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1.
Nat Commun ; 12(1): 5915, 2021 Oct 08.
Artigo em Inglês | MEDLINE | ID: mdl-34625565

RESUMO

Automatic medical image segmentation plays a critical role in scientific research and medical care. Existing high-performance deep learning methods typically rely on large training datasets with high-quality manual annotations, which are difficult to obtain in many clinical applications. Here, we introduce Annotation-effIcient Deep lEarning (AIDE), an open-source framework to handle imperfect training datasets. Methodological analyses and empirical evaluations are conducted, and we demonstrate that AIDE surpasses conventional fully-supervised models by presenting better performance on open datasets possessing scarce or noisy annotations. We further test AIDE in a real-life case study for breast tumor segmentation. Three datasets containing 11,852 breast images from three medical centers are employed, and AIDE, utilizing 10% training annotations, consistently produces segmentation maps comparable to those generated by fully-supervised counterparts or provided by independent radiologists. The 10-fold enhanced efficiency in utilizing expert labels has the potential to promote a wide range of biomedical applications.

2.
Adv Mater ; : e2102950, 2021 Oct 07.
Artigo em Inglês | MEDLINE | ID: mdl-34617645

RESUMO

Lanthanide-based NIR-IIb nanoprobes are ideal for in vivo imaging. However, existing NIR-IIb nanoprobes often suffer from low tumor-targeting specificity, limiting their widespread use. Here the application of bioorthogonal nanoprobes with high tumor-targeting specificity for in vivo NIR-IIb luminescence imaging and magnetic resonance imaging (MRI) is reported. These dual-modality nanoprobes can enhance NIR-IIb emission by 20-fold and MRI signal by twofold, compared with non-bioorthogonal nanoprobes in murine subcutaneous tumors. Moreover, these bioorthogonal probes enable orthotopic brain tumor imaging. Implementation of bio-orthogonal chemistry significantly reduces the nanoprobe dose and hence cytotoxicity, providing a paradigm for real-time in vivo visualization of tumors.

3.
J Neurosci Methods ; 365: 109383, 2021 Oct 08.
Artigo em Inglês | MEDLINE | ID: mdl-34634283

RESUMO

BACKGROUND: Single-element focused transducers applied in blood-brain barrier (BBB) disruption experiments to optimize intravascular therapies in CNS diseases have the advantage of low cost and portability. Most of the in vivo studies on non-human primates report the use of single-element transducers with an annular spherical shape and a central frequency of 500 kHz. High-frequency ultrasound has smaller focal area and less standing-wave effect but lower transcranial penetration efficiency. Our study reports the feasibility and safety concerns of BBB opening by single-element spherical transducers with central frequencies of 300, 650 and 800 kHz on two rhesus macaques. METHODS: Pulsed ultrasound exposure (3-minute duration, 0.5-1% duty cycle) combined with microbubble injection (SonoVue, 0.2uL/g) was used to disrupt the BBB of the monkeys under the magnetic resonance imaging (MRI) guidance. Gadolinium contrast-enhanced MRI was used to confirm and evaluate the BBB opening after sonication. T2-weighted fast spin echo and T2 * -weighted gradient echo sequences were used to check the post-sonication complications, such as edema and micro-bleeding. RESULTS: Contrast enhancement was found on the post-sonication T1 weighted images for all experiments, showing that the BBB was successfully opened under all the three frequencies on both monkeys. The enhanced area was largest at the lowest frequency. No obvious hypo-intensity or hyper-intensity was observed on either the T2 * weighted gradient echo images or T2-weighted fast-spin echo images, implying the safety of the opening procedure. However, signal enhancement was also observed in the subarachnoid space of the sulci for all frequencies, indicating that the BBB was also disrupted in the propagation path outside the focal area. CONCLUSIONS: The feasibility of BBB opening with single-element transducer under frequencies ranging from 300 kHz to 800 kHz was confirmed by experiments in two non-human primates in vivo. Further investigation into the off-target effects and transducer configurations is needed for safety optimization.

