A Photocleavable Contrast Agent for Light-Responsive MRI

Thanks to its innocuousness and high spatiotemporal resolution, light is used in several established and emerging applications in biomedicine. Among them is the modulation of magnetic resonance imaging (MRI) contrast agents' relaxivity with the aim to increase the sensitivity, selectivity and amount of functional information obtained from this outstanding whole-body medical imaging technique. This approach requires the development of molecular contrast agents that show high relaxivity and strongly pronounced photo-responsiveness. To this end, we report here the design and synthesis of a light-activated MRI contrast agent, together with its evaluation using UV-vis spectroscopy, Fast Field Cycling (FFC) relaxometry and relaxometric measurements on clinical MRI scanners. The high relaxivity of the reported agent changes substantially upon irradiation with light, showing a 17% decrease in relaxivity at 0.23T upon irradiation with lambda = 400 nm (violet) light for 60 min. On clinical MRI scanners (1.5T and 3.0T), irradiation leads to a decrease in relaxivity of 9% and 19% after 3 and 60 min, respectively. The molecular design presents an important blueprint for the development of light-activatable MRI contrast agents

Automatic segmentation of the mandible from computed tomography scans for 3D virtual surgical planning using the convolutional neural network

Segmentation of mandibular bone in CT scans is crucial for 3D virtual surgical planning of craniofacial tumor resection and free flap reconstruction of the resection defect, in order to obtain a detailed surface representation of the bones. A major drawback of most existing mandibular segmentation methods is that they require a large amount of expert knowledge for manual or partially automatic segmentation. In fact, due to the lack of experienced doctors and experts, high quality expert knowledge is hard to achieve in practice. Furthermore, segmentation of mandibles in CT scans is influenced seriously by metal artifacts and large variations in their shape and size among individuals. In order to address these challenges we propose an automatic mandible segmentation approach in CT scans, which considers the continuum of anatomical structures through different planes. The approach adopts the architecture of the U-Net and then combines the resulting 2D segmentations from three orthogonal planes into a 3D segmentation. We implement such a segmentation approach on two head and neck datasets and then evaluate the performance. Experimental results show that our proposed approach for mandible segmentation in CT scans exhibits high accuracy

Biological laterality and peripheral nerve DTI metrics

BACKGROUND AND PURPOSE: Clinical comparisons do not usually take laterality into account and thus may report erroneous or misleading data. The concept of laterality, well evaluated in brain and motor systems, may also apply at the level of peripheral nerves. Therefore, we sought to evaluate the extent to which we could observe an effect of laterality in MRI-collected white matter indices of the sciatic nerve and its two branches (tibial and fibular). MATERIALS AND METHODS: We enrolled 17 healthy persons and performed peripheral nerve diffusion weighted imaging (DWI) and magnetization transfer imaging (MTI) of the sciatic, tibial and fibular nerve. Participants were scanned bilaterally, and findings were divided into ipsilateral and contralateral nerve fibers relative to self-reporting of hand dominance. Generalized estimating equation modeling was used to evaluate nerve fiber differences between ipsilateral and contralateral legs while controlling for confounding variables. All findings controlled for age, sex and number of scans performed. RESULTS: A main effect of laterality was found in radial, axial, and mean diffusivity for the tibial nerve. Axial diffusivity was found to be lateralized in the sciatic nerve. When evaluating mean MTR, a main effect of laterality was found for each nerve division. A main effect of sex was found in the tibial and fibular nerve fiber bundles. CONCLUSION: For the evaluation of nerve measures using DWI and MTI, in either healthy or disease states, consideration of underlying biological metrics of laterality in peripheral nerve fiber characteristics need to considered for data analysis. Integrating knowledge regarding biological laterality of peripheral nerve microstructure may be applied to improve how we diagnosis pain disorders, how we track patients’ recovery and how we forecast pain chronification

DTI and MTR Measures of Nerve Fiber Integrity in Pediatric Patients With Ankle Injury

