CT-derived lung vessel morphology correlates with prognostic markers in precapillary pulmonary hypertension.

BACKGROUND: While computed tomography pulmonary angiography (CTPA) is an integral part of the work-up in patients with suspected pulmonary hypertension (PH), there is no established CTPA-derived prognostic marker. We aimed to assess whether quantitative readouts of lung vessel morphology correlate with established prognostic indicators in PH. METHODS: We applied a fully-automatic in-house developed algorithm for segmentation of arteries and veins to determine lung vessel morphology in patients with precapillary PH who underwent right heart catheterization and CTPA between May 2016 and May 2019. Primary endpoint of this retrospective study was the calculation of receiver operating characteristics for identifying low and high mortality risk according to the 3-strata risk assessment model presented in the current guidelines. RESULTS: We analyzed 73 patients, median age 65 years (interquartile range (IQR): 54-76), female/male ratio 35/38, median mean pulmonary arterial pressure 37 mm Hg (IQR: 30-46), and found significant correlations with important prognostic factors in pulmonary arterial hypertension. N-terminal pro-brain natriuretic peptide, cardiac index, mixed venous oxygen saturation, and 6-minute walking distance were correlated with the ratio of the number of arteries over veins with vessel diameters of 6-10 mm (Spearman correlation coefficients ρ = 0.64, p < 0.001; ρ = -0.60, p < 0.001; ρ = -0.47, p = 0.005; ρ = -0.45, p = 0.001, respectively). This ratio predicted a low- and high-risk score with an area under the curve of 0.73 (95% confidence interval (CI): 0.56-0.90) and 0.86 (95% CI: 0.74-0.97), respectively. CONCLUSIONS: The ratio of the number of arteries over veins with diameters between 6 and 10 mm is significantly correlated with prognostic markers in pulmonary hypertension and predicts low and high mortality risk.

Metal artifact reduction in patients with total hip replacements: evaluation of clinical photon counting CT using virtual monoenergetic images.

OBJECTIVES: To investigate photon-counting CT (PCCT)-derived virtual monoenergetic images (VMI) for artifact reduction in patients with unilateral total hip replacements (THR). METHODS: Forty-two patients with THR and portal-venous phase PCCT of the abdomen and pelvis were retrospectively included. For the quantitative analysis, region of interest (ROI)-based measurements of hypodense and hyperdense artifacts, as well as of artifact-impaired bone and the urinary bladder, were conducted, and corrected attenuation and image noise were calculated as the difference of attenuation and noise between artifact-impaired and normal tissue. Two radiologists qualitatively evaluated artifact extent, bone assessment, organ assessment, and iliac vessel assessment using 5-point Likert scales. RESULTS: VMI110keV yielded a significant reduction of hypo- and hyperdense artifacts compared to conventional polyenergetic images (CI) and the corrected attenuation closest to 0, indicating best possible artifact reduction (hypodense artifacts: CI: 237.8 ± 71.4 HU, VMI110keV: 8.5 ± 122.5 HU; p < 0.05; hyperdense artifacts: CI: 240.6 ± 40.8 HU vs. VMI110keV: 13.0 ± 110.4 HU; p < 0.05). VMI110keV concordantly provided best artifact reduction in the bone and bladder as well as the lowest corrected image noise. In the qualitative assessment, VMI110keV received the best ratings for artifact extent (CI: 2 (1-3), VMI110keV: 3 (2-4); p < 0.05) and bone assessment (CI: 3 (1-4), VMI110keV: 4 (2-5); p < 0.05), whereas organ and iliac vessel assessments were rated highest in CI and VMI70keV. CONCLUSIONS: PCCT-derived VMI effectively reduce artifacts from THR and thereby improve assessability of circumjacent bone tissue. VMI110keV yielded optimal artifact reduction without overcorrection, yet organ and vessel assessments at that energy level and higher were impaired by loss of contrast. CLINICAL RELEVANCE STATEMENT: PCCT-enabled artifact reduction is a feasible method for improving assessability of the pelvis in patients with total hip replacements at clinical routine imaging. KEY POINTS: • Photon-counting CT-derived virtual monoenergetic images at 110 keV yielded best reduction of hyper- and hypodense artifacts, whereas higher energy levels resulted in artifact overcorrection. • The qualitative artifact extent was reduced best in virtual monoenergetic images at 110 keV, facilitating an improved assessment of the circumjacent bone. • Despite significant artifact reduction, assessment of pelvic organs as well as vessels did not profit from energy levels higher than 70 keV, due to the decline in image contrast.

Artificial Intelligence in Ophthalmology - Status Quo and Future Perspectives.

