AI support for accurate and fast radiological diagnosis of COVID-19: an international multicenter, multivendor CT study.

OBJECTIVES: Differentiation between COVID-19 and community-acquired pneumonia (CAP) in computed tomography (CT) is a task that can be performed by human radiologists and artificial intelligence (AI). The present study aims to (1) develop an AI algorithm for differentiating COVID-19 from CAP and (2) evaluate its performance. (3) Evaluate the benefit of using the AI result as assistance for radiological diagnosis and the impact on relevant parameters such as accuracy of the diagnosis, diagnostic time, and confidence. METHODS: We included n = 1591 multicenter, multivendor chest CT scans and divided them into AI training and validation datasets to develop an AI algorithm (n = 991 CT scans; n = 462 COVID-19, and n = 529 CAP) from three centers in China. An independent Chinese and German test dataset of n = 600 CT scans from six centers (COVID-19 / CAP; n = 300 each) was used to test the performance of eight blinded radiologists and the AI algorithm. A subtest dataset (180 CT scans; n = 90 each) was used to evaluate the radiologists' performance without and with AI assistance to quantify changes in diagnostic accuracy, reporting time, and diagnostic confidence. RESULTS: The diagnostic accuracy of the AI algorithm in the Chinese-German test dataset was 76.5%. Without AI assistance, the eight radiologists' diagnostic accuracy was 79.1% and increased with AI assistance to 81.5%, going along with significantly shorter decision times and higher confidence scores. CONCLUSION: This large multicenter study demonstrates that AI assistance in CT-based differentiation of COVID-19 and CAP increases radiological performance with higher accuracy and specificity, faster diagnostic time, and improved diagnostic confidence. KEY POINTS: • AI can help radiologists to get higher diagnostic accuracy, make faster decisions, and improve diagnostic confidence. • The China-German multicenter study demonstrates the advantages of a human-machine interaction using AI in clinical radiology for diagnostic differentiation between COVID-19 and CAP in CT scans.

Work expectations, their fulfillment, and exhaustion among radiologists of all career levels: what can be learned from the example of Germany.

OBJECTIVES: To evaluate work expectations of radiologists at different career levels, their fulfillment, prevalence of exhaustion, and exhaustion-associated factors. METHODS: A standardized digital questionnaire was distributed internationally to radiologists of all career levels in the hospital and in ambulatory care via radiological societies and sent manually to 4500 radiologists of the largest German hospitals between December 2020 and April 2021. Statistics were based on age- and gender-adjusted regression analyses of respondents working in Germany (510 out of 594 total respondents). RESULTS: The most frequent expectations were "joy at work" (97%) and a "good working atmosphere" (97%), which were considered fulfilled by at least 78%. The expectation of a "structured residency within the regular time interval" (79%) was more frequently judged fulfilled by senior physicians (83%, odds ratio (OR) 4.31 [95% confidence interval (95% CI) 1.95-9.52]), chief physicians (85%, 6.81 [95% CI 1.91-24.29]), and radiologists outside the hospital (88%, 7.59 [95% CI 2.40-24.03]) than by residents (68%). Exhaustion was most common among residents (physical exhaustion: 38%; emotional exhaustion: 36%), in-hospital specialists (29%; 38%), and senior physicians (30%; 29%). In contrast to paid extra hours, unpaid extra hours were associated with physical exhaustion (5-10 extra hours: OR 2.54 [95% CI 1.54-4.19]). Fewer opportunities to shape the work environment were related to a higher probability of physical (2.03 [95% CI 1.32-3.13]) and emotional (2.15 [95% CI 1.39-3.33]) exhaustion. CONCLUSIONS: While most radiologists enjoy their work, residents wish for more training structure. Ensuring payment of extra hours and employee empowerment may help preventing burnout in high-risk groups. KEY POINTS: • Most important work expectations of radiologists who work in Germany are "joy at work," a "good working atmosphere," "support for further qualification," and a "structured residency within the regular time interval," with the latter containing potential for improvement according to residents. • Physical and emotional exhaustion are common at all career levels except for chief physicians and for radiologists who work outside the hospital in ambulatory care. • Exhaustion as a major burnout criterion is associated with unpaid extra hours and reduced opportunities to shape the work environment.

Image Fusion Improves Interdisciplinary Communication in the Treatment of Head and Neck Cancer.

