paper_authors: Alessandro Fontanella, Wenwen Li, Grant Mair, Antreas Antoniou, Eleanor Platt, Chloe Martin, Paul Armitage, Emanuele Trucco, Joanna Wardlaw, Amos Storkey for: This paper aims to address the challenge of preparing clinical brain CT datasets for deep learning (DL) analysis.methods: The authors propose a complete semi-automatic pipeline to standardize the heterogeneous dataset, which includes handling image sets with different orientations, image types, and dimensions, and removing redundant background.results: The final pipeline was able to process 5,868/10,659 (45%) CT image datasets, with the majority of axial scans being accepted after adjustments such as image cropping, resizing, and scaling. However, 465 scans failed the registration process.Abstract
Despite the large amount of brain CT data generated in clinical practice, the availability of CT datasets for deep learning (DL) research is currently limited. Furthermore, the data can be insufficiently or improperly prepared for machine learning and thus lead to spurious and irreproducible analyses. This lack of access to comprehensive and diverse datasets poses a significant challenge for the development of DL algorithms. In this work, we propose a complete semi-automatic pipeline to address the challenges of preparing a clinical brain CT dataset for DL analysis and describe the process of standardising this heterogeneous dataset. Challenges include handling image sets with different orientations (axial, sagittal, coronal), different image types (to view soft tissues or bones) and dimensions, and removing redundant background. The final pipeline was able to process 5,868/10,659 (45%) CT image datasets. Reasons for rejection include non-axial data (n=1,920), bone reformats (n=687), separated skull base/vault images (n=1,226), and registration failures (n=465). Further format adjustments, including image cropping, resizing and scaling are also needed for DL processing. Of the axial scans that were not localisers, bone reformats or split brains, 5,868/6,333 (93%) were accepted, while the remaining 465 failed the registration process. Appropriate preparation of medical imaging datasets for DL is a costly and time-intensive process.
摘要
尽管在临床实践中生成了大量的脑CT数据，但现在对深度学习（DL）研究的CT数据仍然受到限制。此外，数据可能未经正确准备，导致机器学习分析出现假象和不可重复的问题。这种数据的限制对DL算法的发展带来了重大挑战。在这项工作中，我们提出了一个完整的半自动化管道，以解决在临床脑CT数据上进行DL分析前的挑战。我们描述了处理不同方向（AXIAL、SAGGITAL、CORONAL）、不同图像类型（观察软组织或骨骼）和维度等多种挑战。我们的最终管道可以处理5,868/10,659（45%）的CT图像集。拒绝原因包括非AXIAL数据（n=1,920）、骨 Reformats（n=687）、分割的颅骨基/顶层图像（n=1,226）以及注册失败（n=465）。进一步的格式调整，包括图像剪辑、缩放和缩放，还是需要DL处理。AXIAL扫描中未经本地化的、骨 Reformats或分割的脑，5,868/6,333（93%）被接受，剩下465个失败了注册过程。适当地准备医学成像数据 дляDL是一项成本高和时间投入巨大的过程。
Thalamic nuclei segmentation from T$_1$-weighted MRI: unifying and benchmarking state-of-the-art methods with young and old cohorts
for: 这个研究是为了比较不同State of the art的腋带神经分 segmentation方法的效果，以及这些方法在识别健康人群和阿尔ц海默病人群之间的分化能力。
methods: 这个研究使用了四种State of the art的腋带神经分 segmentation方法，包括FreeSurfer、HIPS-THOMAS、SCS-CNN和T1-THOMAS。这些方法都被应用在T1 MRI图像上，并被比较使用 overlap和异同度量来评估其精度。
results: 研究发现，HIPS-THOMAS方法能够最好地分解各个腋带神经元的尺度，并且能够最 accurately地识别健康人群和阿尔ц海默病人群之间的分化。此外，研究还发现，这些方法的识别健康人群和阿尔ц海默病人群的精度随着疾病的进程而变化。Abstract
