Magnetic resonance imaging for brain stereotactic radiotherapy : A review of requirements and pitfalls.

Due to its superior soft tissue contrast, magnetic resonance imaging (MRI) is essential for many radiotherapy treatment indications. This is especially true for treatment planning in intracranial tumors, where MRI has a long-standing history for target delineation in clinical practice. Despite its routine use, care has to be taken when selecting and acquiring MRI studies for the purpose of radiotherapy treatment planning. Requirements on MRI are particularly demanding for intracranial stereotactic radiotherapy, where accurate imaging has a critical role in treatment success. However, MR images acquired for routine radiological assessment are frequently unsuitable for high-precision stereotactic radiotherapy as the requirements for imaging are significantly different for radiotherapy planning and diagnostic radiology. To assure that optimal imaging is used for treatment planning, the radiation oncologist needs proper knowledge of the most important requirements concerning the use of MRI in brain stereotactic radiotherapy. In the present review, we summarize and discuss the most relevant issues when using MR images for target volume delineation in intracranial stereotactic radiotherapy.

Mobile Workflow in Computed Tomography of the Chest.

A CT system with a tablet as mobile user interface and a wireless remote control for positioning and radiation release has recently been presented. Our aim was to evaluate the effects of a mobile CT examination workflow on the radiographers' performance compared to conventional examinations. A prototype of a radiation protection cabin was installed besides the gantry of a CT system. The CT system was equipped with a simplified user interface on a portable tablet and a mobile remote control. 98 patients with an indication for CT of the chest were randomly assigned to examination using the mobile devices (study group, n = 47) or using the conventional stationary workflow on the console (reference group, n = 51). Three ceiling mounted fisheye cameras were used for motion tracking of the radiographers, two in the examination room and one in the control room. Relative density of detection heat-maps and area counts were assessed using a dedicated software tool to quantify radiographers' movements. Duration of each task of the examination was manually recorded using a stopwatch. In the reference group 25% of the area counts were located inside of the examination room, while it was 48% in the study group. The time spent in the same room with the patient increased from 3:06 min (29%) to 6:01 min (57%) using the mobile workflow (p < 0.05), thereof 0:59 min (9%) were spent in moderate separation with maintained voice and visual contact in the radiation protection cabin. Heat-maps showed an increase of the radiographer's working area, indicating a higher freedom of movement. Total duration of the examination was slightly less in the study group without statistical significance (median time: study 10:36, reference 10:50 min; p = 0.29). A mobile CT examination transfers the radiographers' interaction with the scanner from the control room into the examination room. There, radiographers' freedom of movement is higher, without any tradeoffs regarding the examination duration.

Diagnostic Accuracy and Performance Analysis of a Scanner-Integrated Artificial Intelligence Model for the Detection of Intracranial Hemorrhages in a Traumatology Emergency Department.

Intracranial hemorrhages require an immediate diagnosis to optimize patient management and outcomes, and CT is the modality of choice in the emergency setting. We aimed to evaluate the performance of the first scanner-integrated artificial intelligence algorithm to detect brain hemorrhages in a routine clinical setting. This retrospective study includes 435 consecutive non-contrast head CT scans. Automatic brain hemorrhage detection was calculated as a separate reconstruction job in all cases. The radiological report (RR) was always conducted by a radiology resident and finalized by a senior radiologist. Additionally, a team of two radiologists reviewed the datasets retrospectively, taking additional information like the clinical record, course, and final diagnosis into account. This consensus reading served as a reference. Statistics were carried out for diagnostic accuracy. Brain hemorrhage detection was executed successfully in 432/435 (99%) of patient cases. The AI algorithm and reference standard were consistent in 392 (90.7%) cases. One false-negative case was identified within the 52 positive cases. However, 39 positive detections turned out to be false positives. The diagnostic performance was calculated as a sensitivity of 98.1%, specificity of 89.7%, positive predictive value of 56.7%, and negative predictive value (NPV) of 99.7%. The execution of scanner-integrated AI detection of brain hemorrhages is feasible and robust. The diagnostic accuracy has a high specificity and a very high negative predictive value and sensitivity. However, many false-positive findings resulted in a relatively moderate positive predictive value.