INTRODUCTION: The diagnosis and therapy of oral squamous cell carcinoma (Osee) in Germany is according to guidelines and relies on interdisciplinary board meetings. Standard examination techniques are computed tomography (CT) and magnet resonance imaging (MRI). These technologies are used as objective tools for serial presentation in an oncologic board meeting. The presentation of multiple series at different time points can be time consuming and might not often depict a patients case clearly for all involved disciplinaries. A conclusive image fusion could improve the communication. Thus, this study aims to introduce a novel idea of image fusion into the field of craniomaxillofacial surgery in order to ease understanding and improve therapy in complex Osee patients' cases. MATERIALS AND METHODS: Three key data sets of a patient with OSCC at the right tongue have been merged by image fusion of 3 MRi of head and neck with 3 CT thorax and abdomen using Syngo via (Siemens). Fused images were used as at a glance picture for presenting and discussion a patients case. Focus was on presenting a case of a primary manifestation of OSCC with the potential of a local relapse and distant metastases in an interdisciplinary oncol-ogic board meeting. RESULTS: Image fusion enabled to visualize the primary tumor, local relapse as well as distant pulmonary metastasis and within the suprarenal gland, which have been occurred in a linear time line of 13 months. DISCUSSION: Image fusion of different modalities that is CT and MRi, which were gathered at different time points, presents a new approach within the field of craniomaxillofacial surgery and helped to understand cancer localization and relapse at 1 glance. This new approach will enable a compact data set of patients oncological history as a more decisive tool for all involved disciplinaries. CONCLUSIONS: Image fusion might have the potential to become a standard approach in order to ease multiple therapists to make therapy decisions in oncologic board meetings on basis of current three-dimensional ready CT imaging and MRI.

Generative Adversarial Networks: A Primer for Radiologists.

Artificial intelligence techniques involving the use of artificial neural networks-that is, deep learning techniques-are expected to have a major effect on radiology. Some of the most exciting applications of deep learning in radiology make use of generative adversarial networks (GANs). GANs consist of two artificial neural networks that are jointly optimized but with opposing goals. One neural network, the generator, aims to synthesize images that cannot be distinguished from real images. The second neural network, the discriminator, aims to distinguish these synthetic images from real images. These deep learning models allow, among other applications, the synthesis of new images, acceleration of image acquisitions, reduction of imaging artifacts, efficient and accurate conversion between medical images acquired with different modalities, and identification of abnormalities depicted on images. The authors provide an introduction to GANs and adversarial deep learning methods. In addition, the different ways in which GANs can be used for image synthesis and image-to-image translation tasks, as well as the principles underlying conditional GANs and cycle-consistent GANs, are described. Illustrated examples of GAN applications in radiologic image analysis for different imaging modalities and different tasks are provided. The clinical potential of GANs, future clinical GAN applications, and potential pitfalls and caveats that radiologists should be aware of also are discussed in this review. The online slide presentation from the RSNA Annual Meeting is available for this article. ©RSNA, 2021.

DIALAPP: a prospective validation of a new diagnostic algorithm for acute appendicitis.

PURPOSE: The management of patients with suspected appendicitis remains a challenge in daily clinical practice, and the optimal management algorithm is still being debated. Negative appendectomy rates (NAR) continue to range between 10 and 15%. This prospective study evaluated the accuracy of a diagnostic pathway in acute appendicitis using clinical risk stratification (Alvarado score), routine ultrasonography, gynecology consult for females, and selected CT after clinical reassessment. METHODS: Patients presenting with suspected appendicitis between November 2015 and September 2017 from age 18 years and above were included. Decision-making followed a clear management pathway. Patients were followed up for 6 months after discharge. The hypothesis was that the algorithm can reduce the NAR to a value of under 10%. RESULTS: A total of 183 patients were included. In 65 of 69 appendectomies, acute appendicitis was confirmed by histopathology, corresponding to a NAR of 5.8%. Notably, all 4 NAR appendectomies had other pathologies of the appendix. The perforation rate was 24.6%. Only 36 patients (19.7%) received a CT scan. The follow-up rate after 30 days achieved 69%, including no patients with missed appendicitis. The sensitivity and specificity of the diagnostic pathway was 100% and 96.6%, respectively. The potential saving in costs can be as much as 19.8 million €/100,000 cases presenting with the suspicion of appendicitis. CONCLUSION: The risk-stratified diagnostic algorithm yields a high diagnostic accuracy for patients with suspicion of appendicitis. Its implementation can safely reduce the NAR, simultaneously minimizing the use of CT scans and optimizing healthcare-related costs in the treatment of acute appendicitis. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT02627781 (December 2015).

