The radiological interpretation of possible microbleeds after moderate or severe traumatic brain injury: a longitudinal study.

INTRODUCTION: In order to augment the certainty of the radiological interpretation of "possible microbleeds" after traumatic brain injury (TBI), we assessed their longitudinal evolution on 3-T SWI in patients with moderate/severe TBI. METHODS: Standardized 3-T SWI and T1-weighted imaging were obtained 3 and 26 weeks after TBI in 31 patients. Their microbleeds were computer-aided detected and classified by a neuroradiologist as no, possible, or definite at baseline and follow-up, separately (single-scan evaluation). Thereafter, the classifications were re-evaluated after comparison between the time-points (post-comparison evaluation). We selected the possible microbleeds at baseline at single-scan evaluation and recorded their post-comparison classification at follow-up. RESULTS: Of the 1038 microbleeds at baseline, 173 were possible microbleeds. Of these, 53.8% corresponded to no microbleed at follow-up. At follow-up, 30.6% were possible and 15.6% were definite. Of the 120 differences between baseline and follow-up, 10% showed evidence of a pathophysiological change over time. Proximity to extra-axial injury and proximity to definite microbleeds were independently predictive of becoming a definite microbleed at follow-up. The reclassification level differed between anatomical locations. CONCLUSIONS: Our findings support disregarding possible microbleeds in the absence of clinical consequences. In selected cases, however, a follow-up SWI-scan could be considered to exclude evolution into a definite microbleed.

Incorporating radiomics into clinical trials: expert consensus endorsed by the European Society of Radiology on considerations for data-driven compared to biologically driven quantitative biomarkers.

Existing quantitative imaging biomarkers (QIBs) are associated with known biological tissue characteristics and follow a well-understood path of technical, biological and clinical validation before incorporation into clinical trials. In radiomics, novel data-driven processes extract numerous visually imperceptible statistical features from the imaging data with no a priori assumptions on their correlation with biological processes. The selection of relevant features (radiomic signature) and incorporation into clinical trials therefore requires additional considerations to ensure meaningful imaging endpoints. Also, the number of radiomic features tested means that power calculations would result in sample sizes impossible to achieve within clinical trials. This article examines how the process of standardising and validating data-driven imaging biomarkers differs from those based on biological associations. Radiomic signatures are best developed initially on datasets that represent diversity of acquisition protocols as well as diversity of disease and of normal findings, rather than within clinical trials with standardised and optimised protocols as this would risk the selection of radiomic features being linked to the imaging process rather than the pathology. Normalisation through discretisation and feature harmonisation are essential pre-processing steps. Biological correlation may be performed after the technical and clinical validity of a radiomic signature is established, but is not mandatory. Feature selection may be part of discovery within a radiomics-specific trial or represent exploratory endpoints within an established trial; a previously validated radiomic signature may even be used as a primary/secondary endpoint, particularly if associations are demonstrated with specific biological processes and pathways being targeted within clinical trials. KEY POINTS: • Data-driven processes like radiomics risk false discoveries due to high-dimensionality of the dataset compared to sample size, making adequate diversity of the data, cross-validation and external validation essential to mitigate the risks of spurious associations and overfitting. • Use of radiomic signatures within clinical trials requires multistep standardisation of image acquisition, image analysis and data mining processes. • Biological correlation may be established after clinical validation but is not mandatory.

Brain CT perfusion improves intracranial vessel occlusion detection on CT angiography.

BACKGROUND AND PURPOSE: To evaluate whether brain CT perfusion (CTP) aids in the detection of intracranial vessel occlusion on CT angiography (CTA) in acute ischemic stroke. MATERIALS AND METHODS: Medical-ethical committee approval of our hospital was obtained and informed consent was waived. Patients suspected of acute ischemic stroke who underwent non-contrast CT(NCCT), CTA and whole-brain CTP in our center in the year 2015 were included. Three observers with different levels of experience evaluated the imaging data of 110 patients for the presence or absence of intracranial arterial vessel occlusion with two strategies. In the first strategy, only NCCT and CTA were available. In the second strategy, CTP maps were provided in addition to NCCT and CTA. Receiver-operating-characteristic (ROC) analysis was used for the evaluation of diagnostic accuracy. RESULTS: Overall, a brain perfusion deficit was scored present in 87-89% of the patients with an intracranial vessel occlusion, more frequently observed in the anterior than in the posterior circulation. Performance of intracranial vessel occlusion detection on CTA was significantly improved with the availability of CTP maps as compared to the first strategy (P=0.023), due to improved detection of distal and posterior circulation vessel occlusions (P-values of 0.032 and 0.003 respectively). No added value of CTP was found for intracranial proximal vessel occlusion detection, with already high accuracy based on NCCT and CTA alone. CONCLUSION: The performance of intracranial vessel occlusion detection on CTA was improved with the availability of brain CT perfusion maps due to the improved detection of distal and posterior circulation vessel occlusions.

