Deep learning to automate the labelling of head MRI datasets for computer vision applications.

OBJECTIVES: The purpose of this study was to build a deep learning model to derive labels from neuroradiology reports and assign these to the corresponding examinations, overcoming a bottleneck to computer vision model development. METHODS: Reference-standard labels were generated by a team of neuroradiologists for model training and evaluation. Three thousand examinations were labelled for the presence or absence of any abnormality by manually scrutinising the corresponding radiology reports ('reference-standard report labels'); a subset of these examinations (n = 250) were assigned 'reference-standard image labels' by interrogating the actual images. Separately, 2000 reports were labelled for the presence or absence of 7 specialised categories of abnormality (acute stroke, mass, atrophy, vascular abnormality, small vessel disease, white matter inflammation, encephalomalacia), with a subset of these examinations (n = 700) also assigned reference-standard image labels. A deep learning model was trained using labelled reports and validated in two ways: comparing predicted labels to (i) reference-standard report labels and (ii) reference-standard image labels. The area under the receiver operating characteristic curve (AUC-ROC) was used to quantify model performance. Accuracy, sensitivity, specificity, and F1 score were also calculated. RESULTS: Accurate classification (AUC-ROC > 0.95) was achieved for all categories when tested against reference-standard report labels. A drop in performance (ΔAUC-ROC > 0.02) was seen for three categories (atrophy, encephalomalacia, vascular) when tested against reference-standard image labels, highlighting discrepancies in the original reports. Once trained, the model assigned labels to 121,556 examinations in under 30 min. CONCLUSIONS: Our model accurately classifies head MRI examinations, enabling automated dataset labelling for downstream computer vision applications. KEY POINTS: • Deep learning is poised to revolutionise image recognition tasks in radiology; however, a barrier to clinical adoption is the difficulty of obtaining large labelled datasets for model training. • We demonstrate a deep learning model which can derive labels from neuroradiology reports and assign these to the corresponding examinations at scale, facilitating the development of downstream computer vision models. • We rigorously tested our model by comparing labels predicted on the basis of neuroradiology reports with two sets of reference-standard labels: (1) labels derived by manually scrutinising each radiology report and (2) labels derived by interrogating the actual images.

What brain abnormalities can magnetic resonance imaging detect in foetal and early neonatal spina bifida: a systematic review.

PURPOSE: Open spina bifida (OSB) encompasses a wide spectrum of intracranial abnormalities. With foetal surgery as a new treatment option, robust intracranial imaging is important for comprehensive preoperative evaluation and prognostication. We aimed to determine the incidence of infratentorial and supratentorial findings detected by magnetic resonance imaging (MRI) alone and MRI compared to ultrasound. METHODS: Two systematic reviews comparing MRI to ultrasound and MRI alone were conducted on MEDLINE, EMBASE, and Cochrane databases identifying studies of foetal OSB from 2000 to 2020. Intracranial imaging findings were analysed at ≤ 26 or > 26 weeks gestation and neonates (≤ 28 days). Data was independently extracted by two reviewers and meta-analysis was performed where possible. RESULTS: Thirty-six studies reported brain abnormalities detected by MRI alone in patients who previously had an ultrasound. Callosal dysgenesis was identified in 4/29 cases (2 foetuses ≤ 26 weeks, 1 foetus under any gestation, and 1 neonate ≤ 28 days) (15.1%, CI:5.7-34.3%). Heterotopia was identified in 7/40 foetuses ≤ 26 weeks (19.8%, CI:7.7-42.2%), 9/36 foetuses > 26 weeks (25.3%, CI:13.7-41.9%), and 64/250 neonates ≤ 28 days (26.9%, CI:15.3-42.8%). Additional abnormalities included aberrant cortical folding and other Chiari II malformation findings such as lower cervicomedullary kink level, tectal beaking, and hypoplastic tentorium. Eight studies compared MRI directly to ultrasound, but due to reporting inconsistencies, it was not possible to meta-analyse. CONCLUSION: MRI is able to detect anomalies hitherto underestimated in foetal OSB which may be important for case selection. In view of increasing prenatal OSB surgery, further studies are required to assess developmental consequences of these findings.

Clinical relevance of cortical network dynamics in early primary progressive MS.

