In praise of tedious anatomy

Functional neuroimaging is fundamentally a tool for mapping function to structure, and its success consequently requires neuroanatomical precision and accuracy. Here we review the various means by which functional activation can be localized to neuroanatomy and suggest that the gold standard should be localization to the individual’s or group’s own anatomy through the use of neuroanatomical knowledge and atlases of neuroanatomy. While automated means of localization may be useful, they cannot provide the necessary accuracy, given variability between individuals. We also suggest that the field of functional neuroimaging needs to converge on a common set of methods for reporting functional localization including a common “standard” space and criteria for what constitutes sufficient evidence to report activation in terms of Brodmann’s areas

An evaluation of morphological and functional multi-parametric MRI sequences in classifying non-muscle and muscle invasive bladder cancer

Objectives: Our goal is to determine the ability of multi-parametric magnetic resonance imaging (mpMRI) to differentiate muscle invasive bladder cancer (MIBC) from non-muscle invasive bladder cancer (NMIBC). Methods: Patients underwent mpMRI before tumour resection. Four MRI sets, i.e. T2-weighted (T2W) + perfusion-weighted imaging (PWI), T2W plus diffusion-weighted imaging (DWI), T2W + DWI + PWI, and T2W + DWI + PWI + dif-fusion tensor imaging (DTI) were interpreted qualitatively by two radiologists, blinded to histology results. PWI, DWI and DTI were also analysed quantitatively. Accuracy was determined using histopathology as the reference standard. Results: A total of 82 tumours were analysed. Ninety-six percent of T1-labeled tumours by the T2W + DWI + PWI image set were confirmed to be NMIBC at histopathology. Overall accuracy of the complete mpMRI protocol was 94% in differentiating NMIBC from MIBC. PWI, DWI and DTI quantitative parameters were shown to be significantly different in cancerous versus non-cancerous areas within the bladder wall in T2-labelled lesions. Conclusions: MpMRI with DWI and DTI appears a reliable staging tool for bladder cancer. If our data are validated, then mpMRI could precede cystoscopic resection to allow a faster recognition of MIBC and accelerated treatment pathways. Key Points: • A critical step in BCa staging is to differentiate NMIBC from MIBC. • Morphological and functional sequences are reliable techniques in differentiating NMIBC from MIBC. • Diffusion tensor imaging could be an additional tool in BCa staging

MRI evidence for altered venous drainage and intracranial compliance in mild traumatic brain injury.

To compare venous drainage patterns and associated intracranial hydrodynamics between subjects who experienced mild traumatic brain injury (mTBI) and age- and gender-matched controls. Thirty adult subjects (15 with mTBI and 15 age- and gender-matched controls) were investigated using a 3T MR scanner. Time since trauma was 0.5 to 29 years (mean 11.4 years). A 2D-time-of-flight MR-venography of the upper neck was performed to visualize the cervical venous vasculature. Cerebral venous drainage through primary and secondary channels, and intracranial compliance index and pressure were derived using cine-phase contrast imaging of the cerebral arterial inflow, venous outflow, and the craniospinal CSF flow. The intracranial compliance index is the defined as the ratio of maximal intracranial volume and pressure changes during the cardiac cycle. MR estimated ICP was then obtained through the inverse relationship between compliance and ICP. Compared to the controls, subjects with mTBI demonstrated a significantly smaller percentage of venous outflow through internal jugular veins (60.9±21% vs. controls: 76.8±10%; p = 0.01) compensated by an increased drainage through secondary veins (12.3±10.9% vs. 5.5±3.3%; p<0.03). Mean intracranial compliance index was significantly lower in the mTBI cohort (5.8±1.4 vs. controls 8.4±1.9; p<0.0007). Consequently, MR estimate of intracranial pressure was significantly higher in the mTBI cohort (12.5±2.9 mmHg vs. 8.8±2.0 mmHg; p<0.0007). mTBI is associated with increased venous drainage through secondary pathways. This reflects higher outflow impedance, which may explain the finding of reduced intracranial compliance. These results suggest that hemodynamic and hydrodynamic changes following mTBI persist even in the absence of clinical symptoms and abnormal findings in conventional MR imaging

Stroke mimic diagnoses presenting to a hyperacute stroke unit.

Stroke services have been centralised in several countries in recent years. Diagnosing acute stroke is challenging and a high proportion of patients admitted to stroke units are diagnosed as a non-stroke condition (stroke mimics). This study aims to describe the stroke mimic patient group, including their impact on stroke services. We analysed routine clinical data from 2,305 consecutive admissions to a stroke unit at St George's Hospital, London. Mimic groupings were derived from 335 individual codes into 17 groupings. From 2,305 admissions, 555 stroke mimic diagnoses were identified (24.2%) and 72% of stroke mimics had at least one stroke risk factor. Common mimic diagnoses were headache, seizure and syncope. Medically unexplained symptoms and decompensation of underlying conditions were also common. Median length of stay was 1 day; a diagnosis of dementia (p=0.028) or needing MRI (p=0.006) was associated with a longer stay. Despite emergency department assessment by specialist clinicians and computed tomography brain, one in four suspected stroke patients admitted to hospital had a non-stroke diagnosis. Stroke mimics represent a heterogeneous patient group with significant impacts on stroke services. Co-location of stroke and acute neurology services may offer advantages where service reorganisation is being considered