[Artificial intelligence in oncological radiology : A (p)review]. / Künstliche Intelligenz in der onkologischen Radiologie : Ein (P)review.

BACKGROUND: Artificial intelligence (AI) has the potential to fundamentally change medicine within the coming decades. Radiological imaging is one of the primary fields of its clinical application. OBJECTIVES: In this article, we summarize previous AI developments with a focus on oncological radiology. Based on selected examples, we derive scenarios for developments in the next 10 years. MATERIALS AND METHODS: This work is based on a review of various literature and product databases, publications by regulatory authorities, reports, and press releases. CONCLUSIONS: The clinical use of AI applications is still in an early stage of development. The large number of research publications shows the potential of the field. Several certified products have already become available to users. However, for a widespread adoption of AI applications in clinical routine, several fundamental prerequisites are still awaited. These include the generation of evidence justifying the use of algorithms through representative clinical studies, adjustments to the framework for approval processes and dedicated education and teaching resources for its users. It is expected that use of AI methods will increase, thus, creating new opportunities for improved diagnostics, therapy, and more efficient workflows.

Comparison of machine learning algorithms to predict clinically significant prostate cancer of the peripheral zone with multiparametric MRI using clinical assessment categories and radiomic features.

OBJECTIVES: To analyze the performance of radiological assessment categories and quantitative computational analysis of apparent diffusion coefficient (ADC) maps using variant machine learning algorithms to differentiate clinically significant versus insignificant prostate cancer (PCa). METHODS: Retrospectively, 73 patients were included in the study. The patients (mean age, 66.3 ± 7.6 years) were examined with multiparametric MRI (mpMRI) prior to radical prostatectomy (n = 33) or targeted biopsy (n = 40). The index lesion was annotated in MRI ADC and the equivalent histologic slides according to the highest Gleason Grade Group (GrG). Volumes of interest (VOIs) were determined for each lesion and normal-appearing peripheral zone. VOIs were processed by radiomic analysis. For the classification of lesions according to their clinical significance (GrG ≥ 3), principal component (PC) analysis, univariate analysis (UA) with consecutive support vector machines, neural networks, and random forest analysis were performed. RESULTS: PC analysis discriminated between benign and malignant prostate tissue. PC evaluation yielded no stratification of PCa lesions according to their clinical significance, but UA revealed differences in clinical assessment categories and radiomic features. We trained three classification models with fifteen feature subsets. We identified a subset of shape features which improved the diagnostic accuracy of the clinical assessment categories (maximum increase in diagnostic accuracy ΔAUC = + 0.05, p < 0.001) while also identifying combinations of features and models which reduced overall accuracy. CONCLUSIONS: The impact of radiomic features to differentiate PCa lesions according to their clinical significance remains controversial. It depends on feature selection and the employed machine learning algorithms. It can result in improvement or reduction of diagnostic performance. KEY POINTS: • Quantitative imaging features differ between normal and malignant tissue of the peripheral zone in prostate cancer. • Radiomic feature analysis of clinical routine multiparametric MRI has the potential to improve the stratification of clinically significant versus insignificant prostate cancer lesions in the peripheral zone. • Certain combinations of standard multiparametric MRI reporting and assessment categories with feature subsets and machine learning algorithms reduced the diagnostic performance over standard clinical assessment categories alone.

Three-Dimensional Magnetic Resonance Imaging Volumetry of Radial Forearm Flap Reconstructions After Craniomaxillofacial Tumor Resection.

