Search

The radiologic evaluation of the sagittal angulation of the distal humerus is commonly based on standard lateral radiographs. However, lateral radiographs do not allow to examine the lateral angulation of the capitulum and the trochlea, separately. Although this problem could be approached via computed tomography, there are no data available describing the difference between the angulation of the capitulum and trochlea. Therefore, we aimed to assess sagittal angles of the capitulum and trochlea in relation to the humeral shaft based on 400 CT-scans of the elbow in healthy adults. Angles were measured in sagittal planes at the capitulum center and three anatomically defined trochlea locations and were spanned between the axis of the joint component and the humerus shaft. Angles were tested for differences between measurement locations and correlation with patient characteristics (age, sex, trans-epicondylar distance). Angles increased from lateral to medial measurement locations (107.4 ± 9.6°, 167.4 ± 8.2°, 171.8 ± 7.3°, 179.1 ± 7.0°; p p > 0.05). Intra-rater-reliability was r = 0.79–0.86. As CT-imaging allows to distinguish between sagittal capitulum and trochlea locations, it might benefit the radiologic diagnostic of sagittal malalignments of the distal humerus at the capitulum and trochlea, separately.

Objectives: To evaluate the feasibility of combining compressed sense (CS) with a newly developed deep learning-based algorithm (CS-AI) using convolutional neural networks to accelerate 2D MRI of the knee. Methods: In this prospective study, 20 healthy volunteers were scanned with a 3T MRI scanner. All subjects received a fat-saturated sagittal 2D proton density reference sequence without acceleration and four additional acquisitions with different acceleration levels: 2, 3, 4 and 6. All sequences were reconstructed with the conventional CS and a new CS-AI algorithm. Two independent, blinded readers rated all images by seven criteria (overall image impression, visible artifacts, delineation of anterior ligament, posterior ligament, menisci, cartilage, and bone) using a 5-point Likert scale. Signal- and contrast-to-noise ratios were calculated. Subjective ratings and quantitative metrics were compared between CS and CS-AI with similar acceleration levels and between all CS/CS-AI images and the non-accelerated reference sequence. Friedman and Dunn´s multiple comparison tests were used for subjective, ANOVA and the Tukey Kramer test for quantitative metrics. Results: Conventional CS images at the lowest acceleration level (CS2) were already rated significantly lower than reference for 6/7 criteria. CS-AI images maintained similar image quality to the reference up to CS-AI three for all criteria, which would allow for a reduction in scan time of 64% with unchanged image quality compared to the unaccelerated sequence. SNR and CNR were significantly higher for all CS-AI reconstructions compared to CS (all p < 0.05). Conclusions AI-based image reconstruction showed higher image quality than CS for 2D knee imaging. Its implementation in the clinical routine yields the potential for faster MRI acquisition but needs further validation in non-healthy study subjects. Advances in knowledge Combining compressed SENSE with a newly developed deep learning-based algorithm using convolutional neural networks allows a 64% reduction in scan time for 2D imaging of the knee. Implementation of the new deep learning-based algorithm in clinical routine in near future should enable better image quality/resolution with constant scan time, or reduced acquisition times while maintaining diagnostic quality.

ZusammenfassungDas Ellenbogengelenk wird durch die beteiligten Knochen, Bandstrukturen und Sehnen von Extensoren und Flexoren stabilisiert. Die Instabilität des Gelenks ist nicht genau definiert, daher spielt die Bildgebung für die genaue Diagnostik eine entscheidende Rolle. Dieser Beitrag stellt die beteiligten Strukturen, die Formen der Instabilität und die diagnostischen Möglichkeiten im Detail vor.

Objectives: Positron emission tomography (PET) is currently considered the non-invasive reference standard for lymph node (N-)staging in lung cancer. However, not all patients can undergo this diagnostic procedure due to high costs, limited availability, and additional radiation exposure. The purpose of this study was to predict the PET result from traditional contrast-enhanced computed tomography (CT) and to test different feature extraction strategies. Methods: In this study, 100 lung cancer patients underwent a contrast-enhanced 18F-fluorodeoxyglucose (FDG) PET/CT scan between August 2012 and December 2019. We trained machine learning models to predict FDG uptake in the subsequent PET scan. Model inputs were composed of (i) traditional “hand-crafted” radiomics features from the segmented lymph nodes, (ii) deep features derived from a pretrained EfficientNet-CNN, and (iii) a hybrid approach combining (i) and (ii). Results: In total, 2734 lymph nodes [555 (20.3%) PET-positive] from 100 patients [49% female; mean age 65, SD: 14] with lung cancer (60% adenocarcinoma, 21% plate epithelial carcinoma, 8% small-cell lung cancer) were included in this study. The area under the receiver operating characteristic curve (AUC) ranged from 0.79 to 0.87, and the scaled Brier score (SBS) ranged from 16 to 36%. The random forest model (iii) yielded the best results [AUC 0.871 (0.865–0.878), SBS 35.8 (34.2–37.2)] and had significantly higher model performance than both approaches alone (AUC: p < 0.001, z = 8.8 and z = 22.4; SBS: p < 0.001, z = 11.4 and z = 26.6, against (i) and (ii), respectively). Conclusion: Both traditional radiomics features and transfer-learning deep radiomics features provide relevant and complementary information for non-invasive N-staging in lung cancer.

