Leveraging Phase Information of 3D Isotropic Ultrashort Echo Time Pulmonary MRI for the Detection of Thoracic Lymphadenopathy: Toward an All-in-One Scan Solution.

Background: Ultrashort echo time (UTE) allows imaging of tissues with short relaxation times, but it comes with the expense of long scan times. Magnitude images of UTE magnetic resonance imaging (MRI) are widely used in pulmonary imaging due to excellent parenchymal signal, but have insufficient contrast for other anatomical regions of the thorax. Our work investigates the value of UTE phase images (UTE-Ps)-generated simultaneously from the acquired UTE signal used for the magnitude images-for the detection of thoracic lymph nodes based on water-fat contrast.

Anticipating and suspending: the chronopolitics of cryopreservation.

The article brings together two disparate and so far largely disconnected bodies of research: the critical analysis of cryopreservation technologies and the debate on modes of anticipation. It starts with a short review of the state of the research on the concept of cryopolitics. In the next part I will suggest two revisions.

Speed and efficiency: evaluating pulmonary nodule detection with AI-enhanced 3D gradient echo imaging.

Objectives: Evaluating the diagnostic feasibility of accelerated pulmonary MR imaging for detection and characterisation of pulmonary nodules with artificial intelligence-aided compressed sensing.

Materials And Methods: In this prospective trial, patients with benign and malignant lung nodules admitted between December 2021 and December 2022 underwent chest CT and pulmonary MRI. Pulmonary MRI used a respiratory-gated 3D gradient echo sequence, accelerated with a combination of parallel imaging, compressed sensing, and deep learning image reconstruction with three different acceleration factors (CS-AI-7, CS-AI-10, and CS-AI-15).

Associations between sleep habits, quality, chronotype and depression in a large cross-sectional sample of Swedish adolescents.

Objective: To investigate behavioral sleep habits, self-perceived quality of sleep, and chronotype, and to examine their association with clinically relevant levels of depression in Swedish adolescents.

Method: Questionnaire data were obtained from a representative sample of Swedish adolescents (n = 8449; 50.8% girls; aged 12-16).

Development and Validation of a Deep Learning Algorithm to Differentiate Colon Carcinoma From Acute Diverticulitis in Computed Tomography Images.

Importance: Differentiating between malignant and benign etiology in large-bowel wall thickening on computed tomography (CT) images can be a challenging task. Artificial intelligence (AI) support systems can improve the diagnostic accuracy of radiologists, as shown for a variety of imaging tasks. Improvements in diagnostic performance, in particular the reduction of false-negative findings, may be useful in patient care.