Observer variability of reference tissue selection for relativecerebral blood volume measurements in glioma patients.

OBJECTIVES: To assess observer variability of different reference tissues used for relative CBV (rCBV) measurements in DSC-MRI of glioma patients. METHODS: In this retrospective study, three observers measured rCBV in DSC-MR images of 44 glioma patients on two occasions. rCBV is calculated by the CBV in the tumour hotspot/the CBV of a reference tissue at the contralateral side for normalization. One observer annotated the tumour hotspot that was kept constant for all measurements. All observers annotated eight reference tissues of normal white and grey matter. Observer variability was evaluated using the intraclass correlation coefficient (ICC), coefficient of variation (CV) and Bland-Altman analyses. RESULTS: For intra-observer, the ICC ranged from 0.50-0.97 (fair-excellent) for all reference tissues. The CV ranged from 5.1-22.1 % for all reference tissues and observers. For inter-observer, the ICC for all pairwise observer combinations ranged from 0.44-0.92 (poor-excellent). The CV ranged from 8.1-31.1 %. Centrum semiovale was the only reference tissue that showed excellent intra- and inter-observer agreement (ICC>0.85) and lowest CVs (<12.5 %). Bland-Altman analyses showed that mean differences for centrum semiovale were close to zero. CONCLUSION: Selecting contralateral centrum semiovale as reference tissue for rCBV provides the lowest observer variability. KEY POINTS: • Reference tissue selection for rCBV measurements adds variability to rCBV measurements. • rCBV measurements vary depending on the choice of reference tissue. • Observer variability of reference tissue selection varies between poor and excellent. • Centrum semiovale as reference tissue for rCBV provides the lowest observer variability.

Interleaving cerebral CT perfusion with neck CT angiography part I. Proof of concept and accuracy of cerebral perfusion values.

OBJECTIVES: We present a novel One-Step-Stroke protocol for wide-detector CT scanners that interleaves cerebral CTP with volumetric neck CTA (vCTA). We evaluate whether the resulting time gap in CTP affects the accuracy of CTP values. METHODS: Cerebral CTP maps were retrospectively obtained from 20 patients with suspicion of acute ischemic stroke and served as the reference standard. To simulate a 4 s gap for interleaving CTP with vCTA, we eliminated one acquisition at various time points of CTP starting from the bolus-arrival-time(BAT). Optimal timing of the vCTA was evaluated. At the time point with least errors, we evaluated elimination of a second time point (6 s gap). RESULTS: Mean absolute percentage errors of all perfusion values remained below 10 % in all patients when eliminating any one time point in the CTP sequence starting from the BAT. Acquiring the vCTA 2 s after reaching a threshold of 70HU resulted in the lowest errors (mean <3.0 %). Eliminating a second time point still resulted in mean errors <3.5 %. CBF/CBV showed no significant differences in perfusion values except MTT. However, the percentage errors were always below 10 % compared to the original protocol. CONCLUSION: Interleaving cerebral CTP with neck CTA is feasible with minor effects on the perfusion values. KEY POINTS: • Removing a single CTP acquisition has minor effects on calculated perfusion values • Calculated perfusion values errors depend on timing of skipping a CTP acquisition • Qualitative evaluation of CTP was not influenced by removing two time points • Neck CTA is optimally timed in the upslope of arterial enhancement.

Interleaving cerebral CT perfusion with neck CT angiography. Part II: clinical implementation and image quality.