BACKGROUND: Structural cortical networks (SCNs) reflect the covariance between the cortical thickness of different brain regions, which may share common functions and a common developmental evolution. SCNs appear abnormal in neurodegenerative conditions such as Alzheimer's and Parkinson's diseases, but have never been assessed in primary progressive multiple sclerosis (PPMS). OBJECTIVE: The aim of this study was to test whether SCNs are abnormal in early PPMS and change over 5 years, and correlate with disability worsening. METHODS: A total of 29 PPMS patients and 13 healthy controls underwent clinical and brain magnetic resonance imaging (MRI) assessments for 5 years. Baseline and 5-year follow-up cortical thickness values were obtained and used to build correlation matrices, considered as weighted graphs to obtain network metrics. Bootstrap-based statistics assessed SCN differences between patients and controls and between patients with fast and slow progression. RESULTS: At baseline, patients showed features of lower connectivity (p = 0.02) and efficiency (p < 0.001) than controls. Over 5 years, patients, especially those with fastest clinical progression, showed significant changes suggesting an increase in network connectivity (p < 0.001) and efficiency (p < 0.02), not observed in controls. CONCLUSION: SCNs are abnormal in early PPMS. Longitudinal SCN changes demonstrated a switch from low- to high-efficiency networks especially among fast progressors, indicating their clinical relevance.

Single-subject structural cortical networks in clinically isolated syndrome.

BACKGROUND: Structural cortical networks (SCNs) represent patterns of coordinated morphological modifications in cortical areas, and they present the advantage of being extracted from previously acquired clinical magnetic resonance imaging (MRI) scans. SCNs have shown pathophysiological changes in many brain disorders, including multiple sclerosis. OBJECTIVE: To investigate alterations of SCNs at the individual level in patients with clinically isolated syndrome (CIS), thereby assessing their clinical relevance. METHODS: We analyzed baseline data collected in a prospective multicenter (MAGNIMS) study. CIS patients (n = 60) and healthy controls (n = 38) underwent high-resolution 3T MRI. Measures of disability and cognitive processing were obtained for patients. Single-subject SCNs were extracted from brain 3D-T1 weighted sequences; global and local network parameters were computed. RESULTS: Compared to healthy controls, CIS patients showed altered small-world topology, an efficient network organization combining dense local clustering with relatively few long-distance connections. These disruptions were worse for patients with higher lesion load and worse cognitive processing speed. Alterations of centrality measures and clustering of connections were observed in specific cortical areas in CIS patients when compared with healthy controls. CONCLUSION: Our study indicates that SCNs can be used to demonstrate clinically relevant alterations of connectivity in CIS.

Early microgliosis precedes neuronal loss and behavioural impairment in mice with a frontotemporal dementia-causing CHMP2B mutation.

Frontotemporal dementia (FTD)-causing mutations in the CHMP2B gene lead to the generation of mutant C-terminally truncated CHMP2B. We report that transgenic mice expressing endogenous levels of mutant CHMP2B developed late-onset brain volume loss associated with frank neuronal loss and FTD-like changes in social behaviour. These data are the first to show neurodegeneration in mice expressing mutant CHMP2B and indicate that our mouse model is able to recapitulate neurodegenerative changes observed in FTD. Neuroinflammation has been increasingly implicated in neurodegeneration, including FTD. Therefore, we investigated neuroinflammation in our CHMP2B mutant mice. We observed very early microglial proliferation that develops into a clear pro-inflammatory phenotype at late stages. Importantly, we also observed a similar inflammatory profile in CHMP2B patient frontal cortex. Aberrant microglial function has also been implicated in FTD caused by GRN, MAPT and C9orf72 mutations. The presence of early microglial changes in our CHMP2B mutant mice indicates neuroinflammation may be a contributing factor to the neurodegeneration observed in FTD.

Cortical grey matter sodium accumulation is associated with disability and secondary progressive disease course in relapse-onset multiple sclerosis.

