2025-10-01 05:46:23
:::info Authors:
(1) Jiandong Wang, Shenzhen Xilaiheng Medical Electronics, (HORRON), China and Centre for Medical Engineering and Technology, University of Dundee, DD1 4HN, UK ([email protected]);
(2) Alessandro Perelli, Centre for Medical Engineering and Technology, University of Dundee, DD1 4HN, UK ([email protected]).
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Dual energy X-ray Computed Tomography (DECT) enables to automatically decompose materials in clinical images without the manual segmentation using the dependency of the Xray linear attenuation with energy. In this work we propose a deep learning procedure called End-to-End Material Decomposition (E2E-DEcomp) for quantitative material decomposition which directly convert the CT projection data into material images. The algorithm is based on incorporating the knowledge of the spectral model DECT system into the deep learning training loss and combining a data-learned prior in the material image domain. Furthermore, the training does not require any energy-based images in the dataset but rather only sinogram and material images. We show the effectiveness of the proposed direct E2E-DEcomp method on the AAPM spectral CT dataset Sidky and Pan [2023] compared with state of the art supervised deep learning networks.
Dual-energy computed tomography (DECT) is one spectral CT technology which is based on the deployment of two X-ray sources at different energies which can potentially allow to discriminate different materials in a specimen or to reconstruct virtual mono-energetic images which is of utmost interest in clinical imaging applications and industrial non-destructive testing Mendonça et al. [2013]. The dependency of the attenuation coefficient of different materials respect to the X-ray energy can be leveraged in the DECT material decomposition procedure whose aim is to estimate each pixel’s value as a linear combination of two different basis materials Johnson et al. [2007].
\ Different approaches have been developed to obtain material images: the image domain techniques are based on first reconstructing independently the energy dependent attenuation in each pixel Maaß et al. [2009]. The majority of the proposed networks such as U-Net Nadkarni et al. [2022] and generative adversarial network (GAN) Shi et al. [2021] are trained with a supervised learning approach which requires the pair of energy reconstructed Dual-energy computed tomography (DECT) images and basis material segmented images in the dataset. However these methods do not account from the beam-hardening effect, caused by the poly-energetic nature of the X-ray source. Moreover this approach leads to propagate the estimation errors from the reconstruction to the subsequent material decomposition.
\ In order to account for the beam-hardening effect, one-step methods directly estimate basis materials images from Cai et al. [2013] measurements projects by leveraging a model-based optimization function to minimize. However because of the highly non-linear due to the energies X-ray source coupling, this leads to minimize non-convex cost functions which require high computational cost Long and Fessler [2014].
\ An alternative approach is based on decomposing the high and low energy sinogram into two independent measurements which correspond to a single basis material. Different approximations of the decomposition function that convert the dual energy sinograms into materials independent sinograms have been proposed in Alvarez and Macovski [1976]. Afterwards each material sinogram is converted into the image domain using model-based optimization methods Mechlem et al. [2018] with spatial regularization.
\ Recently, other works have exploited the paradigm of combining deep learning and the knowledge of the physics of the DECT model within the optimisation problem. The one-step material decomposition is implemented using supervised unrolling algorithms in Eguizabal et al. [2022], Perelli and Andersen [2021] or using the Noise2Inverse framework which uses pair of sub-sampled noisy sinograms and training dataset Fang et al. [2021]. One limitation of these methods is the computational cost of the iterative solver which hinders the usage in applications which require strict time constraints.
\ In this work, we propose a new optimization framework called End-to-End Material Decomposition (E2EDEcomp) based on the idea to directly embedding the material decomposition function into the model-based optimization function. The optimization problem to solve becomes linear and the mapping is learned from the data during the iteration procedure of the solver.
\ Furthermore, the proposed method does not require any system calibration procedures to determine the material decomposition function as needed in previous approaches. The designed cost function contains the data consistency term in the material sinogram domain and a regularization term, acting in the material image domain, which is learned through an implicit denoising neural network-based function.