4.
MAGMA ; 2021 Sep 15.
Artigo em Inglês | MEDLINE | ID: mdl-34524564

RESUMO

OBJECTIVE: To propose a fully automated algorithm, which is implemented to segment subcutaneous adipose tissue (SAT) and internal adipose tissue (IAT) from the total adipose tissue for whole-body fat distribution analysis using proton density fat fraction (PDFF) magnetic resonance images. MATERIALS AND METHODS: Adipose tissue segmentation was implemented using the U-Net deep neural network model. All datasets were collected using a 3.0 T magnetic resonance imaging (MRI) scanner for whole-body scan of 20 volunteers covering from neck to knee with about 160 images for each volunteer. PDFF images were reconstructed based on chemical-shift-encoded fat-water imaging. After selecting the representative PDFF images (total 906 images), the manual labeling of the SAT area was used for model training (504 images), validation (168 images), and testing (234 images). RESULTS: The automatic segmentation model was validated through three indices using the validation and test sets. The dice similarity coefficient, precision rate, and recall rate were 0.976 ± 0.048, 0.978 ± 0.048, and 0.978 ± 0.050, respectively, in both validation and test sets. CONCLUSION: The proposed algorithm can reliably and automatically segment SAT and IAT from whole-body MRI PDFF images. The proposed method provides a simple and automatic tool for whole-body fat distribution analysis to explore the relationship between fat deposition and metabolic-related chronic diseases.

5.
Radiology ; : 203281, 2021 Sep 14.
Artigo em Inglês | MEDLINE | ID: mdl-34519578

RESUMO

Background The biologic meaning of prognostic radiomics phenotypes remains poorly understood, hampered in part by lack of multicenter reproducible evidence. Purpose To uncover the biologic meaning of individual prognostic radiomics phenotypes in glioblastomas using paired MRI and RNA sequencing data and to validate the reproducibility of the identified radiogenomics linkages externally. Materials and Methods This retrospective multicenter study included four data sets gathered between January 2015 and December 2016. From a radiomics analysis set, a 13-feature radiomics signature was built using preoperative MRI for overall survival prediction. Using a radiogenomics training set with both MRI and RNA sequencing, biologic pathways were enriched and correlated with each of the 13 radiomics phenotypes. Radiomics-correlated key genes were identified to derive a prognostic radiomics gene expression (RadGene) score. The reproducibility of identified pathways and genes was validated with an external test set and a public data set (The Cancer Genome Atlas [TCGA]). A log-rank test was performed to assess prognostic significance. Results A total of 435 patients (mean age, 55 years ± 15 [standard deviation]; 263 men) were enrolled. The radiomics signature was associated with overall survival (hazard ratio [HR], 3.68; 95% CI: 2.08, 6.52; P < .001) in the radiomics validation subset. Four types of prognostic radiomics phenotypes were correlated with distinct pathways: immune, proliferative, treatment responsive, and cellular functions (false-discovery rate < 0.10). Thirty radiomics-correlated genes were identified. The prognostic significance of the RadGene score was confirmed in an external test set (HR, 2.02; 95% CI: 1.19, 3.41; P = .01) and a TCGA test set (HR, 1.43; 95% CI: 1.001, 2.04; P = .048). The radiomics-associated pathways and key genes can be replicated in an external test set. Conclusion Individual radiomics phenotypes on MRI scans predictive of overall survival were driven by distinct key pathways involved in immune regulation, tumor proliferation, treatment responses, and cellular functions in glioblastoma, which could be reproduced externally. © RSNA, 2021 Online supplemental material is available for this article.

6.
IEEE Trans Biomed Eng ; PP2021 Sep 20.
Artigo em Inglês | MEDLINE | ID: mdl-34543187

RESUMO

A novel photoacoustic imaging system based on a semi-ring transducer array is proposed to imageperipheralbloodvessels. The system's penetration depth is deep (~15 mm) with high spatial (~200 m) and temporal resolution. In a clinical study, volumetric photoacoustic data of limbs were obtained within the 50s (for a FOV of 15 cm4 cm) with the volunteers in the standing and sitting posture. Compared to the previous studies, our system has many advantages, including (1) Larger field of view; (2) Finer elevational and in-plane resolutions; (3) Enhanced 3D visualization of peripheralvascular networks; (4) Compact size and better portability. The 3D visualization and cross-sectional images of five healthy volunteers clearly show the vascular network and the system's ability to image submillimeter blood vessels. This high-resolution PA system has great potential for imaging human periphery vasculatures noninvasively in clinical research.

