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BACKGROUND: Accessible tools to efficiently detect and segment diffusion abnormalities in acute strokes are highly anticipated by the clinical and research communities. METHODS: We developed a tool with deep learning networks trained and tested on a large dataset of 2,348 clinical diffusion weighted MRIs of patients with acute and sub-acute ischemic strokes, and further tested for generalization on 280 MRIs of an external dataset (STIR). RESULTS: Our proposed model outperforms generic networks and DeepMedic, particularly in small lesions, with lower false positive rate, balanced precision and sensitivity, and robustness to data perturbs (e.g., artefacts, low resolution, technical heterogeneity). The agreement with human delineation rivals the inter-evaluator agreement; the automated lesion quantification of volume and contrast has virtually total agreement with human quantification. CONCLUSION: Our tool is fast, public, accessible to non-experts, with minimal computational requirements, to detect and segment lesions via a single command line. Therefore, it fulfills the conditions to perform large scale, reliable and reproducible clinical and translational research.

A 9-year-old child presented with a palpable swelling under the right lower lid for 3 years. On examination, the swelling was nontender, well circumscribed, and cystic on palpation. A computed tomography scan showed a well circumscribed, hyperdense lesion in the inferior orbit with no anatomic connection with the paranasal sinuses. Transcutaneous orbitotomy was performed, and the lesion was excised in toto. Histopathological examination was suggestive of a mucocele. [ABSTRACT FROM AUTHOR]

The article describes the case of a 21-year-old lactating female patient with epithelioid hemangioma. The medical history of the patient and the symptoms she manifested are presented. Physical examination, laboratory tests and imaging done on the patient are mentioned. Background information on the condition is provided.

Transcranial focused ultrasound stimulation (tFUS) has been proven capable of altering focal neuronal activities and neural circuits non-invasively in both animals and humans. The abilities of tFUS for cell-type selection within the targeted area like somatosensory cortex have been shown to be parameter related. However, how neuronal subpopulations across neural pathways are affected, for example how tFUS affected neuronal connections between brain areas remains unclear. In this study, multi-site intracranial recordings were used to quantify the neuronal responses to tFUS stimulation at somatosensory cortex (S1), motor cortex (M1) and posterior medial thalamic nucleus (POm) of cortico-thalamo-cortical (CTC) pathway. We found that when targeting at S1 or POm, only regular spiking units (RSUs, putative excitatory neurons) responded to specific tFUS parameters (duty cycle: 6%-60% and pulse repetition frequency: 1500 and 3000 Hz ) during sonication. RSUs from the directly connected area (POm or S1) showed a synchronized response, which changed the directional correlation between RSUs from POm and S1. The tFUS induced excitation of RSUs activated the feedforward and feedback loops between cortex and thalamus, eliciting delayed neuronal responses of RSUs and delayed activities of fast spiking units (FSUs) by affecting local network. Our findings indicated that tFUS can modulate the CTC pathway through both feedforward and feedback loops, which could influence larger cortical areas including motor cortex.

Transcranial focused ultrasound (tFUS) is a promising neuromodulation technique able to target shallow and deep brain structures with high precision. Previous studies have demonstrated that tFUS stimulation responses are both cell-type specific and controllable through altering stimulation parameters. Specifically, tFUS can elicit time-locked neural activity in regular spiking units (RSUs) that is sensitive to increases in pulse repetition frequency (PRF), while time-locked responses are not seen in fast spiking units (FSUs). These findings suggest a unique capability of tFUS to alter circuit network dynamics with cell-type specificity; however, these results could be biased by the use of anesthesia, which significantly modulates neural activities. In this study, we develop an awake head-fixed rat model specifically designed for tFUS study, and address a key question if tFUS still has cell-type specificity under awake conditions. Using this novel animal model, we examined a series of PRFs and burst duty cycles (DCs) to determine their effects on neuronal subpopulations without anesthesia. We conclude that cell-type specific time-locked and delayed responses to tFUS as well as PRF and DC sensitivity are present in the awake animal model and that despite some differences in response, isoflurane anesthesia is not a major confound in studying the cell-type specificity of ultrasound neuromodulation. We further determine that, in an awake, head-fixed setting, the preferred PRF and DC for inducing time-locked excitation with our pulsed tFUS paradigm are 1500 Hz and 60%, respectively.

Background: Accessible tools to efficiently detect and segment diffusion abnormalities in acute strokes are highly anticipated by the clinical and research communities., Methods: We developed a tool with deep learning networks trained and tested on a large dataset of 2,348 clinical diffusion weighted MRIs of patients with acute and sub-acute ischemic strokes, and further tested for generalization on 280 MRIs of an external dataset (STIR)., Results: Our proposed model outperforms generic networks and DeepMedic, particularly in small lesions, with lower false positive rate, balanced precision and sensitivity, and robustness to data perturbs (e.g., artefacts, low resolution, technical heterogeneity). The agreement with human delineation rivals the inter-evaluator agreement; the automated lesion quantification of volume and contrast has virtually total agreement with human quantification., Conclusion: Our tool is fast, public, accessible to non-experts, with minimal computational requirements, to detect and segment lesions via a single command line. Therefore, it fulfills the conditions to perform large scale, reliable and reproducible clinical and translational research., Competing Interests: Competing interestsM.I.M. owns “AnatomyWorks”. This arrangement is managed by Johns Hopkins University in accordance with its conflict-of-interest policies. The remaining authors declare no competing interests., (© The Author(s) 2021.)