BME frontiers最新文献

Objective and Impact Statement. Segmentation of blood vessels from two-photon microscopy (2PM) angiograms of brains has important applications in hemodynamic analysis and disease diagnosis. Here, we develop a generalizable deep learning technique for accurate 2PM vascular segmentation of sizable regions in mouse brains acquired from multiple 2PM setups. The technique is computationally efficient, thus ideal for large-scale neurovascular analysis. Introduction. Vascular segmentation from 2PM angiograms is an important first step in hemodynamic modeling of brain vasculature. Existing segmentation methods based on deep learning either lack the ability to generalize to data from different imaging systems or are computationally infeasible for large-scale angiograms. In this work, we overcome both these limitations by a method that is generalizable to various imaging systems and is able to segment large-scale angiograms. Methods. We employ a computationally efficient deep learning framework with a loss function that incorporates a balanced binary-cross-entropy loss and total variation regularization on the network's output. Its effectiveness is demonstrated on experimentally acquired in vivo angiograms from mouse brains of dimensions up to $808\times 808\times 702\mu \text{m}$. Results. To demonstrate the superior generalizability of our framework, we train on data from only one 2PM microscope and demonstrate high-quality segmentation on data from a different microscope without any network tuning. Overall, our method demonstrates 10× faster computation in terms of voxels-segmented-per-second and 3× larger depth compared to the state-of-the-art. Conclusion. Our work provides a generalizable and computationally efficient anatomical modeling framework for brain vasculature, which consists of deep learning-based vascular segmentation followed by graphing. It paves the way for future modeling and analysis of hemodynamic response at much greater scales that were inaccessible before.

Objective and Impact Statement. To study the feasibility of combined functional photoacoustic (PA) and quantitative ultrasound (US) for diagnosis of osteoporosis in vivo based on the detection of chemical and microarchitecture (BMA) information in calcaneus bone. Introduction. Clinically available X-ray or US technologies for the diagnosis of osteoporosis do not report important parameters such as chemical information and BMA. With unique advantages, including good sensitivity to molecular and metabolic properties, PA bone assessment techniques hold a great potential for clinical translation. Methods. By performing multiwavelength PA measurements, the chemical information in the human calcaneus bone, including mineral, lipid, oxygenated-hemoglobin, and deoxygenated-hemoglobin, were assessed. In parallel, by performing PA spectrum analysis, the BMA as an important bone physical property was quantified. An unpaired $t$-test and a two-way ANOVA test were conducted to compare the outcomes from the two subject groups. Results. Multiwavelength PA measurement is capable of assessing the relative contents of several chemical components in the trabecular bone in vivo, including both minerals and organic materials such as oxygenated-hemoglobin, deoxygenated-hemoglobin, and lipid, which are relevant to metabolic activities and bone health. In addition, PA measurements of BMA show good correlations ($R^2$ up to 0.65) with DEXA. Both the chemical and microarchitectural measurements from PA techniques can differentiate the two subject groups. Conclusion. The results from this initial clinical study suggest that PA techniques, by providing additional chemical and microarchitecture information relevant to bone health, may lead to accurate and early diagnosis, as well as sensitive monitoring of the treatment of osteoporosis.

Terahertz (THz) waves are electromagnetic waves with frequency in the range from 0.1 to 10 THz. THz waves have great potential in the biomedical field, especially in cancer diagnosis, because they exhibit low ionization energy and can be used to discern most biomolecules based on their spectral fingerprints. In this paper, we review the recent progress in two applications of THz waves in cancer diagnosis: imaging and spectroscopy. THz imaging is expected to help researchers and doctors attain a direct intuitive understanding of a cancerous area. THz spectroscopy is an efficient tool for component analysis of tissue samples to identify cancer biomarkers. Additionally, the advantages and disadvantages of the developed technologies for cancer diagnosis are discussed. Furthermore, auxiliary techniques that have been used to enhance the spectral signal-to-noise ratio (SNR) are also reviewed.

