Vaginal Obstruction after Excision of a Transverse Vaginal Septum: A Rare Case Report

Sharma, Reena; Dogra, Poojan; Srishti, Srishti

doi:10.21276/aimdr.2018.4.1.og1

Cited by 2 publications

(3 citation statements)

References 9 publications

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“…The use of PAI for implant monitoring has been previously explored in studies such as by Lee et al [59] (Figure 7), who achieved reasonable ability to distinguish titanium implant covered by bone or meat, at depths relevant for dentistry applications. However, the depth penetration limit and noticeable optical attenuation makes it increasingly difficult to image the targets that are located deeper than 10-20 mm, limiting the clinical application of PAI to targets near the surface [60]. At the same time, PAI resolution is dependent on the depth based on "factor of 200 rule of thumb", with resolution being 1/200th of the depth [61,62].…”

Section: Photoacoustic Imagingmentioning

confidence: 99%

“…Polymers 2021, 13, x FOR PEER REVIEW 11 of 2 the depth penetration limit and noticeable optical attenuation makes it increasingly diffi cult to image the targets that are located deeper than 10-20 mm, limiting the clinical ap plication of PAI to targets near the surface [60]. At the same time, PAI resolution is de pendent on the depth based on "factor of 200 rule of thumb", with resolution being 1/200th of the depth [61,62].…”

Section: Photoacoustic Imagingmentioning

confidence: 99%

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In Vivo Imaging of Biodegradable Implants and Related Tissue Biomarkers

et al. 2021

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Non-invasive longitudinal imaging of osseointegration of bone implants is essential to ensure a comprehensive, physical and biochemical understanding of the processes related to a successful implant integration and its long-term clinical outcome. This study critically reviews the present imaging techniques that may play a role to assess the initial stability, bone quality and quantity, associated tissue remodelling dependent on implanted material, implantation site (surrounding tissues and placement depth), and biomarkers that may be targeted. An updated list of biodegradable implant materials that have been reported in the literature, from metal, polymer and ceramic categories, is provided with reference to the use of specific imaging modalities (computed tomography, positron emission tomography, ultrasound, photoacoustic and magnetic resonance imaging) suitable for longitudinal and non-invasive imaging in humans. The advantages and disadvantages of the single imaging modality are discussed with a special focus on preclinical imaging for biodegradable implant research. Indeed, the investigation of a new implant commonly requires histological examination, which is invasive and does not allow longitudinal studies, thus requiring a large number of animals for preclinical testing. For this reason, an update of the multimodal and multi-parametric imaging capabilities will be here presented with a specific focus on modern biomaterial research.

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Section: Photoacoustic Imagingmentioning

confidence: 99%

Section: Photoacoustic Imagingmentioning

confidence: 99%

In Vivo Imaging of Biodegradable Implants and Related Tissue Biomarkers

et al. 2021

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“…Patrick et al demonstrated non-invasive whole brain imaging of tau targeting PBB5 probes using volume multispectral photoacoustic tomography (vMSOT) in a 130 m resolution 4-replicate tau P301L model [24]. PAI combines high optical contrast with high ultrasonic resolution, but its maximum detection depth in breast tissue (about 106.4 mm [25]) remains a limitation for wider applications.…”

Section: Introductionmentioning

confidence: 99%

3D Ultrasonic Brain Imaging with Deep Learning Based on Fully Convolutional Networks

Ren,

Wang,

Liu

et al. 2023

Sensors

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Compared to magnetic resonance imaging (MRI) and X-ray computed tomography (CT), ultrasound imaging is safer, faster, and more widely applicable. However, the use of conventional ultrasound in transcranial brain imaging for adults is predominantly hindered by the high acoustic impedance contrast between the skull and soft tissue. This study introduces a 3D AI algorithm, Brain Imaging Full Convolution Network (BIFCN), combining waveform modeling and deep learning for precise brain ultrasound reconstruction. We constructed a network comprising one input layer, four convolution layers, and one pooling layer to train our algorithm. In the simulation experiment, the Pearson correlation coefficient between the reconstructed and true images was exceptionally high. In the laboratory, the results showed a slightly lower but still impressive coincidence degree for 3D reconstruction, with pure water serving as the initial model and no prior information required. The 3D network can be trained in 8 h, and 10 samples can be reconstructed in just 12.67 s. The proposed 3D BIFCN algorithm provides a highly accurate and efficient solution for mapping wavefield frequency domain data to 3D brain models, enabling fast and precise brain tissue imaging. Moreover, the frequency shift phenomenon of blood may become a hallmark of BIFCN learning, offering valuable quantitative information for whole-brain blood imaging.

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Vaginal Obstruction after Excision of a Transverse Vaginal Septum: A Rare Case Report

Cited by 2 publications

References 9 publications

In Vivo Imaging of Biodegradable Implants and Related Tissue Biomarkers

In Vivo Imaging of Biodegradable Implants and Related Tissue Biomarkers

3D Ultrasonic Brain Imaging with Deep Learning Based on Fully Convolutional Networks

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