OBJECTIVE: Sodium (23Na)-MRI is an emerging imaging technique to investigate in vivo changes in tissue viability, reflecting neuroaxonal integrity and metabolism. Using an optimised 23Na-MRI protocol with smaller voxel sizes and improved tissue contrast, we wanted to investigate whether brain total sodium concentration (TSC) is a biomarker for long-term disease outcomes in a cohort of patients with relapse-onset multiple sclerosis (MS), followed from disease onset. METHODS: We performed a cross-sectional study in 96 patients followed up ~ 15 years after a clinically isolated syndrome (CIS) and 34 healthy controls. Disease course was classified as CIS, relapsing-remitting MS or secondary progressive MS (SPMS). We acquired 1H-MRI and 23Na-MRI and calculated the TSC in cortical grey matter (CGM), deep grey matter, normal-appearing white matter (WM) and WM lesions. Multivariable linear regression was used to identify independent associations of tissue-specific TSC with physical disability and cognition, with adjustment for tissue volumes. RESULTS: TSC in all tissues was higher in patients with MS compared with healthy controls and patients who remained CIS, with differences driven by patients with SPMS. Higher CGM TSC was independently associated with Expanded Disability Status Scale (R2=0.26), timed 25-foot walk test (R2=0.23), 9-hole peg test (R2=0.23), Paced Auditory Serial Addition Test (R2=0.29), Symbol Digit Modalities Test (R2=0.31) and executive function (R2=0.36) test scores, independent of grey matter atrophy. CONCLUSIONS: Sodium accumulation in CGM reflects underlying neuroaxonal metabolic abnormalities relevant to disease course heterogeneity and disability in relapse-onset MS. TSC and should be considered as an outcome measure in future neuroprotection trials.

Structural network disruption markers explain disability in multiple sclerosis.

OBJECTIVE: To evaluate whether structural brain network metrics correlate better with clinical impairment and information processing speed in multiple sclerosis (MS) beyond atrophy measures and white matter lesions. METHODS: This cross-sectional study included 51 healthy controls and 122 patients comprising 58 relapsing-remitting, 28 primary progressive and 36 secondary progressive. Structural brain networks were reconstructed from diffusion-weighted MRIs and standard metrics reflecting network density, efficiency and clustering coefficient were derived and compared between subjects' groups. Stepwise linear regression analyses were used to investigate the contribution of network measures that explain clinical disability (Expanded Disability Status Scale (EDSS)) and information processing speed (Symbol Digit Modalities Test (SDMT)) compared with conventional MRI metrics alone and to determine the best statistical model that explains better EDSS and SDMT. RESULTS: Compared with controls, network efficiency and clustering coefficient were reduced in MS while these measures were also reduced in secondary progressive relative to relapsing-remitting patients. Structural network metrics increase the variance explained by the statistical models for clinical and information processing dysfunction. The best model for EDSS showed that reduced network density and global efficiency and increased age were associated with increased clinical disability. The best model for SDMT showed that lower deep grey matter volume, reduced efficiency and male gender were associated with worse information processing speed. CONCLUSIONS: Structural topological changes exist between subjects' groups. Network density and global efficiency explained disability above non-network measures, highlighting that network metrics can provide clinically relevant information about MS pathology.

Spinal cord atrophy as a primary outcome measure in phase II trials of progressive multiple sclerosis.

OBJECTIVES: To measure the development of spinal cord (SC) atrophy over 1 year in patients with progressive multiple sclerosis (PMS) and determine the sample sizes required to demonstrate a reduction in spinal cord cross-sectional area (SC-CSA) as an outcome measure in clinical trials. METHODS: In total, 44 PMS patients (26 primary progressive multiple sclerosis (PPMS), 18 secondary progressive multiple sclerosis (SPMS)) and 29 healthy controls (HCs) were studied at baseline and 12 months. SC-CSA was measured using the three-dimensional (3D) fast field echo sequences acquired at 3T and the active surface model. Multiple linear regressions were used to investigate changes in imaging measurements. RESULTS: PPMS patients had shorter disease duration, lower Expanded Disability Status Scale (EDSS) and larger SC-CSA than SPMS patients. All patients together showed a significantly greater decrease in percentage SC-CSA change than HCs, which was driven by the PPMS. All patients deteriorated over 1 year, but no association was found between percentage SC-CSA change and clinical changes. The sample size per arm required to detect a 50% treatment effect over 1 year, at 80% power, was 57 for PPMS and 546 for SPMS. CONCLUSION: SC-CSA may become an outcome measure in trials of PPMS patients, when they are at an early stage of the disease, have moderate disability and modest SC atrophy.

