Cerebrovascular reserve (CVR) reflects the capacity of cerebral blood flow (CBF) to change. Decreased CVR implies poor hemodynamics and is linked to a higher risk for stroke. Revascularization has been shown to improve CBF in patients with vasculopathy such as Moyamoya disease. Dynamic susceptibility contrast (DSC) can measure transit time to evaluate patients suspected of stroke. Arterial spin labeling (ASL) is a non-invasive technique for CBF, CVR, and arterial transit time (ATT) measurements. Here, we investigate the change in hemodynamics 4-12 months after extracranial-to-intracranial direct bypass in 52 Moyamoya patients using ASL with single and multiple post-labeling delays (PLD). Images were collected using ASL and DSC with acetazolamide. CVR, CBF, ATT, and time-to-maximum (Tmax) were measured in different flow territories. Results showed that hemodynamics improved significantly in regions affected by arterial occlusions after revascularization. CVR increased by 16 ± 11% (p < 0.01) and 25 ± 13% (p < 0.01) for single- and multi-PLD ASL, respectively. Transit time measured by multi-PLD ASL and post-vasodilation DSC reduced by 13 ± 7% (p < 0.01) and 9 ± 5% (p < 0.01), respectively. For all regions, ATT correlated significantly with Tmax (R2  =  0.59, p < 0.01). Thus, revascularization improved CVR and decreased transit times. Multi-PLD ASL can serve as an effective and non-invasive modality to examine vascular hemodynamics in Moyamoya patients.

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Background18F‐fluorodeoxyglucose (FDG) positron emission tomography (PET) is valuable for determining presence of viable tumor, but is limited by geographical restrictions, radiation exposure, and high cost.PurposeTo generate diagnostic‐quality PET equivalent imaging for patients with brain neoplasms by deep learning with multi‐contrast MRI.Study TypeRetrospective.SubjectsPatients (59 studies from 51 subjects; age 56 ± 13 years; 29 males) who underwent 18F‐FDG PET and MRI for determining recurrent brain tumor.Field Strength/Sequence3T; 3D GRE T1, 3D GRE T1c, 3D FSE T2‐FLAIR, and 3D FSE ASL, 18F‐FDG PET imaging.AssessmentConvolutional neural networks were trained using four MRIs as inputs and acquired FDG PET images as output. The agreement between the acquired and synthesized PET was evaluated by quality metrics and Bland–Altman plots for standardized uptake value ratio. Three physicians scored image quality on a 5‐point scale, with score ≥3 as high‐quality. They assessed the lesions on a 5‐point scale, which was binarized to analyze diagnostic consistency of the synthesized PET compared to the acquired PET.Statistical TestsThe agreement in ratings between the acquired and synthesized PET were tested with Gwet's AC and exact Bowker test of symmetry. Agreement of the readers was assessed by Gwet's AC. P = 0.05 was used as the cutoff for statistical significance.ResultsThe synthesized PET visually resembled the acquired PET and showed significant improvement in quality metrics (+21.7% on PSNR, +22.2% on SSIM, −31.8% on RSME) compared with ASL. A total of 49.7% of the synthesized PET were considered as high‐quality compared to 73.4% of the acquired PET which was statistically significant, but with distinct variability between readers. For the positive/negative lesion assessment, the synthesized PET had an accuracy of 87% but had a tendency to overcall.ConclusionThe proposed deep learning model has the potential of synthesizing diagnostic quality FDG PET images without the use of radiotracers.Evidence Level3Technical EfficacyStage 2

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Aiming to raise awareness for the possibility of schistosomal involvement of the central nervous system in travellers returning from endemic areas and/or immigrants to nonendemic areas, the authors report a case of neuroschistosomiasis in a Portuguese patient coming from the Republic of São Tomé and Príncipe with good clinical outcome following praziquantel therapy. This is the first case of neuroschistosomiasis associated with São Tomé and Príncipe reported in literature and further studies are needed to confirm which species of this parasite are endemic of that region. We conclude that early diagnosis is key to reduce clinical severity and therefore validation of new diagnostic techniques and establishment of consensual treatment guidelines would be important.

Traumatic chiasmal syndrome is one of the rare etiologies of chiasmal syndrome, characterized by optic chiasm injury following head trauma. The main visual defect associated is bitemporal hemianopia with macular splitting; however, it can present with a variety of other visual defects and neurologic signs. The authors report a case of complete bitemporal hemianopia after head trauma, with multiple frontal and skull base fractures and no other neurologic deficits, or hypothalamic-pituitary abnormality. Most cases of traumatic chiasmal syndrome are caused by mechanical stretch or compression of the chiasma. Nevertheless, in this case, the radiologic findings excluded macroscopic disruption or external compression of the chiasma, raising the possibility of a contusion necrosis associated with functional impairment of the optic chiasma. Traumatic chiasmal syndrome must be considered in the differential diagnosis of patients presenting with complete bitemporal hemianopia after head injury caused by frontal and skull base fracture.

Subcutaneous emphysema is a possible but infrequent consequence of dental procedures. We present the case of a 6-year-old healthy boy transferred from a dental clinic immediately after local anaesthesia for tooth extraction, due to sudden orbital and facial swelling. On physical examination, oedema of the left upper eyelid with fine crepitus on palpation and left hemiface oedema with local pain were observed. Ophthalmologic observation was normal. CT scan of the face and orbits documented extensive infiltration of the subcutaneous tissue planes of the left face by air, with extension to the external part of the body of the mandible, retromaxillary fat, masticatory muscle spaces, parapharyngeal space and adjacent to the orbital roof. After completing initial evaluation, the dentist confirmed the use of an air-driven device during local anaesthesia administration. The patient improved with conservative treatment. Early recognition of this condition is essential to provide an adequate clinical assessment with exclusion of possible life-threatening complications.

Accurate quantification of cerebral blood flow (CBF) is essential for the diagnosis and assessment of cerebrovascular diseases such as Moyamoya, carotid stenosis, aneurysms, and stroke. Positron emission tomography (PET) is currently regarded as the gold standard for the measurement of CBF in the human brain. PET imaging, however, is not widely available because of its prohibitive costs, use of ionizing radiation, and logistical challenges, which require a co-localized cyclotron to deliver the 2 min half-life 15 O radioisotope. Magnetic resonance imaging (MRI), in contrast, is more readily available and does not involve ionizing radiation. In this study, we propose a multitask learning framework for brain MRI-to-PET translation and disease diagnosis. The proposed framework comprises two prime networks: (1) an attention-based 3D encoder-decoder convolutional neural network (CNN) that synthesizes high-quality PET CBF maps from multi-contrast MRI images, and (2) a multi-scale 3D CNN that identifies the brain disease corresponding to the input MRI images. Our multi-task framework yields promising results on the task of MRI-to-PET translation, achieving an average structural similarity index (SSIM) of 0.94 and peak signal-to-noise ratio (PSNR) of 38dB on a cohort of 120 subjects. In addition, we show that integrating multiple MRI modalities can improve the clinical diagnosis of brain diseases.