Acute peripheral nerve injury can lead to chronic neuropathic pain. Having a standardized, non-invasive method to evaluate pathological changes in a nerve following nerve injury would help with diagnostic and therapeutic assessments or interventions. The accurate evaluation of nerve fiber integrity after injury may provide insight into the extent of pathology and a patient's level of self-reported pain. The aim of this investigation was to evaluate the extent to which peripheral nerve integrity could be evaluated in an acute ankle injury cohort and how markers of nerve fiber integrity correlate with self-reported pain levels in afferent nerves. We recruited 39 pediatric participants with clinically defined neuropathic pain within 3 months of an ankle injury and 16 healthy controls. Participants underwent peripheral nerve MRI using diffusion tensor (DTI) and magnetization transfer imaging (MTI) of their injured and non-injured ankles. The imaging window was focused on the branching point of the sciatic nerve into the tibial and fibular division. Each participant completed the Pain Detection Questionnaire (PDQ). Findings demonstrated group differences in DTI and MTI in the sciatic, tibial and fibular nerve in the injured ankle relative to healthy control and contralateral non-injured nerve fibers. Only AD and RD from the injured fibular nerve correlated with PDQ scores which coincides with the inversion-dominant nature of this particular ankle injuruy cohort. Exploratory analyses highlight the potential remodeling stages of nerve injury from neuropathic pain. Future research should emphasize sub-acute time frames of injury to capture post-injury inflammation and nerve fiber recovery

Focused ultrasound for opening blood-brain barrier and drug delivery monitored with positron emission tomography

Focused ultrasound (FUS) is a minimally-invasive technology used for treatment of many diseases, including diseases related to the colon, uterus, prostate, and brain. Although it has been mainly used for ablative procedures, the ability of FUS to open the blood-brain barrier (BBB) presents a promising new application. However, the mechanism of BBB opening by FUS remains unclear. This review focuses on the use of FUS to open the BBB for enhancing drug delivery and investigating how Positron Emission Tomography (PET) provides insight into the underlying mechanism

Recurrent Convolutional Neural Networks for 3D Mandible Segmentation in Computed Tomography

PURPOSE: Classic encoder-decoder-based convolutional neural network (EDCNN) approaches cannot accurately segment detailed anatomical structures of the mandible in computed tomography (CT), for instance, condyles and coronoids of the mandible, which are often affected by noise and metal artifacts. The main reason is that EDCNN approaches ignore the anatomical connectivity of the organs. In this paper, we propose a novel CNN-based 3D mandible segmentation approach that has the ability to accurately segment detailed anatomical structures. METHODS: Different from the classic EDCNNs that need to slice or crop the whole CT scan into 2D slices or 3D patches during the segmentation process, our proposed approach can perform mandible segmentation on complete 3D CT scans. The proposed method, namely, RCNNSeg, adopts the structure of the recurrent neural networks to form a directed acyclic graph in order to enable recurrent connections between adjacent nodes to retain their connectivity. Each node then functions as a classic EDCNN to segment a single slice in the CT scan. Our proposed approach can perform 3D mandible segmentation on sequential data of any varied lengths and does not require a large computation cost. The proposed RCNNSeg was evaluated on 109 head and neck CT scans from a local dataset and 40 scans from the PDDCA public dataset. The final accuracy of the proposed RCNNSeg was evaluated by calculating the Dice similarity coefficient (DSC), average symmetric surface distance (ASD), and 95% Hausdorff distance (95HD) between the reference standard and the automated segmentation. RESULTS: The proposed RCNNSeg outperforms the EDCNN-based approaches on both datasets and yields superior quantitative and qualitative performances when compared to the state-of-the-art approaches on the PDDCA dataset. The proposed RCNNSeg generated the most accurate segmentations with an average DSC of 97.48%, ASD of 0.2170 mm, and 95HD of 2.6562 mm on 109 CT scans, and an average DSC of 95.10%, ASD of 0.1367 mm, and 95HD of 1.3560 mm on the PDDCA dataset. CONCLUSIONS: The proposed RCNNSeg method generated more accurate automated segmentations than those of the other classic EDCNN segmentation techniques in terms of quantitative and qualitative evaluation. The proposed RCNNSeg has potential for automatic mandible segmentation by learning spatially structured information