Cognitive reserve and TMEM106B genotype modulate brain damage in presymptomatic frontotemporal dementia: a GENFI study.

Frontotemporal dementia is a heterogeneous neurodegenerative disorder with around a third of cases having autosomal dominant inheritance. There is wide variability in phenotype even within affected families, raising questions about the determinants of the progression of disease and age at onset. It has been recently demonstrated that cognitive reserve, as measured by years of formal schooling, can counteract the ongoing pathological process. The TMEM106B genotype has also been found to be a modifier of the age at disease onset in frontotemporal dementia patients with TDP-43 pathology. This study therefore aimed to elucidate the modulating effect of environment (i.e. cognitive reserve as measured by educational attainment) and genetic background (i.e. TMEM106B polymorphism, rs1990622 T/C) on grey matter volume in a large cohort of presymptomatic subjects bearing frontotemporal dementia-related pathogenic mutations. Two hundred and thirty-one participants from the GENFI study were included: 108 presymptomatic MAPT, GRN, and C9orf72 mutation carriers and 123 non-carriers. For each subject, cortical and subcortical grey matter volumes were generated using a parcellation of the volumetric T1-weighted magnetic resonance imaging brain scan. TMEM106B genotyping was carried out, and years of education recorded. First, we obtained a composite measure of grey matter volume by graph-Laplacian principal component analysis, and then fitted a linear mixed-effect interaction model, considering the role of (i) genetic status; (ii) educational attainment; and (iii) TMEM106B genotype on grey matter volume. The presence of a mutation was associated with a lower grey matter volume (P = 0.002), even in presymptomatic subjects. Education directly affected grey matter volume in all the samples (P = 0.02) with lower education attainment being associated with lower volumes. TMEM106B genotype did not influence grey matter volume directly on its own but in mutation carriers it modulated the slope of the correlation between education and grey matter volume (P = 0.007). Together, these results indicate that brain atrophy in presymptomatic carriers of common frontotemporal dementia mutations is affected by both genetic and environmental factors such that TMEM106B enhances the benefit of cognitive reserve on brain structure. These findings should be considered in evaluating outcomes in future disease-modifying trials, and support the search for protective mechanisms in people at risk of dementia that might facilitate new therapeutic strategies.

Handheld Real-Time LED-Based Photoacoustic and Ultrasound Imaging System for Accurate Visualization of Clinical Metal Needles and Superficial Vasculature to Guide Minimally Invasive Procedures.

Ultrasound imaging is widely used to guide minimally invasive procedures, but the visualization of the invasive medical device and the procedure’s target is often challenging. Photoacoustic imaging has shown great promise for guiding minimally invasive procedures, but clinical translation of this technology has often been limited by bulky and expensive excitation sources. In this work, we demonstrate the feasibility of guiding minimally invasive procedures using a dual-mode photoacoustic and ultrasound imaging system with excitation from compact arrays of light-emitting diodes (LEDs) at 850 nm. Three validation experiments were performed. First, clinical metal needles inserted into biological tissue were imaged. Second, the imaging depth of the system was characterized using a blood-vessel-mimicking phantom. Third, the superficial vasculature in human volunteers was imaged. It was found that photoacoustic imaging enabled needle visualization with signal-to-noise ratios that were 1.2 to 2.2 times higher than those obtained with ultrasound imaging, over insertion angles of 26 to 51 degrees. With the blood vessel mimicking phantom, the maximum imaging depth was 38 mm. The superficial vasculature of a human middle finger and a human wrist were clearly visualized in real-time. We conclude that the LED-based system is promising for guiding minimally invasive procedures with peripheral tissue targets.

Longitudinal diffusion tensor imaging in frontotemporal dementia.