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:::info This paper is available on arxiv under CC BY 4.0 DEED license.
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2025-10-01 05:43:36
Ralf Cheung’s Sylvaine AI tackles the gap in adult content by prioritizing narrative intimacy over instant gratification. Built with GenAI models, safety safeguards, and moderation, the platform generates personalized, consent-focused stories. Early users call it empowering, highlighting how it reframes digital intimacy to better reflect women’s voices while ensuring safety and inclusivity.
2025-10-01 05:39:47
:::info Authors:
(1) Fatemeh Azari, Department of Mechanical Engineering and Materials Science, University of Pittsburgh, 3700 O'Hara Street Benedum Hall of Engineering, Pittsburgh, PA 15261 ([email protected]);
(2) Anne M. Robertson, Department of Mechanical Engineering and Materials Science, University of Pittsburgh, 3700 O'Hara Street Benedum Hall of Engineering, Pittsburgh, PA 15261 and Department of Bioengineering, University of Pittsburgh, 3700 O'Hara Street Benedum Hall of Engineering, Pittsburgh, PA 15261 ([email protected]);
(3) Lori A. Birder, Department of Medicine, University of Pittsburgh, 3550 Terrace St, Pittsburgh, PA 15213 and Department of Pharmacology and Chemical Biology, University of Pittsburgh, 3550 Terrace St, Pittsburgh, PA 15213 ([email protected]).
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Summary and 1 Introduction
Current mechanical models of the bladder largely idealize the bladder as spherical with uniform thickness. This present study aims to investigate this idealization using micro-CT to generate 3D reconstructed models of rat bladders at 10-20 micrometer resolution in both voided and filled states. Applied to three rat bladders, this approach identifies-shape, volume, and thickness variations under different pressures. These results demonstrate the filling/voiding process is far from the idealized spherical inflation/contraction. However, the geometry idealizations may be reasonable in cases where the filled bladder geometry is of importance, such as in studies of growth and remodeling.
The bladder's functionality is determined by its geometry, wall thickness, and biomechanical properties, all susceptible to impairment due to aging and disease, exemplified by BOO. The importance of BOO for the population is highlighted by the globally escalating prevalence of benign prostatic hyperplasia (BPH), the central contributor to BOO. BPH has shown a pronounced increase of 70.5% from 51.1 million cases in 2000 to 94 million in 2019 [1,2,3] and is a particularly important medical problem for men aged 50-60 years from lower socio-economic backgrounds. BPH induces a spectrum of urinary dysfunctions [4-5] including bladder wall (BW) hypertrophy, changes in bladder dynamics, trabeculation, diverticula, hematuria, and the formation of bladder stones, all of which severely affect bladder compliance during filling and voiding. This complex scenario underscores the necessity for comprehensive research into the BW's evolving properties during BOO progression so that both pharmacological and surgical interventions for BPH can be improved.
\ Traditionally, the imaging modality of choice for BOO patients has been ultrasound; however, it fails to offer comprehensive insights into wall changes arising from tissue growth and remodeling. Since 1991, there has been intermittent exploration into the geometrical properties of the BW during the filling and voiding cycles. Nonetheless, this field of inquiry is hindered by the paucity of integrated interdisciplinary methodologies and the requisite sophisticated equipment [4,5,6]. Existing scholarly literature delineates two primary experimental approaches for the elucidation of BW properties: analyses conducted at the organ level and those at the tissue strip level [7,8,9]. While investigations at the tissue level, such as uniaxial and biaxial testing, facilitate a detailed examination of layer- specific properties, associated studies of whole bladder structure and function are needed for understanding whole-organ function [10,11,13].
\ Moreover, conventional approaches to bladder mechanics characterization have historically adopted an oversimplified model of the bladder, conceptualizing it as a uniformly thick, spherical vessel. While these idealizations may be appropriate in some settings, they fail to account for the organ’s complex geometry including the changing and non-uniform wall thickness during the filling process. More sophisticated models of the bladder are needed to authentically reproduce the bladder's complex biomechanical behavior. In response to this gap, our investigation employed micro-CT to precisely quantify the geometric properties of the bladder wall within an ex-vivo filling model.
