Recent Advances in Ultrasound and Photoacoustic Analysis for Thyroid Cancer Diagnosis

Park, Byullee; Kim, Jeesu; Kim, Chulhong

doi:10.1002/apxr.202200070

Cited by 8 publications

(7 citation statements)

References 103 publications

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“… 11 – 20 This integration enhances the ability to capture subtle pathological signs and improves diagnostic accuracy. 21 – 23 As a hybrid optical and acoustical imaging modality, PA imaging provides unique photochemical contrast capabilities at previously uncharted depths within biological tissue, 24 – 29 surpassing the capabilities of other optical imaging methods. 30 – 37 It mirrors the US imaging process 38 – 42 but utilizes pulsed laser excitation to induce the PA effect in tissue chromophores (e.g., hemoglobin, melanin, lipid, and collagen).…”

Section: Introductionmentioning

confidence: 99%

Video-rate endocavity photoacoustic/harmonic ultrasound imaging with miniaturized light delivery

Oh,

Kim,

Sung

et al. 2024

J. Biomed. Opt.

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. Significance Endocavity ultrasound (US) imaging is a frequently employed diagnostic technique in gynecology and urology for the assessment of male and female genital diseases that present challenges for conventional transabdominal imaging. The integration of photoacoustic (PA) imaging with clinical US imaging has displayed promising outcomes in clinical research. Nonetheless, its application has been constrained due to size limitations, restricting it to spatially confined locations such as vaginal or rectal canals. Aim This study presents the development of a video-rate (20 Hz) endocavity PA/harmonic US imaging (EPAUSI) system. Approach The approach incorporates a commercially available endocavity US probe with a miniaturized laser delivery unit, comprised of a single large-core fiber and a line beamshaping engineered diffuser. The system facilitates real-time image display and subsequent processing, including angular energy density correction and spectral unmixing, in offline mode. Results The spatial resolutions of the concurrently acquired PA and harmonic US images were measured at and in the radial direction, respectively, and 1.22 deg and 1.50 deg in the angular direction, respectively. Furthermore, the system demonstrated its capability in multispectral PA imaging by successfully distinguishing two clinical dyes in a tissue-mimicking phantom. Its rapid temporal resolution enabled the capture of kinetic dye perfusion into an ex vivo porcine ovary through the depth of porcine uterine tissue. EPAUSI proved its clinical viability by detecting pulsating hemodynamics in the male rat’s prostate in vivo and accurately classifying human blood vessels into arteries and veins based on measurements. Conclusions Our proposed EPAUSI system holds the potential to unveil previously overlooked indicators of vascular alterations in genital cancers or endometriosis, addressing pressing requirements in the fields of gynecology and urology.

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Section: Introductionmentioning

confidence: 99%

Video-rate endocavity photoacoustic/harmonic ultrasound imaging with miniaturized light delivery

Oh,

Kim,

Sung

et al. 2024

J. Biomed. Opt.

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“… – 20 Further, the use of PAI is expanding into clinical research areas, such as thyroid and breast cancer screening, lymph node biopsy guidance, tissue examination, and melanoma staging 21 – 24 Not limited to endogenous chromophores, the high molecular sensitivity of PAI enables molecular imaging when exogenous contrast agents are administered 4 . Biodistribution and pharmacokinetics in the body can be imaged in vivo through exogenous contrast agents that generate PA signals 25 …”

Section: Introductionmentioning

confidence: 99%

Recent advances in deep-learning-enhanced photoacoustic imaging

et al. 2023

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Photoacoustic imaging (PAI), recognized as a promising biomedical imaging modality for preclinical and clinical studies, uniquely combines the advantages of optical and ultrasound imaging. Despite PAI's great potential to provide valuable biological information, its wide application has been hindered by technical limitations, such as hardware restrictions or lack of the biometric information required for image reconstruction. We first analyze the limitations of PAI and categorize them by seven key challenges: limited detection, low-dosage light delivery, inaccurate quantification, limited numerical reconstruction, tissue heterogeneity, imperfect image segmentation/classification, and others. Then, because deep learning (DL) has increasingly demonstrated its ability to overcome the physical limitations of imaging modalities, we review DL studies from the past five years that address each of the seven challenges in PAI. Finally, we discuss the promise of future research directions in DL-enhanced PAI.

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“…We have also recently developed SpatialcoGCN (https://github.com/wwYinYin/SpatialcoGCN) for accurate ST data deconvolution. However, all of these spatial deconvolution methods are limited to inferring only cell type proportions for each spot, and cannot achieve exact single-cell-to-spot mapping, hindering the discovery of spatially de ned cell states, their interaction patterns and their surrounding communities 11 . Therefore, there is an urgent need for computational methods that can achieve high-resolution scRNA-seq and spatial transcriptomics data linking to reconstruct the spatial organization of each single cell.…”

Section: Introductionmentioning

confidence: 99%

Accurate and Flexible Single Cell to Spatial Transcriptome Mapping with Celloc

Zhou,

Yin,

et al. 2023

Preprint

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Accurate mapping between single-cell RNA sequencing (scRNA-seq) and low-resolution spatial transcriptomics (ST) data compensates for both the limited spatial resolution of ST spots and the inability of scRNA-seq to preserve spatial information. Here, we developed Celloc, a deep learning non-convex optimization-based method for flexible single-cell-to-spot mapping, which enables either dissecting cell composition of each spot (regular mapping) or predicting spatial location of every cell in scRNA-seq data (greedy mapping). We benchmarked Celloc on simulated ST data where Celloc outperformed state-of-the-art methods in accuracy and robustness. Evaluations on real datasets suggested that Celloc could reconstruct the spatial pattern of cells in breast cancer, reveal spatial subclonal heterogeneity of ductal carcinoma in situ, infer spatial tumor-immune microenvironment, and signify spatial expression patterns in myocardial infarction. Together, the results suggest that Celloc can accurately reconstruct cellular spatial structures with various cell types across different histological regions.

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Recent Advances in Ultrasound and Photoacoustic Analysis for Thyroid Cancer Diagnosis

Cited by 8 publications

References 103 publications

Video-rate endocavity photoacoustic/harmonic ultrasound imaging with miniaturized light delivery

Video-rate endocavity photoacoustic/harmonic ultrasound imaging with miniaturized light delivery

Recent advances in deep-learning-enhanced photoacoustic imaging

Accurate and Flexible Single Cell to Spatial Transcriptome Mapping with Celloc

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