OBJECTIVE: Novel biomarkers for monitoring progression in neurodegenerative conditions are needed. Measurement of microstructural changes in white matter (WM) using diffusion tensor imaging (DTI) may be a useful outcome measure. Here we report trajectories of WM change using serial DTI in a cohort with behavioral variant frontotemporal dementia (bvFTD). METHODS: Twenty-three patients with bvFTD (12 having genetic mutations), and 18 age-matched control participants were assessed using DTI and neuropsychological batteries at baseline and ~1.3 years later. Baseline and follow-up DTI scans were registered using a groupwise approach. Annualized rates of change for DTI metrics, neuropsychological measures, and whole brain volume were calculated. DTI metric performances were compared, and sample sizes for potential clinical trials were calculated. RESULTS: In the bvFTD group as a whole, rates of change in fractional anisotropy (FA) and mean diffusivity (MD) within the right paracallosal cingulum were greatest (FA: -6.8%/yr, p < 0.001; MD: 2.9%/yr, p = 0.01). MAPT carriers had the greatest change within left uncinate fasciculus (FA: -7.9%/yr, p < 0.001; MD: 10.9%/yr, p < 0.001); sporadic bvFTD and C9ORF72 carriers had the greatest change within right paracallosal cingulum (sporadic bvFTD, FA: -6.7%/yr, p < 0.001; MD: 3.8%/yr, p = 0.001; C9ORF72, FA: -6.8%/yr, p = 0.004). Sample size estimates using FA change were substantially lower than neuropsychological or whole brain measures of change. INTERPRETATION: Serial DTI scans may be useful for measuring disease progression in bvFTD, with particular trajectories of WM damage emerging. Sample size calculations suggest that longitudinal DTI may be a useful biomarker in future clinical trials.

White matter NAA/Cho and Cho/Cr ratios at MR spectroscopy are predictive of motor outcome in preterm infants.

PURPOSE: To determine (a) whether diffuse white matter injury of prematurity is associated with an increased choline (Cho)-to-creatine (Cr) ratio and a reduced N-acetylaspartate (NAA)-to-Cho ratio and whether these measures can be used as biomarkers of outcome and (b) if changes in peak area metabolite ratios at magnetic resonance (MR) spectroscopy are associated with changes in T2 and fractional anisotropy (FA) at MR imaging. MATERIALS AND METHODS: The local ethics committee approved this study, and informed parental consent was obtained for each infant. At term-equivalent age, 43 infants born at less than 32 weeks gestation underwent conventional and quantitative diffusion-tensor and T2-weighted MR imaging. Single-voxel point-resolved proton (hydrogen 1) MR spectroscopy was performed from a 2-cm(3) voxel centered in the posterior periventricular white matter. Outcome was evaluated by using Bayley scales at a corrected age of 1 year. Associations were investigated with Pearson product moment or Spearman rank order correlation. Differences in ratios in infants with and infants without impairment were tested by using t tests. RESULTS: NAA/Cho and Cho/Cr ratios correlated with the scaled gross motor score and the composite motor score, independent of gestational age (P < .05). FA at diffusion-tensor MR imaging and T2 at MR imaging correlated with the NAA/Cho ratio (P < .05 for both) but not with the Cho/Cr ratio. Infants with motor scores of less than 85 (impaired) had an increased Cho/Cr ratio (P < .03) and a reduced NAA/Cho ratio (P < .01) compared to those without impairment. A combination of increased Cho/Cr ratio and decreased NAA/Cho ratio predicted impaired motor outcome at a corrected age of 1 year with a sensitivity of 0.80 (95% confidence interval [CI]: 0.57, 0.94) and a specificity of 0.80 (95% CI: 0.66, 0.88). CONCLUSION: The combination of Cho/Cr and NAA/Cho ratios measured in the posterior periventricular white matter at term-equivalent age is predictive of motor outcome at 1 year in infants born at less than 32 weeks gestation.

Investigation of outer cortical magnetisation transfer ratio abnormalities in multiple sclerosis clinical subgroups.