The urinary bladder, ureters, and urethra were surgically removed from three 3-4-month-old female Sprague-Dawley rats and immediately placed in a HEPES-buffered physiological saline solution (HB-PBS) with a composition of 134 mM NaCl, 6 mM KCl, 1 mM MgCl2, 2 mM 𝑀𝑔𝐶𝑙2, 10 mM HEPES, and 7 mM glucose, adjusted to a pH of 7.4. Calcium channel blockers were added to the solution to prevent spontaneous contraction of smooth muscle cells (SMC). The ureters were cauterized adjacent to the bladder wall and the urethra tied with 3-0 sutures to a 26G needle. All surrounding connective tissue was excised prior to mounting the bladder in the experimental apparatus. The urethra was cannulated and connected to the syringe pump (BS-8000, Braintree SCIENTIFIC INC) without pre-conditioning, delivering air into the bladder at a syringe translation rate of 1.5 ml/min until reaching a specified transmural pressure of 50-80 mmHg. Transmural pressure was quantitatively measured using a pressure transducer (PX409-OMEGA ENGINEERING INC.) placed near the bladder within the flow circuit. After achieving the target pressure, filling ceased, the valve was closed and the bladder was removed from the apparatus in preparation for micro-CT scanning.
he central steps to obtain a 3D reconstructed model of each bladder are i) mounting, alignment, and scanning using a high-resolution Skyscan 1272 scanner (Bruker Micro-CT, Kontich, Belgium), ii) 3D reconstruction of the micro-CT Z stacks of 2D images utilizing Nrecon software (Bruker MicroCT, Kontich, Belgium), iii) morphological analysis of the 3D model using Simpleware ScanIP software (Synopsys, Sunnyvale, California), iv), segmentation of the internal (lumen) and external (ablumen) geometries in Meshmixer software (Autodesk, San Francisco, California), and thickness analysis using Materialize 3-matic software (Materialize GmbH, Munich, Germany). Briefly, the excised bladder was first positioned within a custom-designed holder, ensuring immobilization to prevent data artifacts during the scanning process. After sealing the Luer-lock adapter with parafilm, the holder was mounted in the micro-CT system.
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![Figure 1: Micro-CT visualization of bladder's internal structure (left) [13], highlighting urethra, trigone, mid-bladder, and dome regions (right)](https://cdn.hackernoon.com/images/fWZa4tUiBGemnqQfBGgCPf9594N2-xk8335d.png)
\ Micro-CT scans were performed using 80 kV source voltage and 125 𝜇𝐴 source current. Images were captured at a 10.8 𝜇𝑚 pixel size using a rotation step of 0.6 degrees, a 2048 x 2048 frame size without filtering, and an exposure time of 400 ms. The reconstruction of these images with NRecon software involved smoothing at level 1, addressing ring artifacts at 50%, and correcting for 2% beam hardening. Scanning time for bladders was kept less than 10 minutes to avoid dehydration. The segmentation process began by thresholding grayscale values to create masks, transforming the reconstructed 2D images into 3D models. These models were then converted into surface models to produce stereolithography (STL) data. The finalized STL files were analyzed in Materialize 3-matics, using the midplane thickness tool to assess wall thickness. Our analysis provided key statistics such as median, average, and standard deviation of wall thickness provided as the output of thickness analysis from 3-Matics. Additionally, a histogram with an adjustable range was generated, ensuring no data points were overlooked. Morphology was compared between voided and filled states with transmural pressures for the filled state of P=50 mmHg, 57 mmHg and 80 mmHg for bladders A, B, C, respectively.
A quantitative assessment of bladder geometry was obtained for three rat bladder specimens, labeled as Bladder A, B, and C, Figure 2. Data was obtained at two inflation states: (1) voided (non-distended state – harvested condition) and (2) filled (distended). Bladder specific morphology results are given in Table 1.