Background In oncology, the correct determination of nodal metastatic disease is essential for patient management, as patient treatment and prognosis are closely linked to the stage of the disease. The aim of the study was to develop a tool for automatic 3D detection and segmentation of lymph nodes (LNs) in computed tomography (CT) scans of the thorax using a fully convolutional neural network based on 3D foveal patches. Methods The training dataset was collected from the Computed Tomography Lymph Nodes Collection of the Cancer Imaging Archive, containing 89 contrast-enhanced CT scans of the thorax. A total number of 4275 LNs was segmented semi-automatically by a radiologist, assessing the entire 3D volume of the LNs. Using this data, a fully convolutional neuronal network based on 3D foveal patches was trained with fourfold cross-validation. Testing was performed on an unseen dataset containing 15 contrast-enhanced CT scans of patients who were referred upon suspicion or for staging of bronchial carcinoma. Results The algorithm achieved a good overall performance with a total detection rate of 76.9% for enlarged LNs during fourfold cross-validation in the training dataset with 10.3 false-positives per volume and of 69.9% in the unseen testing dataset. In the training dataset a better detection rate was observed for enlarged LNs compared to smaller LNs, the detection rate for LNs with a short-axis diameter (SAD) ≥ 20 mm and SAD 5–10 mm being 91.6% and 62.2% (p Conclusions The proposed 3D deep learning approach achieves an overall good performance in the automatic detection and segmentation of thoracic LNs and shows reasonable generalizability, yielding the potential to facilitate detection during routine clinical work and to enable radiomics research without observer-bias.

To determine if anemia can be predicted on enhanced computed tomography (CT) examinations of the thorax using virtual non-contrast (VNC) images, in order to support clinicians especially in diagnosing primary asymptomatic patients in daily routine.In this monocentric study, 100 consecutive patients (50 with proven anemia), who underwent a contrast-enhanced CT examination of the thorax due to various indications were included. Attenuation was measured in the descending thoracic aorta, the intraventricular septum, and the left ventricle cavity both in the conventional contrast-enhanced and in the VNC images.Two experienced radiologists annotated the delineation of a dense interventricular septum or a hyperattenuating aortic wall sign for all patients.Hemoglobin levels were then correlated with the measured attenuation values, as well as the visualization of the aortic wall or interventricular septum.Good correlation was shown between hemoglobin levels and CT attenuation values of the left ventricular cavity (r = .59), aorta (r = .56), and ratio between left ventricular cavity and the intraventricular septum (r = .57). Receiver operating characteristic curve revealed ≤ 36.5 hounsfield units (left ventricular cavity) as the threshold for diagnosing anemia. Predicting anemia by visualization of a hyperattenuating aortic wall or a dense interventricular septum yielded a specificity of 98% and 92%, respectively.Predicting anemia on enhanced CT examinations using VNC is feasible. A threshold value of ≤ 36.5 hounsfield units (left ventricular cavity) best defines anemia. Aortic wall or interventricular septum visualization on VNC is a specific anemia indicator.

Purpose To develop a deep-learning (DL)-based approach for thoracic lymph node (LN) mapping based on their anatomical location. Method The training-and validation-dataset included 89 contrast-enhanced computed tomography (CT) scans of the chest. 4201 LNs were semi-automatically segmented and then assigned to LN levels according to their anatomical location. The LN level classification task was addressed by a multi-class segmentation procedure using a fully convolutional neural network. Mapping was performed by firstly determining potential level affiliation for each voxel and then performing majority voting over all voxels belonging to each LN. Mean classification accuracies on the validation data were calculated separately for each level and overall Top-1, Top-2 and Top-3 scores were determined, where a Top-X score describes how often the annotated class was within the top-X predictions. To demonstrate the clinical applicability of our model, we tested its N-staging capabilities in a simulated clinical use case scenario assuming a patient diseased with lung cancer. Results The artificial intelligence(AI)-based assignment revealed mean classification accuracies of 86.36 % (Top-1), 94.48 % (Top-2) and 96.10 % (Top-3). Best accuracies were achieved for LNs in the subcarinal level 7 (98.31 %) and axillary region (98.74 %). The highest misclassification rates were observed among LNs in adjacent levels. The proof-of-principle application in a simulated clinical use case scenario for automated tumor N-staging showed a mean classification accuracy of up to 96.14 % (Top-1). Conclusions The proposed AI approach for automatic classification of LN levels in chest CT as well as the proof-of-principle-experiment for automatic N-staging, revealed promising results, warranting large-scale validation for clinical application.

