Towards clinical-dose grating interferometry breast CT with fused intensity-based iterative reconstruction.

X-ray grating interferometry CT (GI-CT) is an emerging imaging modality which provides three complementary contrasts that could increase the diagnostic content of clinical breast CT: absorption, phase, and dark-field. Yet, reconstructing the three image channels under clinically compatible conditions is challenging because of severe ill-conditioning of the tomographic reconstruction problem. In this work we propose to solve this problem with a novel reconstruction algorithm that assumes a fixed relation between the absorption and the phase-contrast channel to reconstruct a single image by automatically fusing the absorption and phase channels. The results on both simulations and real data show that, enabled by the proposed algorithm, GI-CT outperforms conventional CT at a clinical dose.

An insight into fruit aroma volatilome during postharvest maturation in two popular Musa cultivars of tropics.

BACKGROUND: Banana is one of the major global horticultural fruit crops cultivated in the humid tropics and subtropics. Fruit quality and consumer acceptability of any climacteric fruit depend mainly on its postharvest aroma volatile profiles. The present study aimed to profile fruit volatiles status during postharvest storage of two banana cultivars: Kanthali (Musa sp. cv. Kanthali, Kt) and Kacha Kela (Musa sp. cv. Kacha Kela, Kk) from the ABB genome group. RESULTS: Both cultivars showed differences in the soluble sugar contents, with Kt being higher than Kk. The volatile compounds were profiled from the pulp as emitted, endogenous and glycosyl-bound forms, along with peel-endogenous and whole fruit volatiles during postharvest storage. Both cultivars showed a wide range of variations in volatile aroma pools; nevertheless, esters and aliphatic compounds were found to be the major contributors of fruit volatiles in Kt and Kk, respectively. The pulp-endogenous volatiles served as the major pool, which showed a sharp decline with a corresponding increase of emission. Many volatiles were found to be glycosylated during early postharvest storage, with de-glycosylation occurring with an increase in storage time, resulting in fruit softening and a concurrent supply of sugar bound volatiles towards emission. CONCLUSION: As a whole, the study outcome provides an overview of fruit volatilome during postharvest storage and suggests a possible inter-linking among the volatile components in the cultivars. It is plausible that the release of aroma volatiles from pulp is mediated via peel, with volatiles accumulating as peel-endogenous volatiles representing the temporary pool reservoir. © 2022 Society of Chemical Industry.

The rapid vegetation line shift in response to glacial dynamics and climate variability in Himalaya between 2000 and 2014.

Climate change is causing glaciers to retreat across much of the Himalaya, leading to a rapid shift of the vegetation cover to higher altitudes. However, the rate of vegetation shift with respect to glacier retreat, climate change, and topographic parameters is not empirically quantified. Using remote sensing measurements, we estimate (a) the rate of glacier-ice mass loss, (b) the upward vegetation line shift rate, (c) regional greening trends, and (d) a relationship between the factors influencing the greenness of the landscape and vegetation change in the Himalaya. We find that the glacier mass loss rate is 10.9 ± 1.2 Gt/yr and the mean vegetation line shifts upward in altitude by 7-28 ± 1.5 m/yr. Considering the land use/land cover change pattern, the grassland area is found to be expanding the most, particularly in the de-glaciated regions. The vegetation change is found to be controlled by soil moisture and slope of the area.

Implicit neural representations for unsupervised super-resolution and denoising of 4D flow MRI.

BACKGROUND AND OBJECTIVE: 4D flow magnetic resonance imaging provides time-resolved blood flow velocity measurements, but suffers from limitations in spatio-temporal resolution and noise. In this study, we investigated the use of sinusoidal representation networks (SIRENs) to improve denoising and super-resolution of velocity fields measured by 4D flow MRI in the thoracic aorta. METHODS: Efficient training of SIRENs in 4D was achieved by sampling voxel coordinates and enforcing the no-slip condition at the vessel wall. A set of synthetic measurements were generated from computational fluid dynamics simulations, reproducing different noise levels. The influence of SIREN architecture was systematically investigated, and the performance of our method was compared to existing approaches for 4D flow denoising and super-resolution. RESULTS: Compared to existing techniques, a SIREN with 300 neurons per layer and 20 layers achieved lower errors (up to 50% lower vector normalized root mean square error, 42% lower magnitude normalized root mean square error, and 15% lower direction error) in velocity and wall shear stress fields. Applied to real 4D flow velocity measurements in a patient-specific aortic aneurysm, our method produced denoised and super-resolved velocity fields while maintaining accurate macroscopic flow measurements. CONCLUSIONS: This study demonstrates the feasibility of using SIRENs for complex blood flow velocity representation from clinical 4D flow, with quick execution and straightforward implementation.