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\ In the voided state, the wall thickness was highly non-uniform. For all three cases, the BW for the voided state was markedly thicker at the dome compared to the mid-bladder and trigonal areas. In particular, the average maximum dome thickness for all three bladders was 2.84 mm ±0.28 mm while the median thickness of the middle and trigonal areas was 0.65±0.14,respectively. In contrast, upon distension (Fig. 2, b, d, f), all three bladders have a relatively uniform, thin wall with an average median thickness of 0.10mm ±0.02mm.
\ Numerous models, including our own [3], have idealized the bladder with a spherical configuration and homogenous wall thickness [3,6,10], an approximation advantageous for deriving analytical solutions. The current work suggests this may be a reasonable approximation in studies where the filled bladder geometry is of importance. For example, in a recent study of growth and remodeling for BOO bladders, the filled (spherical) bladder was used as the reference configuration for defining the homeostatic stretch of both collagen fibers and smooth muscle cells [3]. However, it is possible that even the full bladder will show deviations in shape and wall thickness from this idealization in pathological states or as a result of aging [13]. A comparison of the shape and wall thickness between the voided and filled states, Fig 2, demonstrates that even for the healthy bladder, the filling process varies spatially over the wall of the bladder and is not well represented by simple spherical inflation. It is anticipated that heterogeneities in wall thickness will be even more complex during disease and a result of aging [13]. Hence, the changing shape and wall thickness during micturition will need to be quantified in these cases.
\ In summary, this work highlights the necessity to employ sophisticated imaging modalities, such as high-resolution micro-CT, to reveal bladder morphology and identify region-specific alterations in wall thickness throughout the filling and voiding process. Such high-resolution data is vital for computational mechanics models of the bladder needed for studying evolving bladder functionality during diseases such as bladder outlet obstruction (BOO). The authors express their gratitude for the funding received from NIH-R01 AG056944 and NIH-R01 DK133434.
[1] Awedew, A. F., et al. The global, regional, and national burden of benign prostatic hyperplasia in 204 countries and territories from 2000 to 2019: a systematic analysis for the Global Burden of Disease Study 2019. The Lancet Healthy Longevity, 3(11), e754-e776, 2022.
\ [2] Fusco, F., et al. Progressive bladder remodeling due to bladder outlet obstruction: a systematic review of morphological and molecular evidence in humans. BMC urology, 18, 1-11, 2018.
\ [3] Cheng, F., et al. A constrained mixture-micturition-growth (CMMG) model of the urinary bladder: Application to partial bladder outlet obstruction (BOO). Journal of the mechanical behavior of biomedical materials, 134, 105337, 2022.
\ [4] Damaser, M. S. et al. Partial outlet obstruction induces chronic distension and increased stiffness of rat urinary bladder. Neurourology and Urodynamics: Official Journal of the International Continence Society, 15(6), 650-665, 1996.
\ [5] Parekh, A., et al. Ex vivo deformations of the urinary bladder wall during whole bladder filling: contributions of extracellular matrix and smooth muscle. Journal of biomechanics, 43(9), 1708-1716, 2010.
\ [6] Trostorf, R., et al. A pilot study on an active and passive ex vivo characterization of the urinary bladder and its impact on three-dimensional modelling. journal of the mechanical behavior of biomedical materials, 133, 105347, 2022.
\ [7] Trostorf, R., et al. Location-and layer-dependent biomechanical and microstructural characterization of the porcine urinary bladder wall. Journal of the mechanical behavior of biomedical materials, 115, 104275, 2021.
\ [8] Hanczar, M., et al. The Significance of Biomechanics and Scaffold Structure for Bladder Tissue Engineering. International Journal of Molecular Sciences, 22(23), 12657, 2021.
\ [9] Ajalloueian, F., et al. Bladder biomechanics and the use of scaffolds for regenerative medicine in the urinary bladder. Nature Reviews Urology, 15(3), 155-174, 2018.