8.
Artigo em Inglês | MEDLINE | ID: mdl-34437062

RESUMO

High definition intravascular ultrasound (HD-IVUS) utilizing more than 80 MHz frequency to assess atherosclerotic plaque, can theoretically achieve an axial resolution of less than 20 µm. However, the blood is a high-attenuation source at high frequency, which would affect the imaging quality. There has been no research evaluating the blood-induced influence on the HD-IVUS imaging. And whether a temporary removal of blood is needed for HD-IVUS is unknown. In this study, an ultrahigh-frequency (100 MHz) ultrasound transducer was developed to evaluate the blood induced attenuation for HD-IVUS imaging. A series of tungsten-wire phantom images in saline and blood at varying hematocrits were obtained. The images showed that blood did influence the ultra-high frequency imaging quality greatly. The signal-to-noise (SNR) ratio decrease by 71.7% in porcine whole blood compared to that in saline at the same depth of 2.3 mm. Moreover, the potential flushing schemes for HD-IVUS were studied in varying hematocrits. Three flushing agents commonly used in IV-OCT were investigated, including iohexol, mannitol, and dextran 5% and saline as control group. The attenuation of blood in varying hematocrits/flushing agents were measured from 90 to 110 MHz. The result indicated dextran 5% was a suitable flushing agent for HD-IVUS due to its less signal attenuation compared to others.

9.
NMR Biomed ; : e4598, 2021 Aug 15.
Artigo em Inglês | MEDLINE | ID: mdl-34396597

RESUMO

Magnetic resonance acoustic radiation force imaging (MR-ARFI) is a promising tool for transcranial neurosurgery planning and monitoring. However, the ultrasound dose during ARFI is quite high due to the high intensity required and the repetitive ultrasound sonication. To reduce the ultrasound deposition and prevent unwanted neurological effects, undersampling in k-space data acquisition is adopted in the current study. Three reconstruction methods, keyhole, k-space hybrid and temporal differences (TED) compressed sensing, the latter two of which were initially proposed for MR thermometry, were applied to the in vivo transcranial focus localization based on MR-ARFI data in a retrospective way. The accuracies of the three methods were compared with the results from the fully sampled data as reference. The results showed that the keyhole method tended to smooth the displacement map and underestimate the peak displacement. The K-space hybrid method was better at recovering the displacement map and was robust to the undersampling pattern, while the TED method was more time efficient under a higher image resolution. For an image of a lower resolution, the K-space hybrid and TED methods were comparable in terms of accuracy when a high undersampling rate was applied. The results reported here facilitate the choice of appropriate undersampled reconstruction methods in transcranial focal localization based on MR-ARFI.

10.
J Magn Reson ; 330: 107042, 2021 Sep.
Artigo em Inglês | MEDLINE | ID: mdl-34333244

RESUMO

The fractional-order Bloch equations have been shown to describe a wider range of experimental situations involving heterogeneous, porous, or composite materials. This paper introduces a novel dictionary of quantitative MR fingerprinting generated by signal evolution model with fractional-order Bloch equations to describe magnetic resonance (MR) relaxation. Here, the fractional-order relaxation models are implemented into Bloch equations through phase transitions using EPG simulation. In the phantom experiments, the fractional-order analysis showed smaller root mean squared error (T1: RMSE = 5.21%, T2: RMSE=3.75%) using the proposed method compared to using conventional method. Among the in vivo experiments of human brains, the estimated T1 and T2 values (mean ± SD) were 843 ± 46.3 ms and 70 ± 4.7 ms in white matter, 1323 ± 28.5 ms and 95 ± 3.8 ms in gray matter. So the proposed method can provide well extensions of current MR fingerprinting and has shown potential to apply into the phantom experiments and the in vivo applications to approach the standard methods for quantitative imaging.

