MRI-Based Brain Volume Scoring in Cerebral Malaria Is Externally Valid and Applicable to Lower-Resolution Images.

BACKGROUND AND PURPOSE: Children with cerebral malaria have an elevated risk of mortality and neurologic morbidity. Both mortality and morbidity are associated with initially increased brain volume on MR imaging, as graded by the Brain Volume Score, a subjective ordinal rating scale created specifically for brain MRIs in children with cerebral malaria. For the Brain Volume Score to be more widely clinically useful, we aimed to determine its independent reproducibility and whether it can be applicable to lower-resolution MRIs. MATERIALS AND METHODS: To assess the independent reproducibility of the Brain Volume Score, radiologists not associated with the initial study were trained to score MRIs from a new cohort of patients with cerebral malaria. These scores were then compared with survival and neurologic outcomes. To assess the applicability to lower-resolution MRI, we assigned Brain Volume Scores to brain MRIs degraded to simulate a very-low-field (64 mT) portable scanner and compared these with the original scores assigned to the original nondegraded MRIs. RESULTS: Brain Volume Scores on the new cohort of patients with cerebral malaria were highly associated with outcomes (OR for mortality = 16, P < .001). Scoring of the simulated degraded images remained consistent with the Brain Volume Scores assigned to the original higher-quality (0.35 T) images (intraclass coefficients > 0.86). CONCLUSIONS: Our findings demonstrate that the Brain Volume Score is externally valid in reproducibly predicting outcomes and can be reliably assigned to lower-resolution images.

Cerebral Metabolic Crisis in Pediatric Cerebral Malaria.

Cerebral metabolic energy crisis (CMEC), often defined as a cerebrospinal fluid (CSF) lactate: pyruvate ratio (LPR) >40, occurs in various diseases and is associated with poor neurologic outcomes. Cerebral malaria (CM) causes significant mortality and neurodisability in children worldwide. Multiple factors that could lead to CMEC are plausible in these patients, but its frequency has not been explored. Fifty-three children with CM were enrolled and underwent analysis of CSF lactate and pyruvate levels. All 53 patients met criteria for a CMEC (median CSF LPR of 72.9 [interquartile range [IQR]: 58.5-93.3]). Half of children met criteria for an ischemic CMEC (median LPR of 85 [IQR: 73-184]) and half met criteria for a nonischemic CMEC (median LPR of 60 [IQR: 54-79]. Children also underwent transcranial doppler ultrasound investigation. Cerebral blood flow velocities were more likely to meet diagnostic criteria for low flow (<2 standard deviation from normal) or vasospasm in children with an ischemic CMEC (73%) than in children with a nonischemic CMEC (20%, p = 0.04). Children with an ischemic CMEC had poorer outcomes (pediatric cerebral performance category of 3-6) than those with a nonischemic CMEC (46 vs. 22%, p = 0.03). CMEC was ubiquitous in this patient population and the processes underlying the two subtypes (ischemic and nonischemic) may represent targets for future adjunctive therapies.

Automated tumor segmentation and brain tissue extraction from multiparametric MRI of pediatric brain tumors: A multi-institutional study.

Background: Brain tumors are the most common solid tumors and the leading cause of cancer-related death among all childhood cancers. Tumor segmentation is essential in surgical and treatment planning, and response assessment and monitoring. However, manual segmentation is time-consuming and has high interoperator variability. We present a multi-institutional deep learning-based method for automated brain extraction and segmentation of pediatric brain tumors based on multi-parametric MRI scans. Methods: Multi-parametric scans (T1w, T1w-CE, T2, and T2-FLAIR) of 244 pediatric patients ( n = 215 internal and n = 29 external cohorts) with de novo brain tumors, including a variety of tumor subtypes, were preprocessed and manually segmented to identify the brain tissue and tumor subregions into four tumor subregions, i.e., enhancing tumor (ET), non-enhancing tumor (NET), cystic components (CC), and peritumoral edema (ED). The internal cohort was split into training ( n = 151), validation ( n = 43), and withheld internal test ( n = 21) subsets. DeepMedic, a three-dimensional convolutional neural network, was trained and the model parameters were tuned. Finally, the network was evaluated on the withheld internal and external test cohorts. Results: Dice similarity score (median ± SD) was 0.91 ± 0.10/0.88 ± 0.16 for the whole tumor, 0.73 ± 0.27/0.84 ± 0.29 for ET, 0.79 ± 19/0.74 ± 0.27 for union of all non-enhancing components (i.e., NET, CC, ED), and 0.98 ± 0.02 for brain tissue in both internal/external test sets. Conclusions: Our proposed automated brain extraction and tumor subregion segmentation models demonstrated accurate performance on segmentation of the brain tissue and whole tumor regions in pediatric brain tumors and can facilitate detection of abnormal regions for further clinical measurements.

