Yashvardhan Jain | Searcharxiv

arXiv2024

Optimizing Oscilloscope based Acquisition for Pulsed Optically Detected Magnetic Resonance Measurements

Ensembles of nitrogen vacancy (NV) defect centers in diamond have emerged as a promising platform for fundamental studies and applications in quantum sensing and quantum information processing. Here, we demonstrate the use of a digital oscilloscope for acquiring pulsed optically detected magnetic resonance (ODMR) data from an ensemble of NV centers in diamond. The oscilloscope facilitates improved signal visualization, and simplifies system debugging. We show that on-board waveform averaging in the oscilloscope enables more efficient measurements. The detection scheme, and data processing are optimized to allow fast acquisition of high quality data. The system noise, and its impact on the measurements is analyzed in detail. The data processing method is shown to effectively suppress a broad range of noise spectral components, thereby reducing the total noise in the processed data. Furthermore, the introduction of an analog low pass filter in the signal path is shown to improve the measurement by removing aliasing. The framework developed in this work can be extended to other detection techniques and material platforms for ODMR. We expect that the insights developed here will guide the design, and development of dedicated instruments for ODMR in future.

quant-ph↗

arXiv2023

Deep Learning for Vascular Segmentation and Applications in Phase Contrast Tomography Imaging

Automated blood vessel segmentation is vital for biomedical imaging, as vessel changes indicate many pathologies. Still, precise segmentation is difficult due to the complexity of vascular structures, anatomical variations across patients, the scarcity of annotated public datasets, and the quality of images. We present a thorough literature review, highlighting the state of machine learning techniques across diverse organs. Our goal is to provide a foundation on the topic and identify a robust baseline model for application to vascular segmentation in a new imaging modality, Hierarchical Phase Contrast Tomography (HiP CT). Introduced in 2020 at the European Synchrotron Radiation Facility, HiP CT enables 3D imaging of complete organs at an unprecedented resolution of ca. 20mm per voxel, with the capability for localized zooms in selected regions down to 1mm per voxel without sectioning. We have created a training dataset with double annotator validated vascular data from three kidneys imaged with HiP CT in the context of the Human Organ Atlas Project. Finally, utilising the nnU Net model, we conduct experiments to assess the models performance on both familiar and unseen samples, employing vessel specific metrics. Our results show that while segmentations yielded reasonably high scores such as clDice values ranging from 0.82 to 0.88, certain errors persisted. Large vessels that collapsed due to the lack of hydrostatic pressure (HiP CT is an ex vivo technique) were segmented poorly. Moreover, decreased connectivity in finer vessels and higher segmentation errors at vessel boundaries were observed. Such errors obstruct the understanding of the structures by interrupting vascular tree connectivity. Through our review and outputs, we aim to set a benchmark for subsequent model evaluations using various modalities, especially with the HiP CT imaging database.

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