Artificial intelligence (AI) is an emerging technology in healthcare and holds the potential to disrupt many arms in medical care. In particular, disciplines using medical imaging modalities, including e.g. radiology but ophthalmology as well, are already confronted with a wide variety of AI implications. In ophthalmologic research, AI has demonstrated promising results limited to specific diseases and imaging tools, respectively. Yet, implementation of AI in clinical routine is not widely spread due to availability, heterogeneity in imaging techniques and AI methods. In order to describe the status quo, this narrational review provides a brief introduction to AI ("what the ophthalmologist needs to know"), followed by an overview of different AI-based applications in ophthalmology and a discussion on future challenges.Abbreviations: Age-related macular degeneration, AMD; Artificial intelligence, AI; Anterior segment OCT, AS-OCT; Coronary artery calcium score, CACS; Convolutional neural network, CNN; Deep convolutional neural network, DCNN; Diabetic retinopathy, DR; Machine learning, ML; Optical coherence tomography, OCT; Retinopathy of prematurity, ROP; Support vector machine, SVM; Thyroid-associated ophthalmopathy, TAO.

Two-dimensional CT measurements enable assessment of body composition on head and neck CT.

OBJECTIVES: The aim of this study was to evaluate whether simple 2D measurements in axial slices of head and neck CT examinations correlate with generally established measurements of body composition in abdominal CT at the height of the third lumbar vertebra and thus allow for an estimation of muscle and fat masses. METHODS: One hundred twenty-two patients who underwent concurrent CT of the head and neck and the abdomen between July 2016 and July 2020 were retrospectively included. For a subset of 30 patients, additional bioelectrical impedance analysis (BIA) was available. Areas of paraspinal muscles at the height of the third (C3) and fifth cervical vertebrae (C5) as well as the total cross-sectional area at the height of C3 and at the submandibular level were correlated with the results of abdominal measurements and BIA. Furthermore, intra- and interreader variabilities of all measurements were assessed. RESULTS: Regarding adipose tissue, good correlations were found between the total cross-sectional area of the patient's body at the submandibular level and at the height of C3 between both abdominal measurements and BIA results (r = 0.8-0.92; all p < 0.001). Regarding muscle, the total paraspinal muscle area at the height of C3 and C5 showed strong correlations with abdominal measurements and moderate to strong correlations with BIA results (r = 0.44-0.80; all p < 0.001), with the muscle area on C5 yielding slightly higher correlations. CONCLUSIONS: Body composition information can be obtained with comparable reliability from head and neck CT using simple biplanar measurements as from abdominal CT. KEY POINTS: • The total paraspinal muscle area at the height of C3 and C5 correlates strongly with abdominal muscle mass. • The total cross-sectional area at the submandibular level and at the height of C3 shows good correlations with abdominal fat mass. • The described measurements facilitate a rapid, opportunistic assessment of relevant body composition parameters.

Assessment of Therapy Response to Transarterial Radioembolization for Liver Metastases by Means of Post-treatment MRI-Based Texture Analysis.

INTRODUCTION: To determine whether post-treatment magnetic resonance imaging (MRI)-based texture analysis of liver metastases (LM) may be suited predicting therapy response to transarterial radioembolization (TARE) during follow-up. MATERIALS AND METHODS: Thirty-seven patients with LM treated by TARE (mean age 63.4 years) between January 2006 and December 2014 were identified in this retrospective feasibility study. They underwent dynamic contrast-enhanced and hepatocellular phase MRI after TARE (mean 2.2 days). Response was evaluated on follow-up imaging scheduled in intervals of 3 months (median follow-up, 7.3 months) based on response evaluation criteria in solid tumors 1.1 (RECIST 1.1). Results of texture analysis [mean, standard deviation, skewness (s), kurtosis (k), entropy and uniformity] were compared between patients with progressive disease (PD) and patients with stable disease (SD), partial or complete response (PR/CR). Receiver operating characteristics including the area under the curve (AUC) and cutoff values including the sensitivity and specificity were calculated. RESULTS: According to RECIST 1.1, 24 patients (64.9%) had PD, 8 SD (21.6%) and 5 PR (13.5%). MRI-based texture analysis showed an earlier differentiation between patients with and without PD when compared with RECIST 1.1. Median k (2.88 vs. 2.35) in arterial phase MRI and median s (0.48 vs. 0.25) and k (2.85 vs. 2.25) in venous phase MRI were significantly different (p < 0.05). The AUC for k derived from arterial phase MRI was 0.73 (cutoff = 2.55, sensitivity = 0.83, specificity = 0.62) (p < 0.05). The AUC for s and k in venous phase MRI was 0.76 (cutoff = 0.35, sensitivity = 0.71, specificity = 0.85) (p > 0.05) and 0.83 (cutoff = 2.50, sensitivity = 0.75, specificity = 0.85) (p < 0.05). CONCLUSION: This study indicates the potential of MRI-based texture analysis at arterial and venous phase MRI for the early prediction of PD after TARE. LEVEL OF EVIDENCE: IV.