BACKGROUND: Oral cancer is a common and life threatening disease that requires interdisciplinary treatment and often necessitates complex facial reconstruction. Standard care includes tumor resection, while reconstruction is routinely performed with free radial forearm flaps. As esthetic results are crucial for quality of life, flap size, flap volume, and flap composition have to be considered. To date no standardized measurement of flap volume and shrinkage has been established for routine use. The purpose of this study was therefore to evaluate the transplant volume of radial forearm flaps in craniomaxillofacial reconstruction using magnetic resonance imaging (MRI) volumetry. MATERIAL AND METHODS: Ten postoperative MR sequences of 5 patients were included. All patients had received transplantation of radial forearm flaps after tumor resection and radiation therapy. Evaluated parameters were: sex, age, type of flap, flap volume. Two different observers (1 surgeon and 1 radiologist) segmented transplant volume at three different time points in a postoperative MRI independently and in consensus, using both axial and coronal slices. A nonfat saturated T1 spin echo sequence was used. Mean transplant volume was calculated. RESULTS: A total of 90 volumetric measurements were included. Overall Tvolm was 24.83 cm from axial sections and 27.25 cm from coronal sections. Measurements for axial and coronal orientations differed significantly. Results showed excellent intra- and inter-rater correlation, coefficient for rater A and rater B were 0.91 (axial) and 0.96 (coronal). CONCLUSION: MRI volumetry is a noninvasive reproducible method to quantify volume of free radial forearm flaps in situ but should follow specific considerations for best results.

Optimisation of window settings for traditional and noise-optimised virtual monoenergetic imaging in dual-energy computed tomography pulmonary angiography.

OBJECTIVES: To define optimal window settings for displaying virtual monoenergetic images (VMI) of dual-energy CT pulmonary angiography (DE-CTPA). METHODS: Forty-five patients who underwent clinically-indicated third-generation dual-source DE-CTPA were retrospectively evaluated. Standard linearly-blended (M_0.6), 70-keV traditional VMI (M70), and 40-keV noise-optimised VMI (M40+) reconstructions were analysed. For M70 and M40+ datasets, the subjectively best window setting (width and level, B-W/L) was independently determined by two observers and subsequently related with pulmonary artery attenuation to calculate separate optimised values (O-W/L) using linear regression. Subjective evaluation of image quality (IQ) between W/L settings were assessed by two additional readers. Repeated measures of variance were performed to compare W/L settings and IQ indices between M_0.6, M70, and M40+. RESULTS: B-W/L and O-W/L for M70 were 460/140 and 450/140, and were 1100/380 and 1070/380 for M40+, respectively, differing from standard DE-CTPA W/L settings (450/100). Highest subjective scores were observed for M40+ regarding vascular contrast, embolism demarcation, and overall IQ (all p<0.001). CONCLUSIONS: Application of O-W/L settings is beneficial to optimise subjective IQ of VMI reconstructions of DE-CTPA. A width slightly less than two times the pulmonary trunk attenuation and a level approximately of overall pulmonary vessel attenuation are recommended. KEY POINTS: • Application of standard window settings for VMI results in inferior image perception. • No significant differences between B-W/L and O-W/L for M70/M40+ were observed. • O-W/L for M70 were 450/140 and were 1070/380 for M40+. • Improved subjective IQ characteristics were observed for VMI displayed with O-W/L.

Automated Attenuation Based Tube Potential Selection of the Lower Extremity Runoff: A Comparison to Fixed Kilovolt with Automated Tube Current Modulation.

PURPOSE: The aim of this study was to evaluate the impact of automated attenuation-based tube potential selection (ATPS) on image quality and radiation dose exposure parameters at a computed tomography angiography (CTA) lower-extremity runoff. MATERIALS AND METHODS: Two hundred forty patients (156 men, 84 women) underwent CTA examinations of the lower-extremity runoff on a second-generation dual-source computed tomography system: 120 patients at a fixed tube potential of 120 kV and a tube current of 180 reference mAs, another 120 patients using automated ATPS. Volume computed tomography dose index (CTDIvol), dose-length-product (DLP), body diameters, noise, signal-to-noise ratio, and subjective image quality were compared. RESULTS: In the ATPS group, 80 kV was automatically selected in 102 patients, 100 kV in 15 patients, and 120 kV in 3 patients; 140 kV was not chosen in any of the cases. The median CTDIvol of 4.81 mGy (2.2-10.6 mGy) and DLP of 568 mGy⋅cm (203-1324 mGy⋅cm) in the ATPS group were significantly lower compared with the CTDIvol of 8.1 mGy (4.4-14.4 mGy) and DLP of 1027.5 mGy⋅cm (509-1806 mGy⋅cm) in the fixed 120-kV group (P < 0.01). Image quality was comparable (P > 0.05). CONCLUSION: Automated ATPS allows for significant dose savings in lower-extremity runoff CTA, whereas image quality remains constant at a high level.