OBJECTIVES: Feasibility evaluation of the One-Step Stroke Protocol, which is an interleaved cerebral computed tomography perfusion (CTP) and neck volumetric computed tomography angiography (vCTA) scanning technique using wide-detector computed tomography, and to assess the image quality of vCTA. METHODS: Twenty patients with suspicion of acute ischaemic stroke were prospectively scanned and evaluated with a head and neck CTA and with the One-Step Stroke Protocol. Arterial enhancement and contrast-to-noise ratio (CNR) in the carotid arteries was assessed. Three observers scored artefacts and image quality of the cervical arteries. The total z-coverage was evaluated. RESULTS: Mean enhancement in the carotid bifurcation was rated higher in the vCTA (595 ± 164 HU) than CTA (441 ± 117 HU). CNR was rated higher in vCTA. Image quality scores showed no significant difference in the region of the carotid bifurcation between vCTA and CTA. Lower neck image quality scores were slightly lower for vCTA due to artefacts, although not rated as diagnostically relevant. In ten patients, the origin of the left common carotid artery was missed by 1.6 ± 0.8 cm. Mean patient height was 1.8 ± 0.09 m. Carotid bifurcation and origin of vertebral arteries were covered in all patients. CONCLUSIONS: The One-Step Stroke Protocol is feasible with good diagnostic image quality of vCTA, although full z-coverage is limited in tall patients. KEY POINTS: • Interleaving cerebral CTP with neck CTA (One-Step Stroke Protocol) is feasible • Diagnostic quality of One-Step Stroke Protocol neck CTA is similar to conventional CTA • One-Step Stroke Protocol neck CTA suffers from streak artefacts in the lower neck • A limitation of One-Step Stroke Protocol CTA is lack of coverage in tall patients • Precise planning of One-Step Stroke Protocol neck CTA is necessary in tall patients.

Quantitative Dose Dependency Analysis of Whole-Brain CT Perfusion Imaging.

PURPOSE: To quantitatively assess whether decreasing total radiation dose of the image acquisition protocol has an effect on cerebral CT perfusion values in patients with acute stroke. MATERIALS AND METHODS: This retrospective study was approved by the institutional ethics committee, and informed consent was waived. Twenty consecutive patients with ischemic stroke who underwent CT perfusion imaging with a 320-detector row CT scanner were included. A standard acquisition protocol was used, which was started 5 seconds after injection of a contrast agent, with a scan at 200 mAs, followed after 4 seconds by 13 scans, one every 2 seconds, at 100 mAs, and then five scans, one every 5 seconds, at 75 mAs. The total examination had an average effective dose of 5.0 mSv. For each patient, a patient-specific digital perfusion phantom was constructed to simulate the same protocol at a lower total dose (0.5-5.0 mSv, with stepped doses of 0.5 mSv). The lowest setting for which the maximum mean difference remained within 5% of the reference standard (at 5.0 mSv) was marked as the optimal setting. At the optimal setting, Pearson correlation coefficients were calculated to assess correlations with the reference values, and paired t tests were performed to compare the means. RESULTS: At 2.5 mSv, the maximum mean differences in values from those of the reference standard were 4.5%, 5.0%, and 1.9%, for cerebral blood flow, cerebral blood volume, and mean transit time, respectively. Pearson correlation coefficients of perfusion values for white matter and gray matter were 0.864-0.917, and all differences were significant (P < .0001). Paired t tests showed no significant differences between the reference standard and optimal settings (P = .089-.923). CONCLUSION: The total dose of a clinical cerebral CT perfusion protocol can be lowered to 2.5 mSv, with only minor quantitative effects on perfusion values. Dose reduction beyond this point resulted in overestimation of perfusion values.

Spirometer-controlled cine magnetic resonance imaging used to diagnose tracheobronchomalacia in paediatric patients.