Automated Tumor Segmentation and Brain Tissue Extraction from Multiparametric MRI of Pediatric Brain Tumors: A Multi-Institutional Study.

Background: Brain tumors are the most common solid tumors and the leading cause of cancer-related death among all childhood cancers. Tumor segmentation is essential in surgical and treatment planning, and response assessment and monitoring. However, manual segmentation is time-consuming and has high interoperator variability. We present a multi-institutional deep learning-based method for automated brain extraction and segmentation of pediatric brain tumors based on multi-parametric MRI scans. Methods: Multi-parametric scans (T1w, T1w-CE, T2, and T2-FLAIR) of 244 pediatric patients (n=215 internal and n=29 external cohorts) with de novo brain tumors, including a variety of tumor subtypes, were preprocessed and manually segmented to identify the brain tissue and tumor subregions into four tumor subregions, i.e., enhancing tumor (ET), non-enhancing tumor (NET), cystic components (CC), and peritumoral edema (ED). The internal cohort was split into training (n=151), validation (n=43), and withheld internal test (n=21) subsets. DeepMedic, a three-dimensional convolutional neural network, was trained and the model parameters were tuned. Finally, the network was evaluated on the withheld internal and external test cohorts. Results: Dice similarity score (median±SD) was 0.91±0.10/0.88±0.16 for the whole tumor, 0.73±0.27/0.84±0.29 for ET, 0.79±19/0.74±0.27 for union of all non-enhancing components (i.e., NET, CC, ED), and 0.98±0.02 for brain tissue in both internal/external test sets. Conclusions: Our proposed automated brain extraction and tumor subregion segmentation models demonstrated accurate performance on segmentation of the brain tissue and whole tumor regions in pediatric brain tumors and can facilitate detection of abnormal regions for further clinical measurements. Key Points: We proposed automated tumor segmentation and brain extraction on pediatric MRI.The volumetric measurements using our models agree with ground truth segmentations. Importance of the Study: The current response assessment in pediatric brain tumors (PBTs) is currently based on bidirectional or 2D measurements, which underestimate the size of non-spherical and complex PBTs in children compared to volumetric or 3D methods. There is a need for development of automated methods to reduce manual burden and intra- and inter-rater variability to segment tumor subregions and assess volumetric changes. Most currently available automated segmentation tools are developed on adult brain tumors, and therefore, do not generalize well to PBTs that have different radiological appearances. To address this, we propose a deep learning (DL) auto-segmentation method that shows promising results in PBTs, collected from a publicly available large-scale imaging dataset (Children's Brain Tumor Network; CBTN) that comprises multi-parametric MRI scans of multiple PBT types acquired across multiple institutions on different scanners and protocols. As a complementary to tumor segmentation, we propose an automated DL model for brain tissue extraction.

Automated analysis of low-field brain MRI in cerebral malaria.

A central challenge of medical imaging studies is to extract biomarkers that characterize disease pathology or outcomes. Modern automated approaches have found tremendous success in high-resolution, high-quality magnetic resonance images. These methods, however, may not translate to low-resolution images acquired on magnetic resonance imaging (MRI) scanners with lower magnetic field strength. In low-resource settings where low-field scanners are more common and there is a shortage of radiologists to manually interpret MRI scans, it is critical to develop automated methods that can augment or replace manual interpretation, while accommodating reduced image quality. We present a fully automated framework for translating radiological diagnostic criteria into image-based biomarkers, inspired by a project in which children with cerebral malaria (CM) were imaged using low-field 0.35 Tesla MRI. We integrate multiatlas label fusion, which leverages high-resolution images from another sample as prior spatial information, with parametric Gaussian hidden Markov models based on image intensities, to create a robust method for determining ventricular cerebrospinal fluid volume. We also propose normalized image intensity and texture measurements to determine the loss of gray-to-white matter tissue differentiation and sulcal effacement. These integrated biomarkers have excellent classification performance for determining severe brain swelling due to CM.

Mechanisms of Transcranial Doppler Ultrasound phenotypes in paediatric cerebral malaria remain elusive.