Data-Driven Gradient Regularization for Quasi-Newton Optimization in Iterative Grating Interferometry CT Reconstruction.

Grating interferometry CT (GI-CT) is a promising technology that could play an important role in future breast cancer imaging. Thanks to its sensitivity to refraction and small-angle scattering, GI-CT could augment the diagnostic content of conventional absorption-based CT. However, reconstructing GI-CT tomographies is a complex task because of ill problem conditioning and high noise amplitudes. It has previously been shown that combining data-driven regularization with iterative reconstruction is promising for tackling challenging inverse problems in medical imaging. In this work, we present an algorithm that allows seamless combination of data-driven regularization with quasi-Newton solvers, which can better deal with ill-conditioned problems compared to gradient descent-based optimization algorithms. Contrary to most available algorithms, our method applies regularization in the gradient domain rather than in the image domain. This comes with a crucial advantage when applied in conjunction with quasi-Newton solvers: the Hessian is approximated solely based on denoised data. We apply the proposed method, which we call GradReg, to both conventional breast CT and GI-CT and show that both significantly benefit from our approach in terms of dose efficiency. Moreover, our results suggest that thanks to its sharper gradients that carry more high spatial-frequency content, GI-CT can benefit more from GradReg compared to conventional breast CT. Crucially, GradReg can be applied to any image reconstruction task which relies on gradient-based updates.

Plasmodium falciparum J-dot localized J domain protein A8iJp modulates the chaperone activity of human HSPA8.

Plasmodium falciparum renovates the host erythrocyte to survive during intraerythrocytic development. This renovation requires many parasite proteins to unfold and move outside the parasitophorous vacuolar membrane, and chaperone-regulated protein folding becomes essential for the exported proteins to function. We report on a type-IV J domain protein (JDP), PF3D7_1401100, which we found to be processed before export and trafficked inside the lumen of parasite-derived structures known as J-dots. We found this protein to have holdase activity, as well as stimulate the ATPase and aggregation suppression activity of the human HSP70 chaperone HsHSPA8; thus, we named it "HSPA8-interacting J protein" (A8iJp). Moreover, we found a subset of HsHSPA8 to co-localize with A8iJp inside the infected human erythrocyte. Our results suggest that A8iJp modulates HsHSPA8 chaperone activity and may play an important role in host erythrocyte renovation.

Deep learning-based segmentation of multisite disease in ovarian cancer.

PURPOSE: To determine if pelvic/ovarian and omental lesions of ovarian cancer can be reliably segmented on computed tomography (CT) using fully automated deep learning-based methods. METHODS: A deep learning model for the two most common disease sites of high-grade serous ovarian cancer lesions (pelvis/ovaries and omentum) was developed and compared against the well-established "no-new-Net" framework and unrevised trainee radiologist segmentations. A total of 451 CT scans collected from four different institutions were used for training (n = 276), evaluation (n = 104) and testing (n = 71) of the methods. The performance was evaluated using the Dice similarity coefficient (DSC) and compared using a Wilcoxon test. RESULTS: Our model outperformed no-new-Net for the pelvic/ovarian lesions in cross-validation, on the evaluation and test set by a significant margin (p values being 4 × 10-7, 3 × 10-4, 4 × 10-2, respectively), and for the omental lesions on the evaluation set (p = 1 × 10-3). Our model did not perform significantly differently in segmenting pelvic/ovarian lesions (p = 0.371) compared to a trainee radiologist. On an independent test set, the model achieved a DSC performance of 71 ± 20 (mean ± standard deviation) for pelvic/ovarian and 61 ± 24 for omental lesions. CONCLUSION: Automated ovarian cancer segmentation on CT scans using deep neural networks is feasible and achieves performance close to a trainee-level radiologist for pelvic/ovarian lesions. RELEVANCE STATEMENT: Automated segmentation of ovarian cancer may be used by clinicians for CT-based volumetric assessments and researchers for building complex analysis pipelines. KEY POINTS: • The first automated approach for pelvic/ovarian and omental ovarian cancer lesion segmentation on CT images has been presented. • Automated segmentation of ovarian cancer lesions can be comparable with manual segmentation of trainee radiologists. • Careful hyperparameter tuning can provide models significantly outperforming strong state-of-the-art baselines.