Tracheobronchomalacia (TBM) is defined as an excessive collapse of the intrathoracic trachea. Bronchoscopy is the gold standard for diagnosing TBM; however it has major disadvantages, such as general anaesthesia. Cine computed tomography (CT) is a noninvasive alternative used to diagnose TBM, but its use in children is restricted by ionising radiation. Our aim was to evaluate the feasibility of spirometer-controlled cine magnetic resonance imaging (MRI) as an alternative to cine-CT in a retrospective study. 12 children with a mean age (range) of 12 years (7-17 years), suspected of having TBM, underwent cine-MRI. Static scans were acquired at end-inspiration and expiration covering the thorax using a three-dimensional spoiled gradient echo sequence. Three-dimensional dynamic scans were performed covering only the central airways. TBM was defined as a decrease of the trachea or bronchi diameter >50% at end-expiration in the static and dynamic scans. The success rate of the cine-MRI protocol was 92%. Cine-MRI was compared with bronchoscopy or chest CT in seven subjects. TBM was diagnosed by cine-MRI in seven (58%) out of 12 children and was confirmed by bronchoscopy or CT. In four patients, cine-MRI demonstrated tracheal narrowing that was not present in the static scans. Spirometer controlled cine-MRI is a promising technique to assess TBM in children and has the potential to replace bronchoscopy.

Stacked Bidirectional Convolutional LSTMs for Deriving 3D Non-Contrast CT From Spatiotemporal 4D CT.

The imaging workup in acute stroke can be simplified by deriving non-contrast CT (NCCT) from CT perfusion (CTP) images. This results in reduced workup time and radiation dose. To achieve this, we present a stacked bidirectional convolutional LSTM (C-LSTM) network to predict 3D volumes from 4D spatiotemporal data. Several parameterizations of the C-LSTM network were trained on a set of 17 CTP-NCCT pairs to learn to derive a NCCT from CTP and were subsequently quantitatively evaluated on a separate cohort of 16 cases. The results show that the C-LSTM network clearly outperforms the baseline and competitive convolutional neural network methods. We show good scalability and performance of the method by continued training and testing on an independent dataset which includes pathology of 80 and 83 CTP-NCCT pairs, respectively. C-LSTM is, therefore, a promising general deep learning approach to learn from high-dimensional spatiotemporal medical images.

Image-level detection of arterial occlusions in 4D-CTA of acute stroke patients using deep learning.

The triage of acute stroke patients is increasingly dependent on four-dimensional CTA (4D-CTA) imaging. In this work, we present a convolutional neural network (CNN) for image-level detection of intracranial anterior circulation artery occlusions in 4D-CTA. The method uses a normalized 3D time-to-signal (TTS) representation of the input image, which is sensitive to differences in the global arrival times caused by the potential presence of vascular pathologies. The TTS map presents the time within the cranial cavity at which the signal reaches a percentage of the maximum signal intensity, corrected for the baseline intensity. The method was trained and validated on (n=214) patient images and tested on an independent set of (n=279) patient images. This test set included all consecutive suspected-stroke patients admitted to our hospital in 2018. The accuracy, sensitivity, and specificity were 92%, 95%, and 92%. The area under the receiver operating characteristics curve was 0.98 (95% CI: 0.95- 0.99). These results show the feasibility of automated stroke triage in 4D-CTA.

Cerebral Artery and Vein Segmentation in Four-dimensional CT Angiography Using Convolutional Neural Networks.

PURPOSE: To implement and test a deep learning approach for the segmentation of the arterial and venous cerebral vasculature with four-dimensional (4D) CT angiography. MATERIALS AND METHODS: Patients who had undergone 4D CT angiography for the suspicion of acute ischemic stroke were retrospectively identified. A total of 390 patients evaluated in 2014 (n = 113) or 2018 (n = 277) were included in this study, with each patient having undergone one 4D CT angiographic scan. One hundred patients from 2014 were randomly selected, and the arteries and veins on their CT scans were manually annotated by five experienced observers. The weighted temporal average and weighted temporal variance from 4D CT angiography were used as input for a three-dimensional Dense-U-Net. The network was trained with the fully annotated cerebral vessel artery-vein maps from 60 patients. Forty patients were used for quantitative evaluation. The relative absolute volume difference and the Dice similarity coefficient are reported. The neural network segmentations from 277 patients who underwent scanning in 2018 were qualitatively evaluated by an experienced neuroradiologist using a five-point scale. RESULTS: The average time for processing arterial and venous cerebral vasculature with the network was less than 90 seconds. The mean Dice similarity coefficient in the test set was 0.80 ± 0.04 (standard deviation) for the arteries and 0.88 ± 0.03 for the veins. The mean relative absolute volume difference was 7.3% ± 5.7 for the arteries and 8.5% ± 4.8 for the veins. Most of the segmentations (n = 273, 99.3%) were rated as very good to perfect. CONCLUSION: The proposed convolutional neural network enables accurate artery and vein segmentation with 4D CT angiography with a processing time of less than 90 seconds.© RSNA, 2020.