Automated, high accuracy classification of Parkinsonian disorders: a pattern recognition approach

Progressive supranuclear palsy (PSP), multiple system atrophy (MSA) and idiopathic Parkinson’s disease (IPD) can be clinically indistinguishable, especially in the early stages, despite distinct patterns of molecular pathology. Structural neuroimaging holds promise for providing objective biomarkers for discriminating these diseases at the single subject level but all studies to date have reported incomplete separation of disease groups. In this study, we employed multi-class pattern recognition to assess the value of anatomical patterns derived from a widely available structural neuroimaging sequence for automated classification of these disorders. To achieve this, 17 patients with PSP, 14 with IPD and 19 with MSA were scanned using structural MRI along with 19 healthy controls (HCs). An advanced probabilistic pattern recognition approach was employed to evaluate the diagnostic value of several pre-defined anatomical patterns for discriminating the disorders, including: (i) a subcortical motor network; (ii) each of its component regions and (iii) the whole brain. All disease groups could be discriminated simultaneously with high accuracy using the subcortical motor network. The region providing the most accurate predictions overall was the midbrain/brainstem, which discriminated all disease groups from one another and from HCs. The subcortical network also produced more accurate predictions than the whole brain and all of its constituent regions. PSP was accurately predicted from the midbrain/brainstem, cerebellum and all basal ganglia compartments; MSA from the midbrain/brainstem and cerebellum and IPD from the midbrain/brainstem only. This study demonstrates that automated analysis of structural MRI can accurately predict diagnosis in individual patients with Parkinsonian disorders, and identifies distinct patterns of regional atrophy particularly useful for this process

The white matter connectome as an individualized biomarker of language impairment in temporal lobe epilepsy.

ObjectiveThe distributed white matter network underlying language leads to difficulties in extracting clinically meaningful summaries of neural alterations leading to language impairment. Here we determine the predictive ability of the structural connectome (SC), compared with global measures of white matter tract microstructure and clinical data, to discriminate language impaired patients with temporal lobe epilepsy (TLE) from TLE patients without language impairment.MethodsT1- and diffusion-MRI, clinical variables (CVs), and neuropsychological measures of naming and verbal fluency were available for 82 TLE patients. Prediction of language impairment was performed using a robust tree-based classifier (XGBoost) for three models: (1) a CV-model which included demographic and epilepsy-related clinical features, (2) an atlas-based tract-model, including four frontotemporal white matter association tracts implicated in language (i.e., the bilateral arcuate fasciculus, inferior frontal occipital fasciculus, inferior longitudinal fasciculus, and uncinate fasciculus), and (3) a SC-model based on diffusion MRI. For the association tracts, mean fractional anisotropy was calculated as a measure of white matter microstructure for each tract using a diffusion tensor atlas (i.e., AtlasTrack). The SC-model used measurement of cortical-cortical connections arising from a temporal lobe subnetwork derived using probabilistic tractography. Dimensionality reduction of the SC was performed with principal components analysis (PCA). Each model was trained on 49 patients from one epilepsy center and tested on 33 patients from a different center (i.e., an independent dataset). Randomization was performed to test the stability of the results.ResultsThe SC-model yielded a greater area under the curve (AUC; .73) and accuracy (79%) compared to both the tract-model (AUC: .54, p &lt; .001; accuracy: 70%, p &lt; .001) and the CV-model (AUC: .59, p &lt; .001; accuracy: 64%, p &lt; .001). Within the SC-model, lateral temporal connections had the highest importance to model performance, including connections similar to language association tracts such as links between the superior temporal gyrus to pars opercularis. However, in addition to these connections many additional connections that were widely distributed, bilateral and interhemispheric in nature were identified as contributing to SC-model performance.ConclusionThe SC revealed a white matter network contributing to language impairment that was widely distributed, bilateral, and lateral temporal in nature. The distributed network underlying language may be why the SC-model has an advantage in identifying sub-components of the complex fiber networks most relevant for aspects of language performance

Pancreatic tumors imaging: an update

Currently, ultrasound (US), computed tomography (CT) and Magnetic Resonance imaging (MRI) represent the mainstay in the evaluation of pancreatic solid and cystic tumors affecting pancreas in 80-85% and 10-15% of the cases respectively. Integration of US, CT or MR imaging is essential for an accurate assessment of pancreatic parenchyma, ducts and adjacent soft tissues in order to detect and to stage the tumor, to differentiate solid from cystic lesions and to establish an appropriate treatment. The purpose of this review is to provide an overview of pancreatic tumors and the role of imaging in their diagnosis and management. In order to a prompt and accurate diagnosis and appropriate management of pancreatic lesions, it is crucial for radiologists to know the key findings of the most frequent tumors of the pancreas and the current role of imaging modalities. A multimodality approach is often helpful. If multidetector-row CT (MDCT) is the preferred initial imaging modality in patients with clinical suspicion for pancreatic cancer, multiparametric MRI provides essential information for the detection and characterization of a wide variety of pancreatic lesions and can be used as a problem-solving tool at diagnosis and during follow-up