Advanced image-based virtual monoenergetic dual-energy CT angiography of the abdomen: optimization of kiloelectron volt settings to improve image contrast.

OBJECTIVES: To compare quantitative image quality parameters in abdominal dual-energy computed tomography angiography (DE-CTA) using an advanced image-based (Mono+) reconstruction algorithm for virtual monoenergetic imaging and standard DE-CTA. METHODS: Fifty-five patients (36 men; mean age, 64.2 ± 12.7 years) who underwent abdominal DE-CTA were retrospectively included. Mono + images were reconstructed at 40, 50, 60, 70, 80, 90 and 100 keV levels and as standard linearly blended M_0.6 images (60 % 100 kV, 40 % 140 kV). The contrast-to-noise ratio (CNR) and signal-to-noise ratio (SNR) of the common hepatic (CHA), splenic (SA), superior mesenteric (SMA) and left renal arteries (LRA) were objectively measured. RESULTS: Mono+ DE-CTA series showed a statistically superior CNR for 40, 50, 60, 70 and 80 keV (P < 0.031) compared to M_0.6 images for all investigated arteries except SMA at 80 keV (P = 0.08). CNR at 40 keV revealed a mean relative increase of 287.7 % compared to linearly blended images among all assessed arteries (P < 0.001). SNR of Mono+ images was consistently significantly higher at 40, 50, 60 and 70 keV compared to M_0.6 for CHA and SA (P < 0.009). CONCLUSIONS: Compared to linearly blended images, Mono+ reconstructions at low keV levels of abdominal DE-CTA datasets significantly improve quantitative image quality. KEY POINTS: • Mono+ combines increased attenuation with reduced image noise compared to standard DE-CTA. • Mono+ shows superior contrast-to-noise ratios at low keV compared to linearly-blended images. • Contrast-to-noise ratio in monoenergetic DE-CTA peaks at 40 keV. • Mono+ reconstructions significantly improve quantitative image quality at low keV levels.

Quantitative evaluation of beam-hardening artefact correction in dual-energy CT myocardial perfusion imaging.

OBJECTIVES: To assess quantitatively the impact of a novel reconstruction algorithm ("kernel") with beam-hardening correction (BHC) on beam-hardening artefacts of the myocardium at dual-energy CT myocardial perfusion imaging (DE-CTMPI). METHODS: Rest-series of DE-CTMPI examinations from 14 patients were retrospectively analyzed. Six image series were reconstructed for each patient: a) 100 kV, b) 140 kV, and c) linearly blended MIX0.5, each with BHC (D33f kernel) and without (D30f kernel). Seven hundred and fifty-six myocardial regions were assessed. Seven equal regions of interest divided the myocardium in the axial section. Three subdivisions were created within these regions in areas prone to BHA. Reports of SPECT studies performed within 30 days of CT examination were used to confirm the presence and location of true perfusion defects. Paired student t-test was used for statistical evaluation. RESULTS: Overall mean myocardial attenuation was lower using BHC (D30f: 87.3 ± 24.1 HU; D33f: 85.5 ± 21.5 HU; p = 0.009). Overall relative difference from average myocardial attenuation (RDMA) was more homogeneous using BHC (D30f: -0.3 ± 11.4 %; D33f: 0.1 ± 10.1 %; p < 0.001). Changes in RDMA were greatest in the posterobasal myocardium (D30f: -16.2 ± 10.0 %; D33f: 3.4 ± 10.7 %; p < 0.001). CONCLUSIONS: A dedicated reconstruction algorithm with BHC can significantly reduce beam-hardening artefacts in DE-CTMPI. KEY POINTS: • Beam-hardening artefacts (BHA) cause interference with attenuation-based CT myocardial perfusion assessment (CTMPI). • BHA occur mostly in the posterobasal left ventricular wall. • Beam-hardening correction homogenized and decreased mean myocardial attenuation. • BHC can help avoid false-positive findings and increase specificity of static CTMPI.

Improved visual delineation of the intimal flap in Stanford type A and B dissections at 3rd generation dual-source high-pitch CT angiography.