In an age where digitization is widespread in clinical and preclinical workflows, pathology is still predominantly practiced by microscopic evaluation of stained tissue specimens affixed on glass slides. Over the last decade, new high throughput digital scanning microscopes have ushered in the era of digital pathology that, along with recent advances in machine vision, have opened up new possibilities for Computer-Aided-Diagnoses. Despite these advances, the high infrastructural costs related to digital pathology and the perception that the digitization process is an additional and nondirectly reimbursable step have challenged its widespread adoption. Here, we discuss how emerging virtual staining technologies and machine learning can help to disrupt the standard histopathology workflow and create new avenues for the diagnostic paradigm that will benefit patients and healthcare systems alike via digital pathology.

Objective. The goal is to increase the precision of radiation delivery during radiotherapy by tracking the movements of the tumor and other surrounding normal tissues due to respiratory and other body motions. Introduction. This work presents the recent advancement of X-ray-induced radiation acoustic imaging (xRAI) technology and the evaluation of its feasibility for real-time monitoring of geometric and morphological misalignments of the X-ray field with respect to the target tissue by combining xRAI with established ultrasound (US) imaging, thereby improving radiotherapy tumor eradication and limiting treatment side effects. Methods. An integrated xRAI and B-mode US dual-modality system was established based on a clinic-ready research US platform. The performance of this dual-modality imaging system was evaluated via experiments on phantoms and ex vivo and in vivo rabbit liver models. Results. This system can alternatively switch between the xRAI and the US modes, with spatial resolutions of 1.1 mm and 0.37 mm, respectively. 300 times signal averaging was required for xRAI to reach a satisfactory signal-to-noise ratio, and a frame rate of 1.1 Hz was achieved with a clinical linear accelerator. The US imaging frame rate was 22 Hz, which is sufficient for real-time monitoring of the displacement of the target due to internal body motion. Conclusion. Our developed xRAI, in combination with US imaging, allows for mapping of the dose deposition in biological samples in vivo, in real-time, during radiotherapy. Impact Statement. The US-based image-guided radiotherapy system presented in this work holds great potential for personalized cancer treatment and better outcomes.

Objective and impact statement: This is the first longitudinal study investigating the effects of histotripsy on local tumor progression in an in vivo orthotopic, immunocompetent rat hepatocellular carcinoma (HCC) model.

Introduction: Histotripsy is the first noninvasive, nonionizing, nonthermal, mechanical ablation technique using ultrasound to generate acoustic cavitation to liquefy the target tissue into acellular debris with millimeter accuracy. Previously, histotripsy has demonstrated in vivo ablation of noncancerous liver tissue.

Methods: N1-S1 HCC tumors were generated in the livers of immunocompetent rats (n = 6, control; n = 15, treatment). Real-time ultrasound-guided histotripsy was applied to ablate either 100% tumor volume + up to 2mm margin (n = 9, complete treatment) or 50-75% tumor volume (n = 6, partial treatment) by delivering 1-2 cycle histotripsy pulses at 100 Hz PRF (pulse repetition frequency) with p - ≥30MPa using a custom 1MHz transducer. Rats were monitored weekly using MRI (magnetic resonance imaging) for 3 months or until tumors reached ~25mm.

Results: MRI revealed effective post-histotripsy reduction of tumor burden with near-complete resorption of the ablated tumor in 14/15 (93.3%) treated rats. Histopathology showed <5mm shrunken, non-tumoral, fibrous tissue at the treatment site at 3 months. Rats with increased tumor burden (3/6 control and 1 partial treatment) were euthanized early by 2-4 weeks. In 3 other controls, histology revealed fibrous tissue at original tumor site at 3 months. There was no evidence of histotripsy-induced off-target tissue injury.

Conclusion: Complete and partial histotripsy ablation resulted in effective tumor removal for 14/15 rats, with no evidence of local tumor progression or recurrence.