Intracerebral Haemorrhage Segmentation in Non-Contrast CT.

A 3-dimensional (3D) convolutional neural network is presented for the segmentation and quantification of spontaneous intracerebral haemorrhage (ICH) in non-contrast computed tomography (NCCT). The method utilises a combination of contextual information on multiple scales for fast and fully automatic dense predictions. To handle a large class imbalance present in the data, a weight map is introduced during training. The method was evaluated on two datasets of 25 and 50 patients respectively. The reference standard consisted of manual annotations for each ICH in the dataset. Quantitative analysis showed a median Dice similarity coefficient of 0.91 [0.87-0.94] and 0.90 [0.85-0.92] for the two test datasets in comparison to the reference standards. Evaluation of a separate dataset of 5 patients for the assessment of the observer variability produced a mean Dice similarity coefficient of 0.95 ± 0.02 for the inter-observer variability and 0.97 ± 0.01 for the intra-observer variability. The average prediction time for an entire volume was 104 ± 15 seconds. The results demonstrate that the method is accurate and approaches the performance of expert manual annotation.

Validated imaging biomarkers as decision-making tools in clinical trials and routine practice: current status and recommendations from the EIBALL* subcommittee of the European Society of Radiology (ESR).

Observer-driven pattern recognition is the standard for interpretation of medical images. To achieve global parity in interpretation, semi-quantitative scoring systems have been developed based on observer assessments; these are widely used in scoring coronary artery disease, the arthritides and neurological conditions and for indicating the likelihood of malignancy. However, in an era of machine learning and artificial intelligence, it is increasingly desirable that we extract quantitative biomarkers from medical images that inform on disease detection, characterisation, monitoring and assessment of response to treatment. Quantitation has the potential to provide objective decision-support tools in the management pathway of patients. Despite this, the quantitative potential of imaging remains under-exploited because of variability of the measurement, lack of harmonised systems for data acquisition and analysis, and crucially, a paucity of evidence on how such quantitation potentially affects clinical decision-making and patient outcome. This article reviews the current evidence for the use of semi-quantitative and quantitative biomarkers in clinical settings at various stages of the disease pathway including diagnosis, staging and prognosis, as well as predicting and detecting treatment response. It critically appraises current practice and sets out recommendations for using imaging objectively to drive patient management decisions.

Cerebral arteries: fully automated segmentation from CT angiography--a feasibility study.

The purpose of this study was to retrospectively assess the feasibility of a fully automated image postprocessing tool for the segmentation of the arterial cerebrovasculature from computed tomographic (CT) angiography in 27 patients (nine men, 18 women; mean age, 55 years; age range, 33-76 years) with subarachnoid hemorrhage. The institutional review board approved this study, and informed consent was waived. The proposed method, which does not require the acquisition of an additional CT scan for bone suppression, consists of the following: (a) automatic detection of the main arteries for initialization, (b) segmentation of these arteries through the skull base, and (c) suppression of the large veins near the skull. The parameters of this method were optimized on the training subset of nine patients, and the method was successful at segmentation of the arteries in 15 (83%) of the 18 remaining patients. The difference between automatic and manual diameter measurements was 0.0 mm +/- 0.4 (standard deviation). The study results showed that fully automated segmentation of the cerebral arteries is feasible.

Circle of Willis Collateral Flow in Carotid Artery Occlusion Is Depicted by 4D-CTA.

