Fast volume reconstruction from motion corrupted stacks of 2D slices

Capturing an enclosing volume of moving subjects and organs using fast individual image slice acquisition has shown promise in dealing with motion artefacts. Motion between slice acquisitions results in spatial inconsistencies that can be resolved by slice-to-volume reconstruction (SVR) methods to provide high quality 3D image data. Existing algorithms are, however, typically very slow, specialised to specific applications and rely on approximations, which impedes their potential clinical use. In this paper, we present a fast multi-GPU accelerated framework for slice-to-volume reconstruction. It is based on optimised 2D/3D registration, super-resolution with automatic outlier rejection and an additional (optional) intensity bias correction. We introduce a novel and fully automatic procedure for selecting the image stack with least motion to serve as an initial registration target. We evaluate the proposed method using artificial motion corrupted phantom data as well as clinical data, including tracked freehand ultrasound of the liver and fetal Magnetic Resonance Imaging. We achieve speed-up factors greater than 30 compared to a single CPU system and greater than 10 compared to currently available state-of-the-art multi-core CPU methods. We ensure high reconstruction accuracy by exact computation of the point-spread function for every input data point, which has not previously been possible due to computational limitations. Our framework and its implementation is scalable for available computational infrastructures and tests show a speed-up factor of 1.70 for each additional GPU. This paves the way for the online application of image based reconstruction methods during clinical examinations. The source code for the proposed approach is publicly available

Toward Autism-Friendly Magnetic Resonance Imaging: Exploring Autistic Individuals' Experiences of Magnetic Resonance Imaging Scans in the United Kingdom, a Cross-Sectional Survey

BACKGROUND: Autistic individuals might undergo a magnetic resonance imaging (MRI) examination for clinical concerns or research. Increased sensory stimulation, lack of appropriate environmental adjustments, or lack of streamlined communication in the MRI suite may pose challenges to autistic patients and render MRI scans inaccessible. This study aimed at (i) exploring the MRI scan experiences of autistic adults in the United Kingdom; (ii) identifying barriers and enablers toward successful and safe MRI examinations; (iii) assessing autistic individuals' satisfaction with MRI service; and (iv) informing future recommendations for practice improvement. METHODS: We distributed an online survey to the autistic community on social media, using snowball sampling. Inclusion criteria were: being older than 16, have an autism diagnosis or self-diagnosis, self-reported capacity to consent, and having had an MRI scan in the United Kingdom. We used descriptive statistics for demographics, inferential statistics for group comparisons/correlations, and content analysis for qualitative data. RESULTS: We received 112 responses. A total of 29.6% of the respondents reported not being sent any information before the scan. Most participants (68%) confirmed that radiographers provided detailed information on the day of the examination, but only 17.1% reported that radiographers offered some reasonable environmental adjustments. Only 23.2% of them confirmed they disclosed their autistic identity when booking MRI scanning. We found that quality of communication, physical environment, patient emotions, staff training, and confounding societal factors impacted their MRI experiences. Autistic individuals rated their overall MRI experience as neutral and reported high levels of claustrophobia (44.8%). CONCLUSION: This study highlighted a lack of effective communication and coordination of care, either between health care services or between patients and radiographers, and lack of reasonable adjustments as vital for more accessible and person-centered MRI scanning for autistic individuals. Enablers of successful scans included effective communication, adjusted MRI environment, scans tailored to individuals' needs/preferences, and well-trained staff

Beauty is in the AI of the beholder: Are we ready for the clinical integration of Artificial Intelligence in radiography? An exploratory analysis of perceived AI knowledge, skills, confidence, and education perspectives of UK radiographers