PURPOSE The aims of this study were to evaluate spectral detector CT (SDCT)-derived iodine concentration (IC) of lymph nodes diagnosed as metastatic and benign in prostate-specific membrane antigen (PSMA) PET/CT and to assess its potential use for lymph node assessment in prostate cancer. PATIENTS AND METHODS Thirty-four prostate cancer patients were retrospectively included: 16 patients with and 18 without lymph node metastases as determined by PSMA PET/CT. Patients underwent PSMA PET/CT as well as portal venous phase abdominal SDCT for clinical cancer follow-up. Only scan pairs with a stable nodal status indicated by constant size as well as comparable prostate-specific antigen (PSA) levels were included. One hundred benign and 96 suspected metastatic lymph nodes were annotated and correlated between SDCT and PSMA PET/CT. Iodine concentration in SDCT-derived iodine maps and SUVmax in ultra-high definition reconstructions from PSMA PET/CT were acquired based on the region of interest. RESULTS Metastatic lymph nodes as per PSMA PET/CT showed higher IC than nonmetastatic nodes (1.9 ± 0.6 mg/mL vs 1.5 ± 0.5 mg/mL, P < 0.05) resulting in an AUC of 0.72 and sensitivity/specificity of 81.3%/58.5%. The mean short axis diameter of metastatic lymph nodes was larger than that of nonmetastatic nodes (6.9 ± 3.6 mm vs 5.3 ± 1.3 mm; P < 0.05); a size threshold of 1 cm short axis diameter resulted in a sensitivity/specificity of 12.8%/99.0%. There was a significant yet weak positive correlation between SUVmax and IC (rs = 0.25; P < 0.001). CONCLUSIONS Spectral detector CT-derived IC was increased in lymph nodes diagnosed as metastatic in PSMA PET/CT yet showed considerable data overlap. The correlation between IC and SUVmax was weak, highlighting the role of PSMA PET/CT as important reference imaging modality for detection of lymph node metastases in prostate cancer patients.

To evaluate potential clinical acceleration factors of Compressed SENSE (CS)Twenty healthy volunteers were scanned with a 3 T scanner, all receiving a standard, fs 2D PD, three CS (CS 2, CS 3, CS 5) as well as time-equivalent SENSE accelerations (S 2, S 3, S 5). The fs 3D PD sequence was acquired with four CS (CS 6, CS 8, CS 10, CS 15) and equivalent SENSE (S 5.72, S 7.69, S 9.57, S 14) factors. Three independent readers rated the images. Signal-to-noise, contrast-to-noise, root-mean-square error and structural similarity index were analyzed for objective evaluation.Scan time decreased with increasing CS factor (2D CS 2: 145 s, 2D CS 3: 95 s, 2D CS 5: 57 s, 3D CS 6: 293 s, 3D CS 8: 220 s, 3D CS 10: 176 s, 3D CS 15: 119 s). The 2D standard sequence was rated best for diagnostic certainty and overall image impression with an average of 4.97 ± 0.10 and 4.80 ± 0.24 (all p 0.05), except for 2D CS 2 and 2D S 2. For the 3D sequences, the standard sequence performed better for both parameters for CS 15, S 9.57 and S 4, as well as S 7.69 for overall image impression while CS 8 was non-inferior for all tested criteria and CS 10 only inferior for delineation of the anterior cruciate ligament, both outperforming the time-equivalent SENSE accelerations.Compressed SENSE can significantly decrease (34.39 % for 2D CS 2 and 54.17 % for 3D CS 10) scan time in knee imaging with unchanged diagnostic certainty and overall image impression compared to the clinical reference.

The assessment of lymph nodes in CT examinations of cancer patients is essential for cancer staging with direct impact on therapeutic decisions. Automated detection and segmentation of lymph nodes is challenging, especially, due to significant variability in size, shape and location coupled with weak and variable image contrast. In this paper, we propose a joint detection and segmentation approach using a fully convolutional neural network based on 3D foveal patches. To enable network training, 89 publicly available CT data sets were carefully re-annotated yielding an extensive set of 4351 voxel-wise segmentations of thoracic lymph nodes. Based on these annotations, the 3D network was trained to perform per voxel classification. For enlarged potentially malignant lymph nodes, a detection rate of 79% with 8.0 false-positive detections per volume was obtained. A DICE of 0.44 was achieved on average.