OBJECTIVE: Evaluation of the intimal flap visibility comparing 2nd and 3rd generation dual-source high-pitch CT. METHODS: Twenty-five consecutive patients with aortic dissection underwent CT angiography on a second and third generation dual-source CT scanner using prospective ECG-gated high-pitch dual-source CT acquisition mode. Contrast material, saline flush and flow rate were kept equal for optimum comparability. The visibility of the intimal flap as well as the delineation of the different vascular structures was evaluated. RESULTS: In 3rd generation dual-source high-pitch CT we could show a significant improvement of intimal flap visibility in aortic dissection. Especially, the far end of the dissection membrane could be better evaluated in 3rd generation high-pitch CT, reaching statistical significance (P < 0.01). CONCLUSION: 3rd Generation high-pitch CT angiography shows a better delineation of the aortic intimal flap in a small patient cohort, especially in the far ends of the dissection membrane. This might be due to higher tube power in this CT generation. However, to generalise these findings larger trials are needed.

CT Evaluation of Small-Diameter Coronary Artery Stents: Effect of an Integrated Circuit Detector with Iterative Reconstruction.

PURPOSE: To use suitable objective methods of analysis to assess the influence of the combination of an integrated-circuit computed tomographic (CT) detector and iterative reconstruction (IR) algorithms on the visualization of small (≤3-mm) coronary artery stents. MATERIALS AND METHODS: By using a moving heart phantom, 18 data sets obtained from three coronary artery stents with small diameters were investigated. A second-generation dual-source CT system equipped with an integrated-circuit detector was used. Images were reconstructed with filtered back-projection (FBP) and IR at a section thickness of 0.75 mm (FBP75 and IR75, respectively) and IR at a section thickness of 0.50 mm (IR50). Multirow intensity profiles in Hounsfield units were modeled by using a sum-of-Gaussians fit to analyze in-plane image characteristics. Out-of-plane image characteristics were analyzed with z upslope of multicolumn intensity profiles in Hounsfield units. Statistical analysis was conducted with one-way analysis of variance and the Student t test. RESULTS: Independent of stent diameter and heart rate, IR75 resulted in significantly increased xy sharpness, signal-to-noise ratio, and contrast-to-noise ratio, as well as decreased blurring and noise compared with FBP75 (eg, 2.25-mm stent, 0 beats per minute; xy sharpness, 278.2 vs 252.3; signal-to-noise ratio, 46.6 vs 33.5; contrast-to-noise ratio, 26.0 vs 16.8; blurring, 1.4 vs 1.5; noise, 15.4 vs 21.2; all P < .001). In the z direction, the upslopes were substantially higher in the IR50 reconstructions (2.25-mm stent: IR50, 94.0; IR75, 53.1; and FBP75, 48.1; P < .001). CONCLUSION: The implementation of an integrated-circuit CT detector provides substantially sharper out-of-plane resolution of coronary artery stents at 0.5-mm section thickness, while the use of iterative image reconstruction mostly improves in-plane stent visualization.

Assessment of an Advanced Monoenergetic Reconstruction Technique in Dual-Energy Computed Tomography of Head and Neck Cancer.

OBJECTIVES: To define optimal keV settings for advanced monoenergetic (Mono+) dual-energy computed tomography (DECT) in patients with head and neck squamous cell carcinoma (SCC). METHODS: DECT data of 44 patients (34 men, mean age 55.5 ± 16.0 years) with histopathologically confirmed SCC were reconstructed as 40, 55, 70 keV Mono + and M_0.3 (30 % 80 kV) linearly blended series. Attenuation of tumour, sternocleidomastoid muscle, internal jugular vein, submandibular gland, and noise were measured. Three radiologists with >3 years of experience subjectively assessed image quality, lesion delineation, image sharpness, and noise. RESULTS: The highest lesion attenuation was shown for 40 keV series (248.1 ± 94.1 HU), followed by 55 keV (150.2 ± 55.5 HU; P = 0.001). Contrast-to-noise ratio (CNR) at 40 keV (19.09 ± 13.84) was significantly superior to all other reconstructions (55 keV, 10.25 ± 9.11; 70 keV, 7.68 ± 6.31; M_0.3, 5.49 ± 3.28; all P < 0.005). Subjective image quality was highest for 55 keV images (4.53; κ = 0.38, P = 0.003), followed by 40 keV (4.14; κ = 0.43, P < 0.001) and 70 keV reconstructions (4.06; κ = 0.32, P = 0.005), all superior (P < 0.004) to linear blending M_0.3 (3.81; κ = 0.280, P = 0.056). CONCLUSIONS: Mono + DECT at low keV levels significantly improves CNR and subjective image quality in patients with head and neck SCC, as tumour CNR peaks at 40 keV, and 55 keV images are preferred by observers. KEY POINTS: • Mono + DECT combines increased contrast with reduced image noise, unlike linearly blended images. • Mono + DECT imaging allows for superior CNR and subjective image quality. • Head and neck tumour contrast-to-noise ratio peaks at 40 keV. • 55 keV images are preferred over all other series by observers.