Iterative phase contrast CT reconstruction with novel tomographic operator and data-driven prior.

Breast cancer remains the most prevalent malignancy in women in many countries around the world, thus calling for better imaging technologies to improve screening and diagnosis. Grating interferometry (GI)-based phase contrast X-ray CT is a promising technique which could make the transition to clinical practice and improve breast cancer diagnosis by combining the high three-dimensional resolution of conventional CT with higher soft-tissue contrast. Unfortunately though, obtaining high-quality images is challenging. Grating fabrication defects and photon starvation lead to high noise amplitudes in the measured data. Moreover, the highly ill-conditioned differential nature of the GI-CT forward operator renders the inversion from corrupted data even more cumbersome. In this paper, we propose a novel regularized iterative reconstruction algorithm with an improved tomographic operator and a powerful data-driven regularizer to tackle this challenging inverse problem. Our algorithm combines the L-BFGS optimization scheme with a data-driven prior parameterized by a deep neural network. Importantly, we propose a novel regularization strategy to ensure that the trained network is non-expansive, which is critical for the convergence and stability analysis we provide. We empirically show that the proposed method achieves high quality images, both on simulated data as well as on real measurements.

INSIDEnet: Interpretable NonexpanSIve Data-Efficient network for denoising in grating interferometry breast CT.

PURPOSE: Breast cancer is the most common malignancy in women. Unfortunately, current breast imaging techniques all suffer from certain limitations: they are either not fully three dimensional, have an insufficient resolution or low soft-tissue contrast. Grating interferometry breast computed tomography (GI-BCT) is a promising X-ray phase contrast modality that could overcome these limitations by offering high soft-tissue contrast and excellent three-dimensional resolution. To enable the transition of this technology to clinical practice, dedicated data-processing algorithms must be developed in order to effectively retrieve the signals of interest from the measured raw data. METHODS: This article proposes a novel denoising algorithm that can cope with the high-noise amplitudes and heteroscedasticity which arise in GI-BCT when operated in a low-dose regime to effectively regularize the ill-conditioned GI-BCT inverse problem. We present a data-driven algorithm called INSIDEnet, which combines different ideas such as multiscale image processing, transform-domain filtering, transform learning, and explicit orthogonality to build an Interpretable NonexpanSIve Data-Efficient network (INSIDEnet). RESULTS: We apply the method to simulated breast phantom datasets and to real data acquired on a GI-BCT prototype and show that the proposed algorithm outperforms traditional state-of-the-art filters and is competitive with deep neural networks. The strong inductive bias given by the proposed model's architecture allows to reliably train the algorithm with very limited data while providing high model interpretability, thus offering a great advantage over classical convolutional neural networks (CNNs). CONCLUSIONS: The proposed INSIDEnet is highly data-efficient, interpretable, and outperforms state-of-the-art CNNs when trained on very limited training data. We expect the proposed method to become an important tool as part of a dedicated plug-and-play GI-BCT reconstruction framework, needed to translate this promising technology to the clinics.

Reference equation for spirometry interpretation for Eastern India.

INTRODUCTION: Spirometry measurements are interpreted by comparing with reference values for healthy individuals that have been derived from multiple regression equations from earlier studies. There are only two such studies from Eastern India, both by Chatterjee et al., one each for males and females. These are however single center and approximately two decades old studies. AIMS: (1) to formulate a new regression equation for predicting FEV1 and FVC for eastern India and (2) to compare the results to the previous two studies by Chatterjee et al. MATERIALS AND METHODS: Healthy nonsmokers were recruited through health camps under the initiative of four large hospitals of Kolkata. Predicted equations were derived for FEV1, FVC and FEV1/FVC in males and females separately using multiple linear regression, which were then compared with the older equations using Bland-Altman method. RESULTS: The Bland-Altman analyses show that the mean bias for females for FVC was 0.39 L (95% limits of agreement 1.32 to -0.54 L) and for FEV1 was 0.334 L (95% limits of agreement of 1.08 to -0.41 L). For males the mean bias for FEV1 was -0.141 L, (95% limits of agreement 0.88 to -1.16 L) while that for FVC was -0.112 L (95% limits of agreement 0.80 to -1.08 L). CONCLUSION: New updated regression equations are needed for predicting reference values for spirometry interpretation. The regression equations proposed in this study may be considered appropriate for use in current practice for eastern India until further studies are available.