medicine should be disclosed and controlled to protect patient interests and meet ethical standards

Virtual Noncontrast Images From Portal Venous Phase Spectral-Detector CT Acquisitions for Adrenal Lesion Characterization

Objective The aim of this study was to investigate if Hounsfield unit (HU) values from virtual noncontrast (VNC) images derived from portal venous phase spectral-detector computed tomography can help to differentiate adrenal adenomas and metastases. Methods Spectral-detector computed tomography datasets of 33 patients with presence of adrenal lesions and standard of reference for lesion origin by follow-up/prior examinations or dedicated magnetic resonance imaging were included. Conventional and VNC images were reconstructed from the same scan. Region of interest-based image analysis was performed in adrenal lesions and contralateral healthy adrenal tissue. Results The 33 lesions consisted of 23 adenomas and 10 metastases. Hounsfield unit values of all lesions in VNC images were significantly lower compared with conventional images (18.2 +/- 12.6 HU vs 59.6 +/- 21.7 HU, P < 0.001). Hounsfield unit values in adenomas were significantly lower in VNC images (11.3 +/- 6.5 HU vs 34.1 +/- 9.1 HU, P < 0.001). Conclusions Virtual noncontrast HU values differed significantly between adrenal adenomas and metastases and can therefore be used for improved characterization of incidental adrenal lesions and definition of adrenal adenomas

Reducing artifacts from total hip replacements in dual layer detector CT: Combination of virtual monoenergetic images and orthopedic metal artifact reduction

Purpose: To evaluate the reduction of artifacts caused by total hip replacements (THR) in dual-layer DECT (DLCT) provided by the combination of virtual monoenergetic images (VMI) and orthopedic metal artifact reduction (MAR). Materials and Methods: A total of 24 consecutive patients carrying THR, who received DLCT, were included. Four different images were reconstructed from the same CT dataset: a) conventional images (CI), b) conventional images with orthopedic metal artifact reduction (CIMAR) c) VMI and d) VMI combined with orthopedic metal artifact reduction (VMIMAR). VMI and VMIMAR were reconstructed at 140 keV, 160 keV, 180 keV and 200 keV. Attenuation (HU) and noise (SD) were measured in order to evaluate reduction of hypodense and hyperdense artifacts, evaluate reduction of image noise as well as to calculate contrast-to-noise ratios (CNR). Image quality was additionally rated with regard to: a) extent of artifact reduction and assessment of b) pelvic organs, c) bone and d) muscle adjacent to the metal implants. Statistical analysis was performed using Wilcoxon test. Results: VMIMAR at high keV, 140, 160, 180 and 200 keV, led to the greatest reduction of hypodense artifacts in comparison to plain VMI or CIMAR (p < 0.01), while in comparison to CI hyperdense artifacts were significantly reduced in all reconstructions (p < 0.05). Accordingly, subjective analysis found VMIMAR to be superior in reducing hypodense artifacts in comparison to VMI and CIMAR (p < 0.05), while hyperdense artifacts were equally reduced in all reconstructions compared to CI (p < 0.0001). Additionally, assessment of the pelvic organs and adjacent bone was significantly improved in VMIMAR in comparison to VMI and CIMAR (p < 0.05). In contrast, muscles adjacent to the metal implants were significantly better assessable in all reconstructions compared to CI (p < 0.01). Conclusion: The combination of VMI and MAR yields strongest reduction of hypo- and hyperdense artifacts caused by total hip replacements in staging DLCT in comparison to each technique by itself

Calcification of the thoracic aorta on low-dose chest CT predicts severe COVID-19

Background Cardiovascular comorbidity anticipates poor prognosis of SARS-CoV-2 disease (COVID-19) and correlates with the systemic atherosclerotic transformation of the arterial vessels. The amount of aortic wall calcification (AWC) can be estimated on low-dose chest CT. We suggest quantification of AWC on the low-dose chest CT, which is initially performed for the diagnosis of COVID-19, to screen for patients at risk of severe COVID-19. Methods Seventy consecutive patients (46 in center 1, 24 in center 2) with parallel low-dose chest CT and positive RT-PCR for SARS-CoV-2 were included in our multi-center, multi-vendor study. The outcome was rated moderate (no hospitalization, hospitalization) and severe (ICU, tracheal intubation, death), the latter implying a requirement for intensive care treatment. The amount of AWC was quantified with the CT vendor's software. Results Of 70 included patients, 38 developed a moderate, and 32 a severe COVID-19. The average volume of AWC was significantly higher throughout the subgroup with severe COVID-19, when compared to moderate cases (771.7 mm(3) (Q1 = 49.8 mm(3), Q3 = 3065.5 mm(3)) vs. 0 mm(3) (Q1 = 0 mm(3), Q3 = 57.3 mm(3))). Within multivariate regression analysis, including AWC, patient age and sex, as well as a cardiovascular comorbidity score, the volume of AWC was the only significant regressor for severe COVID-19 (p = 0.004). For AWC > 3000 mm(3), the logistic regression predicts risk for a severe progression of 0.78. If there are no visually detectable AWC risk for severe progression is 0.13, only. Conclusion AWC seems to be an independent biomarker for the prediction of severe progression and intensive care treatment of COVID-19 already at the time of patient admission to the hospital; verification in a larger multi-center, multi-vendor study is desired