Enhancing Forensic Diagnostics: Structured Reporting of Post-Mortem CT versus Autopsy for Laryngohyoid Complex Fractures in Strangulation.

BACKGROUND: The purpose of this study was to establish a standardized structured workflow to compare findings from high-resolution, optimized reconstructions from post-mortem computed tomography (pmCT) with autopsy results in the detection of fractures of the laryngohyoid complex in strangulation victims. METHOD: Forty-two strangulation cases were selected, and pmCT scans of the laryngohyoid complex were obtained. Both pmCT scans and autopsy reports were analyzed using a structured template and compared using Cohen's kappa coefficient (κ) and the McNemar test. The study also compared the prevalence of ossa sesamoidea and non-fusion of the major and minor horns of the hyoid bone between both diagnostic methods. RESULTS: The detection of fractures showed a very good correlation between autopsy and pmCT results (κ = 0.905), with the McNemar test showing no statistically significant difference between the two methods. PmCT identified 28 sesamoid bones, 45 non-fusions of the major horns, and 47 non-fusions of the minor horns of the hyoid bone, compared to four, six, and zero, respectively, identified by autopsy (p < 0.0001). CONCLUSIONS: Autopsy and pmCT findings correlate well and can be used in a complementary manner. PmCT is superior to autopsy in identifying dislocations and detecting anatomical variations in the laryngohyoid complex, which can lead to misinterpretations during autopsy. Therefore, we do not advocate replacing autopsy with pmCT but propose using a structured workflow, including our standardized reporting template, for evaluating lesions in the laryngohyoid complex.

Multiparametric Cardiovascular MRI Assessment of Post-COVID Syndrome in Children in Comparison to Matched Healthy Individuals.

BACKGROUND: Post-COVID syndrome (PCS) can adversely affect the quality of life of patients and their families. In particular, the degree of cardiac impairment in children with PCS is unknown. OBJECTIVE: The aim of this study was to identify potential cardiac inflammatory sequelae in children with PCS compared with healthy controls. METHODS: This single-center, prospective, intraindividual, observational study assesses cardiac function, global and segment-based strains, and tissue characterization in 29 age- and sex-matched children with PCS and healthy children using a 3 T magnetic resonance imaging (MRI). RESULTS: Cardiac MRI was carried out over 36.4 ± 24.9 weeks post-COVID infection. The study cohort has an average age of 14.0 ± 2.8 years, for which the majority of individuals experience from fatigue, concentration disorders, dyspnea, dizziness, and muscle ache. Children with PSC in contrast to the control group exhibited elevated heart rate (83.7 ± 18.1 beats per minute vs 75.2 ± 11.2 beats per minute, P = 0.019), increased indexed right ventricular end-diastolic volume (95.2 ± 19.2 mlm -2 vs 82.0 ± 21.5 mlm -2 , P = 0.018) and end-systolic volume (40.3 ± 7.9 mlm -2 vs 34.8 ± 6.2 mlm -2 , P = 0.005), and elevated basal and midventricular T1 and T2 relaxation times ( P < 0.001 to P = 0.013). Based on the updated Lake Louise Criteria, myocardial inflammation is present in 20 (69%) children with PCS. No statistically significant difference was observed for global strains. CONCLUSIONS: Cardiac MRI revealed altered right ventricular volumetrics and elevated T1 and T2 mapping values in children with PCS, suggestive for a diffuse myocardial inflammation, which may be useful for the diagnostic workup of PCS in children.

