Artificial intelligence, machine learning and deep learning in musculoskeletal imaging: Current applications.

Artificial intelligence is rapidly expanding in all technological fields. The medical field, and especially diagnostic imaging, has been showing the highest developmental potential. Artificial intelligence aims at human intelligence simulation through the management of complex problems. This review describes the technical background of artificial intelligence, machine learning, and deep learning. The first section illustrates the general potential of artificial intelligence applications in the context of request management, data acquisition, image reconstruction, archiving, and communication systems. In the second section, the prospective of dedicated tools for segmentation, lesion detection, automatic diagnosis, and classification of musculoskeletal disorders is discussed.

Virtual non-calcium dual-energy CT: clinical applications.

Dual-energy CT (DECT) has emerged into clinical routine as an imaging technique with unique postprocessing utilities that improve the evaluation of different body areas. The virtual non-calcium (VNCa) reconstruction algorithm has shown beneficial effects on the depiction of bone marrow pathologies such as bone marrow edema. Its main advantage is the ability to substantially increase the image contrast of structures that are usually covered with calcium mineral, such as calcified vessels or bone marrow, and to depict a large number of traumatic, inflammatory, infiltrative, and degenerative disorders affecting either the spine or the appendicular skeleton. Therefore, VNCa imaging represents another step forward for DECT to image conditions and disorders that usually require the use of more expensive and time-consuming techniques such as magnetic resonance imaging, positron emission tomography/CT, or bone scintigraphy. The aim of this review article is to explain the technical background of VNCa imaging, showcase its applicability in the different body regions, and provide an updated outlook on the clinical impact of this technique, which goes beyond the sole improvement in image quality.

Value of minimum intensity projections for chest CT in COVID-19 patients.

PURPOSE: To investigate whether minimum intensity projection (MinIP) reconstructions enable more accurate depiction of pulmonary ground-glass opacity (GGO) compared to standard transverse sections and multiplanar reformat (MPR) series in patients with suspected coronavirus disease 2019 (COVID-19). METHOD: In this multinational study, chest CT scans of 185 patients were retrospectively analyzed. Diagnostic accuracy, diagnostic confidence, image quality regarding the assessment of GGO, as well as subjective time-efficiency of MinIP and standard MPR series were analyzed based on the assessment of six radiologists. In addition, the suitability for COVID-19 evaluation, image quality regarding GGO and subjective time-efficiency in clinical routine was assessed by five clinicians. RESULTS: The reference standard revealed a total of 149 CT scans with pulmonary GGO. MinIP reconstructions yielded significantly higher sensitivity (99.9 % vs 95.6 %), specificity (95.8 % vs 86.1 %) and accuracy (99.1 % vs 93.8 %) for assessing of GGO compared with standard MPR series. MinIP reconstructions achieved significantly higher ratings by radiologists concerning diagnostic confidence (medians, 5.00 vs 4.00), image quality (medians, 4.00 vs 4.00), contrast between GGO and unaffected lung parenchyma (medians, 5.00 vs 4.00) as well as subjective time-efficiency (medians, 5.00 vs 4.00) compared with MPR-series (all P < .001). Clinicians preferred MinIP reconstructions for COVID-19 assessment (medians, 5.00 vs 3.00), image quality regarding GGO (medians, 5.00 vs 3.00) and subjective time-efficiency in clinical routine (medians, 5.00 vs 3.00). CONCLUSIONS: MinIP reconstructions improve the assessment of COVID-19 in chest CT compared to standard images and may be suitable for routine application.