Objectives To investigate the utilization of virtual mono-energetic images (MonoE) at low kiloelectron volt (keV) levels derived from a dual-layer spectral detector CT (SDCT) for the assessment of abdominal arteries in venous contrast phase scans using arterial phase imaging as an internal reference standard. Materials and methods A total of 50 patients who received arterial and venous phase imaging of the abdomen on a SDCT system were included in this study. Absolute attenuation, noise, signal- and contrast to noise ratios (SNR; CNR) as well as arterial diameters in defined landmarks were assessed. In arterial phase, conventional reconstructions (CRART) as well as MonoEART at 40 keV and in venous phase, conventional reconstructions (CRVEN) as well as MonoEVEN at 70 and 40 keV were investigated and intra-individual comparisons were performed. If an artery stenosis (10 patients) was present, the degree of stenosis was assessed according to the system of the North American Symptomatic Carotid Endarterectomy Trial (NASCET). Results MonoE 40 keV yielded significantly higher attenuation values (in arterial as well as in venous phase) compared to CRART (p Luminal diameters were significantly smaller in MonoE 40 keV in both contrast phases compared to CRART (p Conclusion MonoE at low keV of venous contrast phase scans derived from a novel SDCT are suitable for the assessment of arteries in the abdomen and subsequent stenosis assessment. However, MonoE at 40 keV constantly showed significant smaller luminal diameters than the corresponding conventional reconstructions (including the reference standard). This is possibly due to an improved differentiation of the vessel lumen from the wall and raises the question, which imaging technique should be used as an appropriate reference standard for vascular SDCT imaging studies.

OBJECTIVE We aimed to determine optimal window settings for conventional polyenergetic (PolyE) and virtual monoenergetic images (MonoE) derived from abdominal portal venous phase computed tomography (CT) examinations on a novel dual-layer spectral-detector CT (SDCT). METHODS From 50 patients, SDCT data sets MonoE at 40 kiloelectron volt as well as PolyE were reconstructed and best individual window width and level values manually were assessed separately for evaluation of abdominal arteries as well as for liver lesions. Via regression analysis, optimized individual values were mathematically calculated. Subjective image quality parameters, vessel, and liver lesion diameters were measured to determine influences of different W/L settings. RESULTS Attenuation and contrast-to-noise values were significantly higher in MonoE compared with PolyE. Compared with standard settings, almost all adjusted W/L settings varied significantly and yielded higher subjective scoring. No differences were found between manually adjusted and mathematically calculated W/L settings. CONCLUSIONS PolyE and MonoE from abdominal portal venous phase SDCT examinations require appropriate W/L settings depending on reconstruction technique and assessment focus.

We aimed to determine optimal window settings for poly-energetic (PolyE) and virtual mono-energetic images (MonoE) derived from abdominal angiographic studies on a novel dual-layer spectral detector CT (SDCT) system. From 50 patients, SDCT datasets PolyE and MonoE at 70 and 40 keV levels were reconstructed and best individual window width and level (BI-W/L) manually assessed. Through regression analysis, the so-called optimized individual (OI-W/L) values were obtained. Subjective image quality parameters and vessel diameters were measured to determine influences of different W/L settings. Image noise was lower and attenuation and contrast-to-noise ratio were higher in MonoE compared to PolyE (all p ≤ 0.002). Mean BI-W/L values for PolyE, 70, and 40 keV were 637/284, 647/291, and 1568/691, respectively. Mean OI-W/L values were 631/276, 628/286, and 1516/667, respectively. Compared to standard settings, all adjusted W/L settings varied significantly and yielded higher subjective scoring. No between-group differences were found between manually adjusted and mathematically calculated W/L settings. PolyE and MonoE from abdominal angiographic SDCT studies require appropriate W/L settings especially at low energy reconstruction levels. Individual adjustment reaches the best image quality but is time consuming. From our data, predefined W/L settings of 640/280 (PolyE/MonoE 70 keV) and 1570/690 (MonoE 40 keV) as a non-individualized starting point for abdominal angiographic studies from the novel SDCT system are suggested.

Objective:The aim of this study was to determine optimal window settings for conventional polyenergetic and virtual monoenergetic images derived from computed tomography pulmonary angiogram (CTPA) examinations of a novel dual-layer spectral detector computed tomography system (DLCT).Methods