The thalamus and its constituent nuclei are critical for a broad range of cognitive and sensorimotor processes, and implicated in many neurological and neurodegenerative conditions. However, the functional involvement and specificity of thalamic nuclei in human neuroimaging is underappreciated and not well studied due, in part, to technical challenges of accurately identifying and segmenting nuclei. This challenge is further exacerbated by a lack of common nomenclature for comparing segmentation methods. Here, we use data from healthy young (Human Connectome Project, 100 subjects) and older healthy adults, plus those with minor cognitive impairment and Alzheimer$'$s disease (Alzheimer$'$s Disease Neuroimaging Initiative, 540 subjects), to benchmark four state of the art thalamic segmentation methods for T1 MRI (FreeSurfer, HIPS-THOMAS, SCS-CNN, and T1-THOMAS) under a single segmentation framework. Segmentations were compared using overlap and dissimilarity metrics to the Morel stereotaxic atlas. We also quantified each method$'$s estimation of thalamic nuclear degeneration across Alzheimer$'$s disease progression, and how accurately early and late mild cognitive impairment, and Alzheimers disease could be distinguished from healthy controls. We show that HIPS-THOMAS produced the most effective segmentations of individual thalamic nuclei and was also most accurate in discriminating healthy controls from those with mild cognitive impairment and Alzheimer$'$s disease using individual nucleus volumes. This work is the first to systematically compare the efficacy of anatomical thalamic segmentation approaches under a unified nomenclature. We also provide recommendations of which segmentation method to use for studying the functional relevance of specific thalamic nuclei, based on their overlap and dissimilarity with the Morel atlas.
摘要
腔室和其组成部分 nuclei 对认知和感觉过程具有关键作用，并且与许多神经病理和神经退化病种相关。然而，人类腔室 nuclei 的功能参与度和特定性在人像成像中尚未得到充分认可和研究，这主要是因为识别和分割腔室 nuclei 技术上的挑战。此外，不同的识别方法之间没有共同的命名标准，进一步增加了研究难度。本文使用100名健康年轻人（人类连接计划）和older健康成人（540名），以及急性认知障碍和阿尔茨海默病（阿尔茨海默病成像计划）的Subjects，对4种state-of-the-art腔室分割方法（FreeSurfer、HIPS-THOMAS、SCS-CNN和T1-THOMAS）进行比较，并使用单一分割框架。分割结果与Moreel颈部 Atlases 进行比较，并计算每种方法对腔室 nuclear degeneration 的估计，以及在阿尔茨海默病进程中，健康控制组和急性认知障碍、阿尔茨海默病之间的区别如何准确。结果表明HIPS-THOMAS方法生成了最有效的各个腔室 nuclei 分割，并且在健康控制组和急性认知障碍、阿尔茨海默病之间的分割结果最为准确。本研究是首次系统地比较了不同的腔室分割方法，并提供了选择具体腔室 nuclei 研究功能相关性的建议，基于它们与Moreel Atlases 的重叠和不同程度。
Multiplex ultrasound imaging of perfluorocarbon nanodroplets enabled by decomposition of post-vaporization dynamics
paper_authors: Austin Van Namen, Sidhartha Jandhyala, Catalina-Paula Spatarelu, Kenneth M. Tichauer, Kimberley S. Samkoe, Geoffrey P. Luke
for: This paper aims to develop a new approach to multiplex ultrasound imaging using perfluorocarbon (PFC) nanodroplets as activatable contrast agents.
methods: The paper uses two populations of PFC nanodroplets with different core boiling points, and leverages their unique temporal responses to an acoustic trigger to differentiate their unique contributions to the overall ultrasound signal.