11.
IEEE Trans Med Imaging ; PP2021 Jul 12.
Artigo em Inglês | MEDLINE | ID: mdl-34252024

RESUMO

Deep learning methods have achieved attractive performance in dynamic MR cine imaging. However, most of these methods are driven only by the sparse prior of MR images, while the important low-rank (LR) prior of dynamic MR cine images is not explored, which may limit further improvements in dynamic MR reconstruction. In this paper, a learned singular value thresholding (Learned-SVT) operator is proposed to explore low-rank priors in dynamic MR imaging to obtain improved reconstruction results. In particular, we put forward a model-based unrolling sparse and low-rank network for dynamic MR imaging, dubbed as SLR-Net. SLR-Net is defined over a deep network flow graph, which is unrolled from the iterative procedures in the iterative shrinkage-thresholding algorithm (ISTA) for optimizing a sparse and LR-based dynamic MRI model. Experimental results on a single-coil scenario show that the proposed SLR-Net can further improve the state-of-the-art compressed sensing (CS) methods and sparsity-driven deep learning-based methods with strong robustness to different undersampling patterns, both qualitatively and quantitatively. Besides, SLR-Net has been extended to a multi-coil scenario, and achieved excellent reconstruction results compared with a sparsity-driven multi-coil deep learning-based method under a high acceleration. Prospective reconstruction results on an open real-time dataset further demonstrate the capability and flexibility of the proposed method on real-time scenarios.

12.
Proc Natl Acad Sci U S A ; 118(28)2021 Jul 13.
Artigo em Inglês | MEDLINE | ID: mdl-34244441

RESUMO

Ultrasonic hearing and vocalization are the physiological mechanisms controlling echolocation used in hunting and navigation by microbats and bottleneck dolphins and for social communication by mice and rats. The molecular and cellular basis for ultrasonic hearing is as yet unknown. Here, we show that knockout of the mechanosensitive ion channel PIEZO2 in cochlea disrupts ultrasonic- but not low-frequency hearing in mice, as shown by audiometry and acoustically associative freezing behavior. Deletion of Piezo2 in outer hair cells (OHCs) specifically abolishes associative learning in mice during hearing exposure at ultrasonic frequencies. Ex vivo cochlear Ca2+ imaging has revealed that ultrasonic transduction requires both PIEZO2 and the hair-cell mechanotransduction channel. The present study demonstrates that OHCs serve as effector cells, combining with PIEZO2 as an essential molecule for ultrasonic hearing in mice.

13.
Adv Mater ; 33(32): e2101717, 2021 Aug.
Artigo em Inglês | MEDLINE | ID: mdl-34219296

RESUMO

Most contemporary X-ray detectors adopt device structures with non/low-gain energy conversion, such that a fairly thick X-ray photoconductor or scintillator is required to generate sufficient X-ray-induced charges, and thus numerous merits for thin devices, such as mechanical flexibility and high spatial resolution, have to be compromised. This dilemma is overcome by adopting a new high-gain device concept of a heterojunction X-ray phototransistor. In contrast to conventional detectors, X-ray phototransistors allow both electrical gating and photodoping for effective carrier-density modulation, leading to high photoconductive gain and low noise. As a result, ultrahigh sensitivities of over 105  µC Gyair -1  cm-2 with low detection limit are achieved by just using an ≈50 nm thin photoconductor. The employment of ultrathin photoconductors also endows the detectors with superior flexibility and high imaging resolution. This concept offers great promise in realizing well-balanced detection performance, mechanical flexibility, integration, and cost for next-generation X-ray detectors.

14.
Magn Reson Med ; 2021 Jul 17.
Artigo em Inglês | MEDLINE | ID: mdl-34272757

RESUMO

PURPOSE: This work was aimed at designing a deep-learning-based approach for MR image phase unwrapping to improve the robustness and efficiency of traditional methods. METHODS: A deep learning network called PHU-NET was designed for MR phase unwrapping. In this network, a novel training data generation method was proposed to simulate the wrapping patterns in MR phase images. The wrapping boundary and wrapping counts were explicitly estimated and used for network training. The proposed method was quantitatively evaluated and compared to other methods using a number of simulated datasets with varying signal-to-noise ratio (SNR) and MR phase images from various parts of the human body. RESULTS: The results showed that our method performed better in the simulated data even under an extremely low SNR. The proposed method had less residual wrapping in the images from various parts of human body and worked well in the presence of severe anatomical discontinuity. Our method was also advantageous in terms of computational efficiency compared to the traditional methods. CONCLUSION: This work proposed a robust and computationally efficient MR phase unwrapping method based on a deep learning network, which has promising performance in applications using MR phase information.