The use of artificial intelligence (AI) in medical imaging and radiotherapy has been met with both scepticism and excitement. However, clinical integration of AI is already well-underway. Many authors have recently reported on the AI knowledge and perceptions of radiologists/medical staff and students however there is a paucity of information regarding radiographers. Published literature agrees that AI is likely to have significant impact on radiology practice. As radiographers are at the forefront of radiology service delivery, an awareness of the current level of their perceived knowledge, skills, and confidence in AI is essential to identify any educational needs necessary for successful adoption into practice. The aim of this survey was to determine the perceived knowledge, skills, and confidence in AI amongst UK radiographers and highlight priorities for educational provisions to support a digital healthcare ecosystem. A survey was created on Qualtrics® and promoted via social media (Twitter®/LinkedIn®). This survey was open to all UK radiographers, including students and retired radiographers. Participants were recruited by convenience, snowball sampling. Demographic information was gathered as well as data on the perceived, self-reported, knowledge, skills, and confidence in AI of respondents. Insight into what the participants understand by the term "AI" was gained by means of a free text response. Quantitative analysis was performed using SPSS® and qualitative thematic analysis was performed on NVivo®. Four hundred and eleven responses were collected (80% from diagnostic radiography and 20% from a radiotherapy background), broadly representative of the workforce distribution in the UK. Although many respondents stated that they understood the concept of AI in general (78.7% for diagnostic and 52.1% for therapeutic radiography respondents, respectively) there was a notable lack of sufficient knowledge of AI principles, understanding of AI terminology, skills, and confidence in the use of AI technology. Many participants, 57% of diagnostic and 49% radiotherapy respondents, do not feel adequately trained to implement AI in the clinical setting. Furthermore 52% and 64%, respectively, said they have not developed any skill in AI whilst 62% and 55%, respectively, stated that there is not enough AI training for radiographers. The majority of the respondents indicate that there is an urgent need for further education (77.4% of diagnostic and 73.9% of therapeutic radiographers feeling they have not had adequate training in AI), with many respondents stating that they had to educate themselves to gain some basic AI skills. Notable correlations between confidence in working with AI and gender, age, and highest qualification were reported. Knowledge of AI terminology, principles, and applications by healthcare practitioners is necessary for adoption and integration of AI applications. The results of this survey highlight the perceived lack of knowledge, skills, and confidence for radiographers in applying AI solutions but also underline the need for formalised education on AI to prepare the current and prospective workforce for the upcoming clinical integration of AI in healthcare, to safely and efficiently navigate a digital future. Focus should be given on different needs of learners depending on age, gender, and highest qualification to ensure optimal integration. [Abstract copyright: Copyright © 2021 Rainey, O'Regan, Matthew, Skelton, Woznitza, Chu, Goodman, McConnell, Hughes, Bond, McFadden and Malamateniou.

Resting State fMRI in the moving fetus: A robust framework for motion, bias field and spin history correction

There is growing interest in exploring fetal functional brain development, particularly with Resting State fMRI. However, during a typical fMRI acquisition, the womb moves due to maternal respiration and the fetus may perform large-scale and unpredictable movements. Conventional fMRI processing pipelines, which assume that brain movements are infrequent or at least small, are not suitable. Previous published studies have tackled this problem by adopting conventional methods and discarding as much as 40% or more of the acquired data. In this work, we developed and tested a processing framework for fetal Resting State fMRI, capable of correcting gross motion. The method comprises bias field and spin history corrections in the scanner frame of reference, Combined with slice to volume registration and scattered data interpolation to place all data into a consistent anatomical space. The aim is to recover an ordered set of samples suitable for further analysis using standard tools such as Group Independent Component Analysis (Group ICA). We have tested the approach using simulations and in vivo data acquired at 1.5 T. After full motion correction, Group ICA performed on a population of 8 fetuses extracted 20 networks, 6 of which were identified as matching those previously observed in preterm babies

TIPIT: A randomised controlled trial of thyroxine in preterm infants under 28 weeks gestation: Magnetic Resonance Imaging and Magnetic Resonance Angiography protocol

<p>Abstract </p> <p>Background</p> <p>Infants born at extreme prematurity are at high risk of developmental disability. A major risk factor for disability is having a low level of thyroid hormone described as hypothyroxinaemia, which is recognised to be a frequent phenomenon in these infants. Derangements of critical thyroid function during the sensitive window in prematurity when early development occurs, may have a range of long term effects for brain development. Further research in preterm infants using neuroimaging techniques will increase our understanding of the specificity of the effects of hypothyroxinaemia on the developing foetal brain. This is an explanatory double blinded randomised controlled trial which is aimed to assess the effect of thyroid hormone supplementation on brain size, key brain structures, extent of myelination, white matter integrity and vessel morphology, somatic growth and the hypothalamic-pituitary-adrenal axis.</p> <p>Methods</p> <p>The study is a multi-centred double blinded randomised controlled trial of thyroid hormone supplementation in babies born below 28 weeks' gestation. All infants will receive either levothyroxine or placebo until 32 weeks corrected gestational age. The primary outcomes will be width of the sub-arachnoid space measured using cranial ultrasound and head circumference at 36 weeks corrected gestational age. The secondary outcomes will be thyroid hormone concentrations, the hypothalamic pituitary axis status and auxological data between birth and expected date of delivery; thyroid gland volume, brain size, volumes of key brain structures, extent of myelination and brain vessel morphology at expected date of delivery and markers of morbidity which include duration of mechanical ventilation and/or oxygen requirement and chronic lung disease.</p> <p><b>Trial registration</b></p> <p>Current Controlled Trials ISRCTN89493983</p