BACKGROUND: In case of carotid artery occlusion, the risk and extent of ischemic cerebral damage are highly dependent on the pathways of collateral flow including the anatomy of the circle of Willis. In this report, cases are presented to illustrate that 4-dimensional computed tomography angiography (4D-CTA) can be considered as a noninvasive alternative to digital subtraction angiography for the evaluation of circle of Willis collateral flow. CASE DESCRIPTION: Five patients with unilateral internal carotid artery (ICA) occlusion underwent 4D-CTA for the evaluation of intracranial hemodynamics. Next to a visual evaluation of 4D-CTA, temporal information was visualized using a normalized color scale on the cerebral vasculature, which enabled quantification of the contrast bolus arrival time. In these patients, 4D-CTA demonstrated dominant middle cerebral artery blood supply on the side of ICA occlusion originating from either the contralateral ICA or posterior circulation via the communicating arteries. CONCLUSIONS: Temporal dynamics of collateral flow in the circle of Willis can be depicted with 4D-CTA in patients with a unilateral carotid artery occlusion.

Author Correction: White Matter and Gray Matter Segmentation in 4D Computed Tomography.

A correction to this article has been published and is linked from the HTML and PDF versions of this paper. The error has not been fixed in the paper.

Vessel axis tracking using topology constrained surface evolution.

An approach to 3-D vessel axis tracking based on surface evolution is presented. The main idea is to guide the evolution of the surface by analyzing its skeleton topology during evolution, and imposing shape constraints on the topology. For example, the intermediate topology can be processed such that it represents a single vessel segment, a bifurcation, or a more complex vascular topology. The evolving surface is then reinitialized with the newly found topology. Reinitialization is a crucial step since it creates probing behavior of the evolving front, encourages the segmentation process to extract the vascular structure of interest and reduces the risk on leaking of the curve into the background. The method was evaluated in two computed tomography angiography applications: 1) extracting the internal carotid arteries including the region in which they traverse through the skull base, which is challenging due to the proximity of bone structures and overlap in intensity values; 2) extracting the carotid bifurcations including many cases in which they are severely stenosed and contain calcifications. The vessel axis was found in 90% (18/20 internal carotids in ten patients) and 70% (14/20 carotid bifurcations in a different set of ten patients) of the cases.

Robust cranial cavity segmentation in CT and CT perfusion images of trauma and suspected stroke patients.

A robust and accurate method is presented for the segmentation of the cranial cavity in computed tomography (CT) and CT perfusion (CTP) images. The method consists of multi-atlas registration with label fusion followed by a geodesic active contour levelset refinement of the segmentation. Pre-registration atlas selection based on differences in anterior skull anatomy reduces computation time whilst optimising performance. The method was evaluated on a large clinical dataset of 573 acute stroke and trauma patients that received a CT or CTP in our hospital in the period February 2015-December 2015. The database covers a large spectrum of the anatomical and pathological variations that is typically observed in everyday clinical practice. Three orthogonal slices were randomly selected per patient and manually annotated, resulting in 1659 reference annotations. Segmentations were initially visually inspected for the entire study cohort to assess failures. A total of 20 failures were reported. Quantitative evaluation in comparison to the reference dataset showed a mean Dice coefficient of 98.36 ±  2.59%. The results demonstrate that the method closely approaches the high performance of expert manual annotation.

White Matter and Gray Matter Segmentation in 4D Computed Tomography.

Modern Computed Tomography (CT) scanners are capable of acquiring contrast dynamics of the whole brain, adding functional to anatomical information. Soft tissue segmentation is important for subsequent applications such as tissue dependent perfusion analysis and automated detection and quantification of cerebral pathology. In this work a method is presented to automatically segment white matter (WM) and gray matter (GM) in contrast- enhanced 4D CT images of the brain. The method starts with intracranial segmentation via atlas registration, followed by a refinement using a geodesic active contour with dominating advection term steered by image gradient information, from a 3D temporal average image optimally weighted according to the exposures of the individual time points of the 4D CT acquisition. Next, three groups of voxel features are extracted: intensity, contextual, and temporal. These are used to segment WM and GM with a support vector machine. Performance was assessed using cross validation in a leave-one-patient-out manner on 22 patients. Dice coefficients were 0.81 ± 0.04 and 0.79 ± 0.05, 95% Hausdorff distances were 3.86 ± 1.43 and 3.07 ± 1.72 mm, for WM and GM, respectively. Thus, WM and GM segmentation is feasible in 4D CT with good accuracy.