15.
Artigo em Inglês | MEDLINE | ID: mdl-34280096

RESUMO

The use of acoustic tweezers for precise manipulation of microparticles in the aqueous environment is essential and challenging for biomechanical applications in vivo. A 3D acoustic tweezer is developed in this study for three-dimensional manipulation by using a 2D phased array consisting of 256 elements operating at 1.04 MHz. The emission phases of each element are iteratively determined by a backpropagation algorithm to generate multiple acoustic traps. Different traps are multiplexed in time, thus forming synthesized acoustic fields. We demonstrate the three-dimensional levitation and translation of positive acoustic contrast particles, a major class of bioparticles, in water by different acoustic traps, and compare the positional deviation along the intended path via experimentally measured trajectories. Improved manipulating stability was achieved by multiplexed acoustic traps. The 3D acoustic tweezers proposed in this study provide a versatile approach of contactless bioparticle trapping and translation, paving the way towards future application of nanodroplet and microbubble manipulations.

16.
Med Phys ; 48(9): 5259-5271, 2021 Sep.
Artigo em Inglês | MEDLINE | ID: mdl-34252216

RESUMO

PURPOSE: Clinically, single radiotracer positron emission tomography (PET) imaging is a commonly used examination method; however, since each radioactive tracer reflects the information of only one kind of cell, it easily causes false negatives or false positives in disease diagnosis. Therefore, reasonably combining two or more radiotracers is recommended to improve the accuracy of diagnosis and the sensitivity and specificity of the disease when conditions permit. METHODS: This paper proposes incorporating 18 F-fluorodeoxyglucose (FDG) as a higher-quality PET image to guide the reconstruction of other lower-count 11 C-methionine (MET) PET datasets to compensate for the lower image quality by a popular kernel algorithm. Specifically, the FDG prior is needed to extract kernel features, and these features were used to build a kernel matrix using a k-nearest-neighbor (kNN) search for MET image reconstruction. We created a 2-D brain phantom to validate the proposed method by simulating sinogram data containing Poisson random noise and quantitatively compared the performance of the proposed FDG-guided kernelized expectation maximization (KEM) method with the performance of Gaussian and non-local means (NLM) smoothed maximum likelihood expectation maximization (MLEM), MR-guided KEM, and multi-guided-S KEM algorithms. Mismatch experiments between FDG/MR and MET data were also carried out to investigate the outcomes of possible clinical situations. RESULTS: In the simulation study, the proposed method outperformed the other algorithms by at least 3.11% in the signal-to-noise ratio (SNR) and 0.68% in the contrast recovery coefficient (CRC), and it reduced the mean absolute error (MAE) by 8.07%. Regarding the tumor in the reconstructed image, the proposed method contained more pathological information. Furthermore, the proposed method was still superior to the MR-guided KEM method in the mismatch experiments. CONCLUSIONS: The proposed FDG-guided KEM algorithm can effectively utilize and compensate for the tissue metabolism information obtained from dual-tracer PET to maximize the advantages of PET imaging.


Assuntos
Fluordesoxiglucose F18 , Processamento de Imagem Assistida por Computador , Algoritmos , Imagens de Fantasmas , Tomografia por Emissão de Pósitrons , Razão Sinal-Ruído
18.
Phys Med Biol ; 66(15)2021 Jul 26.
Artigo em Inglês | MEDLINE | ID: mdl-34192685

RESUMO

We investigate the reconstruction of low-count positron emission tomography (PET) projection, which is an important, but challenging, task. Using the texture feature extraction method of radiomics, i.e. the gray-level co-occurrence matrix (GLCM), texture features can be extracted from magnetic resonance imaging images with high-spatial resolution. In this work, we propose a kernel reconstruction method combining autocorrelation texture features derived from the GLCM. The new kernel function includes the correlations of both the intensity and texture features from the prior image. By regarding the GLCM as a discrete approximation of a probability density function, the asymptotically gray-level-invariant autocorrelation texture feature is generated, which can maintain the accuracy of texture features extracted from small image regions by reducing the number of quantized image gray levels. A computer simulation shows that the proposed method can effectively reduce the noise in the reconstructed image compared to the maximum likelihood expectation maximum method and improve the image quality and tumor region accuracy compared to the original kernel method for low-count PET reconstruction. A simulation study on clinical patient images also shows that the proposed method can improve the whole image quality and that the reconstruction of a high-uptake lesion is more accurate than that achieved by the original kernel method.