Association of cardiovascular risk factors with MRI indices of cerebrovascular structure and function and white matter hyperintensities in young adults

Importance: Risk of stroke and brain atrophy in later life relate to levels of cardiovascular risk in early adulthood. However, it is unknown whether cerebrovascular changes are already present in young adults. Objective: To examine relationships between modifiable cardiovascular risk factors and cerebrovascular structure, function and white matter integrity in young adults. Design, Setting, and Participants: A cross-sectional observational study completed between August 2014 and May 2016 at the University of Oxford, United Kingdom. Participants recruited through active and passive recruitment from the local community, including invitation from the Oxford University Hospitals Hypertension Service. Exposures: Clinic and ambulatory blood pressure (mmHg), body mass index (kg/m2), objective physical activity (hours/week), alcohol intake (drinks/week), smoking (pack years), peak oxygen uptake (ml/kg/min), peak exercise blood 65 pressure (mmHg), lipid profile (mg/dL), insulin resistance and use of anti-66 hypertension medication. 67 Main Outcomes and Measures: Cerebral vessel density (vessels/cm3), caliber (μm) and tortuosity, brain white matter hyperintensity lesion count (number), and in a subgroup (n=52) brain blood arrival time (seconds) and cerebral blood flow (ml/100g/min) assessed by brain magnetic resonance. Results: 125 participants (mean age 25±5 years, 49% female) were recruited. Cerebrovascular morphology and white matter hyperintensity count correlated with cardiovascular risk factors in univariable and multivariable models. In a risk score, for each healthier modifiable risk factor, characterised as: ambulatory blood pressure ; BMI < 25kg/m2; top tertile of cardiovascular fitness; non-smoker; <8 alcoholic drinks/week; normotensive exercise blood pressure response; cholesterol <200mg/dL; and fasting glucose <100mg/dL, vessel density increased by 0.3 vessels/cm3 (95%CI 0.1 to 0.5, p=0.003), vessel caliber by 8μm (95%CI 3 to 13, p=0.01) and white matter hyperintensity lesions reduced by 1.6 lesions (95%CI 0.6 to 2.8, p=0.006). In subgroup analysis, cerebral blood flow varied with vessel density and increased by 2.5ml/min/100g per risk score (95%CI 0.05 to 4.98, p=0.05). Conclusions and Relevance: In this preliminary study, involving young adults without clinical evidence of cerebrovascular disease, modifiable cardiovascular risk factors were associated with MR indices of cerebral vessel structure and function, and white matter hyperintensities. Further research is needed to determine the clinical importance of these findings for the primordial prevention of cerebrovascular disease

Placenta Imaging Workshop 2018 report:Multiscale and multimodal approaches

The Centre for Medical Image Computing (CMIC) at University College London (UCL) hosted a two-day workshop on placenta imaging on April 12th and 13th 2018. The workshop consisted of 10 invited talks, 3 contributed talks, a poster session, a public interaction session and a panel discussion about the future direction of placental imaging. With approximately 50 placental researchers in attendance, the workshop was a platform for engineers, clinicians and medical experts in the field to network and exchange ideas. Attendees had the chance to explore over 20 posters with subjects ranging from the movement of blood within the placenta to the efficient segmentation of fetal MRI using deep learning tools. UCL public engagement specialists also presented a poster, encouraging attendees to learn more about how to engage patients and the public with their research, creating spaces for mutual learning and dialogue

Optimisatin and clinical applications of neonatal magnetic resonance angiography of cerebral vessels at 3 tesla

EThOS - Electronic Theses Online ServiceGBUnited Kingdo