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Three-dimensional (3D) ultrasound imaging has contributed to our understanding of fetal developmental processes by providing rich contextual information of the inherently 3D anatomies. However, its use is limited in clinical settings, due to the high purchasing costs and limited diagnostic practicality. Freehand 2D ultrasound imaging, in contrast, is routinely used in standard obstetric exams, but inherently lacks a 3D representation of the anatomies, which limits its potential for more advanced assessment. Such full representations are challenging to recover even with external tracking devices due to internal fetal movement which is independent from the operator-led trajectory of the probe. Capitalizing on the flexibility offered by freehand 2D ultrasound acquisition, we propose ImplicitVol to reconstruct 3D volumes from non-sensor-tracked 2D ultrasound sweeps. Conventionally, reconstructions are performed on a discrete voxel grid. We, however, employ a deep neural network to represent, for the first time, the reconstructed volume as an implicit function. Specifically, ImplicitVol takes a set of 2D images as input, predicts their locations in 3D space, jointly refines the inferred locations, and learns a full volumetric reconstruction. When testing natively-acquired and volume-sampled 2D ultrasound video sequences collected from different manufacturers, the 3D volumes reconstructed by ImplicitVol show significantly better visual and semantic quality than the existing interpolation-based reconstruction approaches. The inherent continuity of implicit representation also enables ImplicitVol to reconstruct the volume to arbitrarily high resolutions. As formulated, ImplicitVol has the potential to integrate seamlessly into the clinical workflow, while providing richer information for diagnosis and evaluation of the developing brain. [Display omitted] • We propose a framework for sensorless 3D reconstruction from freehand 2D ultrasound. • External positional sensor is impractical due to uncorrelated fetal and probe motion. • Our framework uses implicit neural representation for the 2D-to-3D reconstruction. • It outperforms the baselines on natively-acquired and volume-sampled 2D scans. • Have better visual and semantic quality than existing methods (interpolation-based). [ABSTRACT FROM AUTHOR]

• We propose a CNN for predicting the 3D location of 2D fetal brain images. • The images tested can be acquired from standard or oblique planes. • Training of the CNN only requires minimal human annotation. • Our proposed model outperforms a baseline CNN for plane localization. • It is applicable to 2D freehand ultrasound images and video scanning sequences. [Display omitted] In fetal neurosonography, aligning two-dimensional (2D) ultrasound scans to their corresponding plane in the three-dimensional (3D) space remains a challenging task. In this paper, we propose a convolutional neural network that predicts the position of 2D ultrasound fetal brain scans in 3D atlas space. Instead of purely supervised learning that requires heavy annotations for each 2D scan, we train the model by sampling 2D slices from 3D fetal brain volumes, and target the model to predict the inverse of the sampling process, resembling the idea of self-supervised learning. We propose a model that takes a set of images as input, and learns to compare them in pairs. The pairwise comparison is weighted by the attention module based on its contribution to the prediction, which is learnt implicitly during training. The feature representation for each image is thus computed by incorporating the relative position information to all the other images in the set, and is later used for the final prediction. We benchmark our model on 2D slices sampled from 3D fetal brain volumes at 18–22 weeks' gestational age. Using three evaluation metrics, namely, Euclidean distance, plane angles and normalized cross correlation, which account for both the geometric and appearance discrepancy between the ground-truth and prediction, in all these metrics, our model outperforms a baseline model by as much as 23%, when the number of input images increases. We further demonstrate that our model generalizes to (i) real 2D standard transthalamic plane images, achieving comparable performance as human annotations, as well as (ii) video sequences of 2D freehand fetal brain scans. [ABSTRACT FROM AUTHOR]

We report a mid-infrared quartz-enhanced photoacoustic sensor for highly sensitive nitrous oxide (N2O) detection using a 4.56 µm quantum cascade laser (QCL) with a butterfly package type. This new type of QCL features a compact size (30 mm × 12.7 mm × 13 mm), low threshold current (< 120 mA) at room temperature, and relatively high emission power (> 100 mW), which are suitable for portable trace gas sensor development. In this work, the QCL beam is directly coupled to the QEPAS detection module by using only one focusing lens. By measuring N2O mixtures with different concentrations (120 ppb to 3.16 ppm), the developed sensor shows a good linearity (R-square value of 0.999) and a minimum detection limit of below 1 ppb. The sensor has been successfully deployed for ambient air measurement, demonstrating the applicability and promising potential of using this QCL with butterfly package for various gas sensing applications. [ABSTRACT FROM AUTHOR]

We demonstrate a method for the detection of nucleic acid using hybridization-induced suppression of the coffee ring effect. When a sessile droplet is pinned on a solid surface, evaporation induces an outward capillary flow that moves suspended particles toward the periphery of the droplet and leaves a ring-shape structure. Compared with spherical particles that are carried to the contact line and form rings, non-spherical particles tend to adhere to each other at the air–water interface where they gain enhanced resistance to the capillary flow and suppress the coffee ring effect. Here, we used suspended microspheres surface-functionalized with single-strand oligonucleotide probes that were complementary to a target DNA. The present target DNA hybridized with the oligonucleotide probe and connected with multiple microspheres, leading to the generation of non-spherical particle agglomerates that resist the capillary flow and form a more uniform deposition of particles after evaporation. Video microscopy and numerical simulation showed that the suppression is because non-spherical particle agglomerates distorted the meniscus surrounding them and exerted a long-range capillary attraction that enhanced the fluidic resistance. Eventually, the microscale hybridization events were translated into the change of coffee ring patterns at macroscale. Using coffee rings as the readout, proof-of-principle studies showed effective sensitivity at low concentrations of 10–100 nM with a wide dynamic range from 10 −5 M to 10 −8 M, and high specificity that can distinguish the sequence with a single mismatched nucleotide. Owing to the simplicity of the operation and visual readout without the need of a special detector, our approach demonstrates immense potential for the inexpensive and convenient detection of nucleic acid in at resources-limited settings. [ABSTRACT FROM AUTHOR]