\ [10] Hennig, G., et al. Quantifying whole bladder biomechanics using the novel pentaplanar reflected image macroscopy system. Biomechanics and Modeling in Mechanobiology, 1- 11, 2023.
\ [11] Damaser, M. S. Whole bladder mechanics during filling. Scandinavian Journal of Urology and Nephrology, 33(201), 51-58, 1999
\ [12] “X-ray micro-ct analysis,” DigiM Solution, https://digimsolution.com/services/imaging/xray-micro-ct-analysis, 2023
\ [13] Birder, L. A., et al. Hypoxanthine Induces Signs of Bladder Aging with Voiding dysfunction and Lower Urinary Tract Remodeling. The Journals of Gerontology: Series A, glad171, 2023.
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:::info This paper is available on arxiv under CC BY 4.0 DEED license.
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2025-10-01 05:34:38
Oladipo Jegede, a senior project engineer, has led U.S. renewable projects exceeding 2 GW of solar and 1.5 GWh of battery storage. From designing solar streetlights in Nigeria to utility-scale projects in the U.S., he champions reliable, clean energy. Looking ahead, Jegede aims to bridge Africa’s energy gap by founding an engineering firm focused on sustainable infrastructure.
2025-10-01 05:30:52
Zenode, founded by Brandon Bourn and Collin Stoner, is tackling hardware design bottlenecks with AI. Their intelligent search engine reads and interprets electronic component datasheets, enabling engineers to find optimal parts faster. By cutting manual work and boosting precision, Zenode aims to accelerate PCB development and push hardware innovation beyond today’s limits.
2025-10-01 05:30:42
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The laser (TOPTICA-TopMode 405) with a center wavelength of 405 nm and bandwidth of 0.01 pm emits a Gaussian beam. The beam passes through a half-wave plate (HWP) followed by a polarising beam splitter (PBS). This combination gives a control over the intensity of the beam. A 50cm lens (L1) is used to focus the beam on a Type-I Bismuth Borate (BiBO) crystal of length 5 mm. The pump power before the nonlinear crystal is 5 mW. Two correlated degenerate spontaneous parametric down-converted photons are generated in the crystal in a non-collinear geometry assisted via angle tuning. The photon pairs are collimated by a 10 cm lens (L2) and separated by a prism mirror (PM) to interfere in a Mach Zehnder like setup as shown in Fig. 1. In one of the paths, a motorized translation stage MTS25-Z8, with a resolution of 29 nm, is added to compensate for the extra delay, if any. The two output ports of the beam splitter are initially used to show a HOM dip [29] as shown in Fig. 2.
\ Once the two polarisation entangled qubits are generated as described in the theoretical section, one can simply detect one of the qubits in the detector following path “X”. The other qubit is measured in H-V polarisation basis (path “Y” and “Z”). Measuring this other qubit in coincidence with the photon in path “X” generates random numbers as it gets detected either in the H polarisation (bit 0) or V polarisation (bit 1) at a particular instant with a 50:50 probability as shown in Fig. 1 (c) .The detectors (SPCM-800-14FC with a dark count of 100 counts per second) are identical in all three arms. To see measurement-device independence on the detector side, the diagram of the setup is slightly modified after the beam splitter to take projective measurements using quarter wave plate (QWP), HWP and a PBS combination as shown in Fig. 1 (a). On the contrary, to show the source independence, HOM curve is used as a certification technique. it’s visibility serves as a parameter for source-independence. Thus, a high visibility of the HOM curve is desired. In our case, we achieved a visibility of 97%. Since no phase information is required to generate bit streams, random bit-streams generated from maximally mixed state of a bipartite system and single photon source are identical. The extra phase information in the entangled case helps in providing security against attacks on the source. After calibration of data points A and B (as highlighted in Fig. 2) using the HOM curve, we generate entangled states at these data points.
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In this study, we have generated two datasets of 4.5 million (M) bit streams from the experimental setup shown in Fig. 1. The data is recorded for two different cases: Dataset A is recorded at the point of highest visibility of the HOM curve of Fig. 2), which represents a maximally entangled state, and Dataset B is recorded 700 nm away from dip point which degrades the quality of the above entangled quantum state. After post-processing using Toeplitz hash function, the length of random numbers is reduced from 4.5 M to 1.2 M.