BACKGROUND: Pathological abnormalities including demyelination and neuronal loss are reported in the outer cortex in multiple sclerosis (MS). OBJECTIVE: We investigated for in vivo evidence of outer cortical abnormalities by measuring the magnetisation transfer ratio (MTR) in MS patients of different subgroups. METHODS: Forty-four relapsing-remitting (RR) (mean age 41.9 years, median Expanded Disability Status Scale (EDSS) 2.0), 25 secondary progressive (SP) (54.1 years, EDSS 6.5) and 19 primary progressive (PP) (53.1 years, EDSS 6.0) MS patients and 35 healthy control subjects (mean age 39.2 years) were studied. Three-dimensional (3D) 1×1×1mm(3) T1-weighted images and MTR data were acquired. The cortex was segmented, then subdivided into outer and inner bands, and MTR values were calculated for each band. RESULTS: In a pairwise analysis, mean outer cortical MTR was lower than mean inner cortical MTR in all MS groups and controls (p<0.001). Compared with controls, outer cortical MTR was decreased in SPMS (p<0.001) and RRMS (p<0.01), but not PPMS. Outer cortical MTR was lower in SPMS than PPMS (p<0.01) and RRMS (p<0.01). CONCLUSIONS: Lower outer than inner cortical MTR in healthy controls may reflect differences in myelin content. The lowest outer cortical MTR was seen in SPMS and is consistent with more extensive outer cortical (including subpial) pathology, such as demyelination and neuronal loss, as observed in post-mortem studies of SPMS patients.

Myelination of preterm brain networks at adolescence.

Prematurity and preterm stressors severely affect the development of infants born before 37 weeks of gestation, with increasing effects seen at earlier gestations. Although preterm mortality rates have declined due to the advances in neonatal care, disability rates, especially in middle-income settings, continue to grow. With the advances in MR imaging technology, there has been a focus on safely imaging the preterm brain to better understand its development and discover the brain regions and networks affected by prematurity. Such studies aim to support interventions and improve the neurodevelopment of preterm infants and deliver accurate prognoses. Few studies, however, have focused on the fully developed brain of preterm born infants, especially in extremely preterm subjects. To assess the long-term effect of prematurity on the adult brain, myelin related biomarkers such as myelin water fraction and g-ratio are measured for a cohort of 19-year-old extremely preterm born subjects. Using multi-modal imaging techniques that combine T2 relaxometry and neurite density information, the results show that specific brain regions associated with white matter injuries due to preterm birth, such as the posterior limb of the internal capsule and corpus callosum, are still less myelinated in adulthood. Furthermore, a weak positive relationship between myelin water fraction values and Full-Scale Intelligence Quotient (FSIQ) scores was found in multiple brain regions previously defined as less myelinated in the Extremely Preterm (EPT) cohort. These findings might suggest altered connectivity in the adult preterm brain and explain differences in cognitive outcomes.

Diffusion MRI-based cortical complexity alterations associated with executive function in multiple sclerosis.

PURPOSE: To report a novel magnetic resonance imaging measure (diffusion orientational complexity [DOC]) in a study of people with multiple sclerosis (MS) and healthy controls and to determine patterns of abnormality, correlations with conventional diffusion measures, and associations with cognitive function. MATERIALS AND METHODS: We performed high angular resolution diffusion imaging (HARDI) and measured DOC, mean diffusivity (MD), and fractional anisotropy (FA) in 51 MS patients and 28 healthy controls. All subjects had a 2-mm isotropic HARDI scan on a 3 T scanner using a 32-channel head receiver coil. DOC, MD, and FA were measured in three regions of interest (ROIs) in frontal cortex linked to executive function, two ROIs in occipital cortex thought unlikely to affect cognition, and in the whole cortex. RESULTS: Frontal cortex DOC was significantly decreased in MS patients. DOC correlated mostly with FA but not MD in controls and with MD but not FA in people with MS. In regression models that included all three diffusion-based measures, frontal cortex DOC and frontal cortex FA independently predicted executive function scores. CONCLUSION: DOC is a new useful measure of functionally relevant cortical pathology in MS, providing information that complements conventional diffusion measures.

Factors affecting the labelling accuracy of brain MRI studies relevant for deep learning abnormality detection.

Unlocking the vast potential of deep learning-based computer vision classification systems necessitates large data sets for model training. Natural Language Processing (NLP)-involving automation of dataset labelling-represents a potential avenue to achieve this. However, many aspects of NLP for dataset labelling remain unvalidated. Expert radiologists manually labelled over 5,000 MRI head reports in order to develop a deep learning-based neuroradiology NLP report classifier. Our results demonstrate that binary labels (normal vs. abnormal) showed high rates of accuracy, even when only two MRI sequences (T2-weighted and those based on diffusion weighted imaging) were employed as opposed to all sequences in an examination. Meanwhile, the accuracy of more specific labelling for multiple disease categories was variable and dependent on the category. Finally, resultant model performance was shown to be dependent on the expertise of the original labeller, with worse performance seen with non-expert vs. expert labellers.