BACKGROUND: Cerebral malaria (CM) results in significant paediatric death and neurodisability in sub-Saharan Africa. Several different alterations to typical Transcranial Doppler Ultrasound (TCD) flow velocities and waveforms in CM have been described, but mechanistic contributors to these abnormalities are unknown. If identified, targeted, TCD-guided adjunctive therapy in CM may improve outcomes. METHODS: This was a prospective, observational study of children 6 months to 12 years with CM in Blantyre, Malawi recruited between January 2018 and June 2021. Medical history, physical examination, laboratory analysis, electroencephalogram, and magnetic resonance imaging were undertaken on presentation. Admission TCD results determined phenotypic grouping following a priori definitions. Evaluation of the relationship between haemodynamic, metabolic, or intracranial perturbations that lead to these observed phenotypes in other diseases was undertaken. Neurological outcomes at hospital discharge were evaluated using the Paediatric Cerebral Performance Categorization (PCPC) score. RESULTS: One hundred seventy-four patients were enrolled. Seven (4%) had a normal TCD examination, 57 (33%) met criteria for hyperaemia, 50 (29%) for low flow, 14 (8%) for microvascular obstruction, 11 (6%) for vasospasm, and 35 (20%) for isolated posterior circulation high flow. A lower cardiac index (CI) and higher systemic vascular resistive index (SVRI) were present in those with low flow than other groups (p < 0.003), though these values are normal for age (CI 4.4 [3.7,5] l/min/m2, SVRI 1552 [1197,1961] dscm-5m2). Other parameters were largely not significantly different between phenotypes. Overall, 118 children (68%) had a good neurological outcome. Twenty-three (13%) died, and 33 (19%) had neurological deficits. Outcomes were best for participants with hyperaemia and isolated posterior high flow (PCPC 1-2 in 77 and 89% respectively). Participants with low flow had the least likelihood of a good outcome (PCPC 1-2 in 42%) (p < 0.001). Cerebral autoregulation was significantly better in children with good outcome (transient hyperemic response ratio (THRR) 1.12 [1.04,1.2]) compared to a poor outcome (THRR 1.05 [0.98,1.02], p = 0.05). CONCLUSIONS: Common pathophysiological mechanisms leading to TCD phenotypes in non-malarial illness are not causative in children with CM. Alternative mechanistic contributors, including mechanical factors of the cerebrovasculature and biologically active regulators of vascular tone should be explored.

Beyond the Spinal Canal.

Cross-sectional spinal imaging is common, and extraspinal findings are often incidentally identified during interpretation. Although some of these findings may cause symptoms that mimic a spinal disorder, the majority are entirely asymptomatic and incidental. It is essential that the radiologist not only identify those abnormalities that may have clinical significance but also recognize those that are clinically irrelevant and thereby prevent patients from being subjected to further unnecessary, expensive and potentially harmful interventions. This article focuses on those abnormalities that are commonly encountered and provides practical guidance for follow-up and management based on current recommendations.

Real-time MRI-guided percutaneous sclerotherapy treatment of venous low-flow malformations in the head and neck.

Purpose This manuscript describes the technique of real-time MRI-guided sclerotherapy for low-flow venous malformations in the head and neck based on our institutional experience. Materials and methods Ethanolamine oleate is used as the sclerosant and is mixed with gadolinium for visualization during the procedure. The five procedural steps include: (I) an initial tri-plane T2-weighted sequence to visualize the lesion; (II) a T1 FSE or trueFISP sequence to assess needle placement and advancement within the lesion; (III) a tri-plane T1 FLASH sequence to monitor sclerosant injection; (IV) a T1 FSE or VIBE sequence to assess sclerosant coverage of the malformation before needle removal; (V) a post-procedural tri-plane T1 fat-saturated sequence to confirm sclerosant coverage of the lesion. Periprocedural medications typically include steroids, antibiotic prophylaxis, and non-steroidal anti-inflammatory medication. Patients are typically admitted for overnight observation. Conclusion Real-time MRI-guided sclerotherapy for low-flow venous malformations in the head and neck is effective and safe.

Real-time MRI-guided percutaneous sclerotherapy of low-flow head and neck lymphatic malformations in the pediatric population - a stepwise approach.

Real-time MRI-guided percutaneous sclerotherapy is a novel and evolving treatment for congenital lymphatic malformations in the head and neck. We elaborate on the specific steps necessary to perform an MRI-guided percutaneous sclerotherapy of lymphatic malformations including pre-procedure patient work-up and preparation, stepwise intraprocedural interventional techniques and post-procedure management. Based on our institutional experience, MRI-guided sclerotherapy with a doxycycline-gadolinium-based mixture as a sclerosant for lymphatic malformations of the head and neck region in children is well tolerated and effective.

Impact of Patient Protection and Affordable Care Act on academic radiology departments' clinical, research, and education missions.

The Patient Protection and Affordable Care Act (ACA) generated significant media attention since its inception. When the law was approved in 2010, the U.S. health care system began facing multiple changes to adapt and to incorporate measures to meet the new requirements. These mandatory changes will be challenging for academic radiology departments (ARDs) since they will need to promote a shift from a volume-focused to a value-focused practice. This will affect all components of the mission of ARDs, including clinical practice, education, and research. A unique key element to success in this transition is to focus on both quality and safety, thus improving the value of radiology in the post-ACA era. Given the changes ARDs will face during the implementation of ACA, suggestions are provided on how to adapt ARDs to this new environment.