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\ The datasets are tested against NIST-STS for quality of statistical randomness, and the results are highlighted in Table I. Kolmogorov-Smirnov test (KS test) is performed on sets with
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\ multiple p-values to provide an overall p-value indicating that they are normally distributed. Every test in Table I, shows a test-static p ≥ 0.01, suggesting that the generated dataset is statistically random.
\ The dataset A and B are obtained from two different quantum states, therefore having different “quantum information”. Here, we have used CHSH inequality (S) as one such quantifier of entanglement, as given in Eq. (2). The CHSH violation is measured from the direct observation as discussed in Ref. [13] and alternatively by estimating the density matrix using Bayesian and MLE quantum state tomographic techniques as in Ref.[26, 27].
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\ The results in Table I indicate a high confidence that both the datasets have statistically independent random bit-streams. Additionally, the results shown in Table II, prove the quantum correlation. The table, although indicating quantum signature, shows some inconsistency amongst different values. This discrepancy arises due to two reasons. One that Bayesian estimation is better than MLE in cases where right prior is known (Dataset A in our case). Secondly, the effect of sampling can bias the Bayesian estimation (Dataset B in our case).
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\ For Dataset B, right prior is not known. This requires further investigation. However, the result provided in this study positively concludes that the QRNG developed using entangled polarisation entangled photon pair is statistically random and secure against source and detector side attacks as indicated by HOM dip visibility and CHSH value respectively. A contrasting difference between our technique and randomness expansion protocols is that we have performed sequential measurements bunched together for a quantum correlation quantifier, while the semi-device independent protocols sparse them in generation and test rounds with some bias picked from a quantum source.
\ One open problem with random numbers is to investigate whether unpredictability and statistical properties are interrelated. To address this query, we performed scrambling operations at different lengths. One can use a quantifier from information theory, specifically min-entropy [30], defined as:
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In this article, we have provided a sanity check of the quantum source (HOM dip) and investigated the working of a fully device-independent quantum random number generator. One can put a constraint on resources and translate the fully deviceindependent scheme to measurement device-independent or source-independent quantum random number generator protocols with automation of HWPs and calculation of smooth conditional min-entropy (based on density matrix). Other quantifiers like entanglement measure/witness can be used to define new protocols in semi-device independent regime or for higher dimensions. Also, it is theoretically proven that the visibility of the HOM curve is equal to the purity of input photons [31]. This provides a correlation between closeness to the dip point and the amount of CHSH Bell violation as indicated by Table II. Physically, it means that relative phase information between two photons is used to prove device independence and thus, preventing attacks on QRNGs from Eve. Here device independence checks can be relaxed to cases where HOM serves as a check for source independence or CHSH Bell parameter serves as a security check for measurement device independence. Sending the qubit which is being traced out here, to another party, post-processing to consider loss over the channel and together with the addition of local operations using classical communication (LOCC) can convert this random number generation scheme to device-independent quantum key distribution scheme.
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:::info Authors:
(1) Vardaan Mongia, Quantum Science and Technology Laboratory, Physical Research Laboratory, Ahmedabad, 380009, India and Dept. of Physics, Indian Institute of Technology Gandhinagar, Gujarat, 382355, India;
(2) Abhishek Kumar, Space Weather Laboratory, Physical Research Laboratory, Ahmedabad, 380009, India;
(3) Shashi Prabhakar, Quantum Science and Technology Laboratory, Physical Research Laboratory, Ahmedabad, 380009, India;
(4) Anindya Banerji, Center for Quantum Technologies, National University of Singapore, Singapore 117543;
(5) R.P. Singh, Quantum Science and Technology Laboratory, Physical Research Laboratory, Ahmedabad, 380009, India.
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:::info This paper is available on arxiv under CC BY 4.0 DEED license.
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