19.
ACS Nano ; 15(6): 10010-10024, 2021 06 22.
Artigo em Inglês | MEDLINE | ID: mdl-34060821

RESUMO

Tumor-associated macrophages (TAMs) play a crucial part in cancer evolution. Dynamic imaging of TAMs is of great significance for treatment outcome evaluation and precision tumor therapy. Currently, most fluorescence nanoprobes tend to accumulate in the liver and are difficult to metabolize, which leads to strong background signals and inadequate imaging quality of TAMs nearby the liver such as pancreatic cancer. Herein, we aim to develop metabolizable dextran-indocyanine green (DN-ICG) nanoprobes in the second near-infrared window (NIR-II, 1 000-1 700 nm) for dynamic imaging of TAMs in pancreatic cancer. Compared to free ICG, the NIR-II fluorescence intensity of DN-ICG nanoprobes increased by 279% with significantly improved stability. We demonstrated that DN-ICG nanoprobes could specifically target TAMs through the interaction of dextran with specific ICAM-3-grabbing nonintegrin related 1 (SIGN-R1), which were highly expressed in TAMs. Subsequently, DN-ICG nanoprobes gradually metabolized in the liver yet remained in pancreatic tumor stroma in mouse models, achieving a high signal-to-background ratio (SBR = 7) in deep tissue (∼0.5 cm) NIR-II imaging of TAMs. Moreover, DN-ICG nanoprobes could detect dynamic changes of TAMs induced by low-dose radiotherapy and zoledronic acid. Therefore, the highly biocompatible and biodegradable DN-ICG nanoprobes harbor great potential for precision therapy in pancreatic cancer.


Assuntos
Neoplasias Pancreáticas , Macrófagos Associados a Tumor , Animais , Verde de Indocianina , Camundongos , Imagem Óptica , Neoplasias Pancreáticas/diagnóstico por imagem , Espectroscopia de Luz Próxima ao Infravermelho
20.
Phys Med Biol ; 66(14)2021 Jul 16.
Artigo em Inglês | MEDLINE | ID: mdl-34077922

RESUMO

To reduce overall patient radiation exposure in some clinical scenarios (since cancer patients need frequent follow-ups), noncontrast CT is not used in some institutions. However, although less desirable, noncontrast CT could provide additional important information. In this article, we propose a deep subtraction residual network based on adjacency content transfer to reconstruct noncontrast CT from contrast CT and maintain image quality comparable to that of a CT scan originally acquired without contrast. To address the slight structural dissimilarity of the paired CT images (noncontrast CT and contrast CT) due to involuntary physiological motion, we introduce a contrastive loss network derived from the adjacency content-transfer strategy. We evaluate the results of various similarity metrics (MSE, SSIM, NRMSE, PSNR, MAE) and the fitting curve (HU distribution) of the output mapping to estimate the reconstruction performance of the algorithm. To build the model, we randomly select a total of 15,405 CT paired images (noncontrast CT and contrast-enhanced CT) for training and 10,270 CT paired images for testing. The proposed algorithm preserves the robust structures from the contrast-enhanced CT scans and learns the noncontrast attenuation pattern from the noncontrast CT scans. During the evaluation, the deep subtraction residual network achieves higher MSE, MAE, NRMSE, and PSNR scores (by 30%) than those of the baseline models (BEGAN, CycleGAN, Pixel2Pixel) and better simulates the HU curve of noncontrast CT attenuation. After validation based on an analysis of the experimental results, we can report that the noncontrast CT images reconstructed by our proposed algorithm not only preserve the high-quality structures from the contrast-enhanced CT images, but also mimic the CT attenuation of the originally acquired noncontrast CT images.

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