results: The paper demonstrates the potential of this approach for multiplex ultrasound imaging, showing that the relative concentrations of the two populations of PFC nanodroplets can be accurately measured in the same imaging volume within an average error of 1.1%.Abstract
Among the various molecular imaging modalities, ultrasound imaging benefits from its real-time, nonionizing, and cost-effective nature. Despite its benefits, there is a dearth of methods to visualize two or more populations of contrast agents simultaneously, a technique known as multiplex imaging. In this paper, we present a new approach to multiplex ultrasound imaging using perfluorocarbon (PFC) nanodroplets. The nanodroplets, which undergo a liquid-to-gas phase transition in response to an acoustic trigger, act as activatable contrast agents. By using two populations of PFC nanodroplets, each with a different core boiling point, their unique temporal responses to an acoustic trigger were leveraged to differentiate their unique contributions to the overall ultrasound signal. This work characterized the dynamic responses of two PFC nanodroplets with boiling points of 28 and 56 {\deg}C. These characteristic responses were then used to demonstrate that the relative concentrations of the two populations of PFC nanodroplets could be accurately measured in the same imaging volume within an average error of 1.1%. Overall, the findings indicate the potential of this approach for multiplex ultrasound imaging, allowing for the visualization of multiple molecular targets simultaneously.
摘要
在多种分子成像方法中，超声成像具有实时、非离子和cost-effective的特点，但 simultanously visualizing two or more populations of contrast agents still remains a challenge, a technique known as multiplex imaging. In this paper, we present a new approach to multiplex ultrasound imaging using perfluorocarbon (PFC) nanodroplets. The nanodroplets, which undergo a liquid-to-gas phase transition in response to an acoustic trigger, act as activatable contrast agents. By using two populations of PFC nanodroplets, each with a different core boiling point, their unique temporal responses to an acoustic trigger were leveraged to differentiate their unique contributions to the overall ultrasound signal. This work characterized the dynamic responses of two PFC nanodroplets with boiling points of 28 and 56 ℃. These characteristic responses were then used to demonstrate that the relative concentrations of the two populations of PFC nanodroplets could be accurately measured in the same imaging volume within an average error of 1.1%. Overall, the findings indicate the potential of this approach for multiplex ultrasound imaging, allowing for the visualization of multiple molecular targets simultaneously.
Depolarized Holography with Polarization-multiplexing Metasurface
paper_authors: Seung-Woo Nam, Youngjin Kim, Dongyeon Kim, Yoonchan Jeong
for: 提高投射幕技术的表现，超越物理限制
methods: 利用受体表面，充分利用偏振度的多样性，实现无关性的投射幕显示
results: 实验和 simulations 表明，通过偏振度多样性，可以减少雾点噪声，提高图像质量Abstract
The evolution of computer-generated holography (CGH) algorithms has prompted significant improvements in the performances of holographic displays. Nonetheless, they start to encounter a limited degree of freedom in CGH optimization and physical constraints stemming from the coherent nature of holograms. To surpass the physical limitations, we consider polarization as a new degree of freedom by utilizing a novel optical platform called metasurface. Polarization-multiplexing metasurfaces enable incoherent-like behavior in holographic displays due to the mutual incoherence of orthogonal polarization states. We leverage this unique characteristic of a metasurface by integrating it into a holographic display and exploiting polarization diversity to bring an additional degree of freedom for CGH algorithms. To minimize the speckle noise while maximizing the image quality, we devise a fully differentiable optimization pipeline by taking into account the metasurface proxy model, thereby jointly optimizing spatial light modulator phase patterns and geometric parameters of metasurface nanostructures. We evaluate the metasurface-enabled depolarized holography through simulations and experiments, demonstrating its ability to reduce speckle noise and enhance image quality.
摘要
计算机生成投射法（CGH）的进化已经提高了投射显示器的性能。然而，它们开始遇到物理限制，即投射的干扰性。为超越物理限制，我们利用一种新的自由度，即极化。我们使用一种新的光学平台，即元件表面（metasurface），以实现无关的行为。元件表面的多极化能力使得投射显示器的行为更加不干扰。我们利用这一特点，并通过在投射显示器中集成元件表面，以及利用极化多样性来带来一个额外的自由度，以便CGH算法进行优化。为最小化干扰噪和最大化图像质量，我们设计了一个完全可导优化管道，包括元件表面代理模型，以联合投射显示器的灵敏度模ulator相位模式和元件表面 nanostructure 的几何参数。我们通过实验和仿真来评估元件表面启用的投射极化投射，并证明其能够减少干扰噪并提高图像质量。