Presence of the posterior pituitary bright spot sign on MRI in the general population: a comparison between 1.5 and 3T MRI and between 2D-T1 spin-echo- and 3D-T1 gradient-echo sequences.

PURPOSE: To describe the prevalence of the posterior pituitary bright spot (PPBS) in the general population on 1.5 and 3T MRI examinations and on 2D-T1 spin-echo (SE) and 3D-T1 gradient-echo (GE) sequences. MATERIALS AND METHODS: 1017 subjects who received an MRI of the brain for aspecific neurological complaints were included. MRI was performed on 1.5T in 64.5% and on 3T in 35.5% of subjects. Presence of the PPBS was evaluated on sagittal 2D T1-SE echo images with slice thickness 3 mm in 67.5% and on sagittal 3D T1-GE with slice thickness 0.9 mm in 32.5% of subjects. RESULTS: The PPBS was detectable in 95.9% of subjects. After correction for sex and age, no statistically significant difference could be seen concerning PPBS detection between 1.5 and 3T MRI examinations (p = 0.533), nor between 2D T1-SE and 3D T1-GE sequences (p = 0.217). There was a statistically significant association between increasing age and the absence of the PPBS (p < 0.001). The PPBS could not be identified in 6.2% of male subjects, compared to 2.2% of female subjects (p = 0.01). DISCUSSION: Absence of the PPBS can be seen in 4.1% of patients undergoing MRI of the brain for non-endocrinological reasons. Neither field-strength nor the use of a thick-sliced 2D T1-SE versus a thin-sliced 3D T1-GE sequence influenced the detectability of the PPBS. There is a statistically significant association between increasing age and male sex and the absence of the PPBS.

Software solutions alone cannot guarantee useful radiology requests.

Background The availability of clinical information and a pertinent clinical question can improve the diagnostic accuracy of the imaging process. Purpose To examine if an electronic request form forcing referring clinicians to provide separate input of both clinical information and a clinical question can improve the quality of the request. Material and Methods A total of 607 request forms in the clinical worklists for a computed tomography (CT) scan of the thorax, the abdomen or their combination, were examined. Using software of our own making, we examined the presence of clinical information and a clinical question before and after the introduction of a new, more compelling order method. We scored and compared the quality of the clinical information and the clinical question between the two systems and we examined the effect on productivity. Results Both clinical information and a clinical question were present in 76.7% of cases under the old system and in 95.3% under the new system ( P < 0.001). Individual characteristics of the clinical information and the clinical question however, with the exception of incompleteness, showed little improvement under the new system. There was also no significant difference between the two systems in the number of requests requiring further search. Conclusion The introduction of electronic radiology request forms compelling referring clinicians to provide separate input of clinical information and a clinical question provides only limited benefit to the quality of the request. Raising awareness among clinicians of the importance of a well-written request remains essential.

Declutter the MRI protocol tree: Managing and comparing sequence parameters of multiple clinical Siemens MRI systems.

An MRI protocol tree on a clinical MRI system is a large database containing hundreds of protocols, each containing multiple sequences, and up to 900 parameters per sequence. Protocol variation between scan sessions or patients must be avoided as much as possible, as it may lead to financial loss and less than optimal outcomes for the patient. Without proper management, protocol variation and errors in MRI protocol trees are easily introduced and may remain undetected, leading to a cluttered protocol tree. This in turn reduces the efficiency of the radiological MRI workflow. We introduce a method and open-source software tools for managing MRI protocols on a sequence parameter level, which can detect deviations and variations in the protocol tree. It can be used offline, away from the scanner console, without disturbing the clinical workflow. These tools help to create a standardized protocol library across multiple MRI scanners, reducing variation and errors, enabling radiology departments to create optimal value for the patient and institution.

Monitoring the PACS implementation process in a large university hospital--discrepancies between radiologists and physicians.

Successfully introducing a new technology in a health-care setting is not a walk in the park. Many barriers need to be overcome, not only technical and financial but also human barriers. In this study, we focus on the human barriers to health-care information systems' implementation. We monitored the acceptance of a Picture Archiving and Communication System (PACS) by radiologists and hospital physicians in a large Belgian university hospital. Hereto, questionnaires were taken pre-implementation (T1) and 1 year after the radiology department stopped printing film (T2). The framework we used to perform the study was the Unified Theory of Acceptance and Use of Technology. Main findings were that both groups were positive toward PACS prior to the introduction and that each group was even more positive at T2 with extensive PACS experience. In general, the ratings of the radiologists were higher than those of the physicians, as the radiologists experienced more of the benefits of PACS and had to use PACS throughout the day. Two factors were salient for predicting users' intention to use PACS: the usefulness of PACS (performance expectancy) and the availability of support of any kind (facilitating conditions). The results show that our approach was successful. Both radiologists and physicians give evidence of an excellent level of user acceptance. We can conclude that the implementation of PACS into our hospital has succeeded.

Automated Assessment of Bone Age Using Deep Learning and Gaussian Process Regression.

Bone age is an essential measure of skeletal maturity in children with growth disorders. It is typically assessed by a trained physician using radiographs of the hand and a reference model. However, it has been described that the reference models leave room for interpretation leading to a large inter-observer and intra-observer variation. In this work, we explore a novel method for automated bone age assessment to assist physicians with their estimation. It consists of a powerful combination of deep learning and Gaussian process regression. Using this combination, sensitivity of the deep learning model to rotations and flips of the input images can be exploited to increase overall predictive performance compared to only using the deep learning network. We validate our approach retrospectively on a set of 12611 radiographs of patients between 0 and 19 years of age.

Do hospital physicians' attitudes change during PACS implementation? A cross-sectional acceptance study.

PURPOSE: The purpose of this study is to gain a better insight into the reasons why hospital physicians accept and use a Picture Archiving and Communication System (PACS). Two research questions are put forward, pertaining to (1) factors that contribute to physicians' acceptance of PACS, and (2) whether these factors change as physicians gain experience in using PACS. METHODS: Questionnaires were administered at three moments in time during the PACS implementation process in a private hospital: just before its introduction (T1), four months later (T2), and about fifteen months after the introduction of PACS (T3). The Unified Theory of Acceptance and Use of Technology was chosen as the theoretical framework for this study. Hence, the following scales were measured: performance expectancy, effort expectancy, social influence, facilitating conditions, behavioral intention, and self-reported frequency of use. RESULTS: Forty-six usable responses were obtained at T1, 52 at T2 and 61 at T3. Three variables directly influenced PACS acceptance (measured as behavioral intention and use of PACS): effort expectancy, performance expectancy, and social influence; and their influence evolved over time. Effort expectancy was of particular importance at T1, whereas performance expectancy influenced acceptance at T2 and T3; social influence was the only consistent predictor of PACS acceptance at all times. Variance explained in behavioral intention ranged from .26 at T1 to .58 at T3. CONCLUSIONS: In this setting, the main motivation for physicians to start using PACS is effort expectancy, whereas performance expectancy only becomes important after the physicians started using PACS. It is also very important that physicians perceive that their social environment encourages the use of PACS.