Prevalence and prognostic role of thoracic lymphadenopathy in Covid-19. / Prävalenz und prognostische Rolle der thorakalen Lymphadenopathie bei Covid-19.

PURPOSE: The prevalent coronavirus disease 2019 (COVID-19) pandemic has spread throughout the world and is considered a serious threat to global health. The prognostic role of thoracic lymphadenopathy in COVID-19 is unclear. The aim of the present meta-analysis was to analyze the prognostic role of thoracic lymphadenopathy for the prediction of 30-day mortality in patients with COVID-19. MATERIALS AND METHODS: The MEDLINE library, Cochrane, and SCOPUS databases were screened for associations between CT-defined features and mortality in COVID-19 patients up to June 2021. In total, 21 studies were included in the present analysis. The quality of the included studies was assessed by the Newcastle-Ottawa Scale. The meta-analysis was performed using RevMan 5.3. Heterogeneity was calculated by means of the inconsistency index I2. DerSimonian and Laird random-effect models with inverse variance weights were performed without any further correction. RESULTS: The included studies comprised 4621 patients. The prevalence of thoracic lymphadenopathy varied between 1 % and 73.4 %. The pooled prevalence was 16.7 %, 95 % CI = (15.6 %; 17.8 %). The hospital mortality was higher in patients with thoracic lymphadenopathy (34.7 %) than in patients without (20.0 %). The pooled odds ratio for the influence of thoracic lymphadenopathy on mortality was 2.13 (95 % CI = [1.80-2.52], p < 0.001). CONCLUSION: The prevalence of thoracic lymphadenopathy in COVID-19 is 16.7 %. The presence of thoracic lymphadenopathy is associated with an approximately twofold increase in the risk for hospital mortality in COVID-19. KEY POINTS: · The prevalence of lymphadenopathy in COVID-19 is 16.7 %.. · Patients with lymphadenopathy in COVID-19 have a higher risk of mortality during hospitalization.. · Lymphadenopathy nearly doubles mortality and plays an important prognostic role.. CITATION FORMAT: · Bucher AM, Sieren M, Meinel F et al. Prevalence and prognostic role of thoracic lymphadenopathy in Covid-19. Fortschr Röntgenstr 2024; DOI: 10.1055/a-2293-8132.

Value of MRI - T2 Mapping to Differentiate Clinically Significant Prostate Cancer.

Standardized reporting of multiparametric prostate MRI (mpMRI) is widespread and follows international standards (Pi-RADS). However, quantitative measurements from mpMRI are not widely comparable. Although T2 mapping sequences can provide repeatable quantitative image measurements and extract reliable imaging biomarkers from mpMRI, they are often time-consuming. We therefore investigated the value of quantitative measurements on a highly accelerated T2 mapping sequence, in order to establish a threshold to differentiate benign from malignant lesions. For this purpose, we evaluated a novel, highly accelerated T2 mapping research sequence that enables high-resolution image acquisition with short acquisition times in everyday clinical practice. In this retrospective single-center study, we included 54 patients with clinically indicated MRI of the prostate and biopsy-confirmed carcinoma (n = 37) or exclusion of carcinoma (n = 17). All patients had received a standard of care biopsy of the prostate, results of which were used to confirm or exclude presence of malignant lesions. We used the linear mixed-effects model-fit by REML to determine the difference between mean values of cancerous tissue and healthy tissue. We found good differentiation between malignant lesions and normal appearing tissue in the peripheral zone based on the mean T2 value. Specifically, the mean T2 value for tissue without malignant lesions was (151.7 ms [95% CI: 146.9-156.5 ms] compared to 80.9 ms for malignant lesions [95% CI: 67.9-79.1 ms]; p < 0.001). Based on this assessment, a limit of 109.2 ms is suggested. Aditionally, a significant correlation was observed between T2 values of the peripheral zone and PI-RADS scores (p = 0.0194). However, no correlation was found between the Gleason Score and the T2 relaxation time. Using REML, we found a difference of -82.7 ms in mean values between cancerous tissue and healthy tissue. We established a cut-off-value of 109.2 ms to accurately differentiate between malignant and non-malignant prostate regions. The addition of T2 mapping sequences to routine imaging could benefit automated lesion detection and facilitate contrast-free multiparametric MRI of the prostate.