A Position Statement on the Utility of Interval Imaging in Standard of Care Brain Tumour Management: Defining the Evidence Gap and Opportunities for Future Research.

OBJECTIV E: To summarise current evidence for the utility of interval imaging in monitoring disease in adult brain tumours, and to develop a position for future evidence gathering while incorporating the application of data science and health economics. METHODS: Experts in 'interval imaging' (imaging at pre-planned time-points to assess tumour status); data science; health economics, trial management of adult brain tumours, and patient representatives convened in London, UK. The current evidence on the use of interval imaging for monitoring brain tumours was reviewed. To improve the evidence that interval imaging has a role in disease management, we discussed specific themes of data science, health economics, statistical considerations, patient and carer perspectives, and multi-centre study design. Suggestions for future studies aimed at filling knowledge gaps were discussed. RESULTS: Meningioma and glioma were identified as priorities for interval imaging utility analysis. The "monitoring biomarkers" most commonly used in adult brain tumour patients were standard structural MRI features. Interval imaging was commonly scheduled to provide reported imaging prior to planned, regular clinic visits. There is limited evidence relating interval imaging in the absence of clinical deterioration to management change that alters morbidity, mortality, quality of life, or resource use. Progression-free survival is confounded as an outcome measure when using structural MRI in glioma. Uncertainty from imaging causes distress for some patients and their caregivers, while for others it provides an important indicator of disease activity. Any study design that changes imaging regimens should consider the potential for influencing current or planned therapeutic trials, ensure that opportunity costs are measured, and capture indirect benefits and added value. CONCLUSION: Evidence for the value, and therefore utility, of regular interval imaging is currently lacking. Ongoing collaborative efforts will improve trial design and generate the evidence to optimise monitoring imaging biomarkers in standard of care brain tumour management.

3D printing from microfocus computed tomography (micro-CT) in human specimens: education and future implications.

Microfocus CT (micro-CT) is an imaging method that provides three-dimensional digital data sets with comparable resolution to light microscopy. Although it has traditionally been used for non-destructive testing in engineering, aerospace industries and in preclinical animal studies, new applications are rapidly becoming available in the clinical setting including post-mortem fetal imaging and pathological specimen analysis. Printing three-dimensional models from imaging data sets for educational purposes is well established in the medical literature, but typically using low resolution (0.7 mm voxel size) data acquired from CT or MR examinations. With higher resolution imaging (voxel sizes below 1 micron, <0.001 mm) at micro-CT, smaller structures can be better characterised, and data sets post-processed to create accurate anatomical models for review and handling. In this review, we provide examples of how three-dimensional printing of micro-CT imaged specimens can provide insight into craniofacial surgical applications, developmental cardiac anatomy, placental imaging, archaeological remains and high-resolution bone imaging. We conclude with other potential future usages of this emerging technique.

Longitudinal in vivo MRI in a Huntington's disease mouse model: Global atrophy in the absence of white matter microstructural damage.

Huntington's disease (HD) is a genetically-determined neurodegenerative disease. Characterising neuropathology in mouse models of HD is commonly restricted to cross-sectional ex vivo analyses, beset by tissue fixation issues. In vivo longitudinal magnetic resonance imaging (MRI) allows for disease progression to be probed non-invasively. In the HdhQ150 mouse model of HD, in vivo MRI was employed at two time points, before and after the onset of motor signs, to assess brain macrostructure and white matter microstructure. Ex vivo MRI, immunohistochemistry, transmission electron microscopy and behavioural testing were also conducted. Global brain atrophy was found in HdhQ150 mice at both time points, with no neuropathological progression across time and a selective sparing of the cerebellum. In contrast, no white matter abnormalities were detected from the MRI images or electron microscopy images alike. The relationship between motor function and MR-based structural measurements was different for the HdhQ150 and wild-type mice, although there was no relationship between motor deficits and histopathology. Widespread neuropathology prior to symptom onset is consistent with patient studies, whereas the absence of white matter abnormalities conflicts with patient data. The myriad reasons for this inconsistency require further attention to improve the translatability from mouse models of disease.