Objective:Diffusion-weighted MRI (dMRI) is a powerful tool for quantifying cellular microenvironment parameters. However, the inherently low signal-to-noise ratio (SNR) of dMRI can compromise the accuracy and reliability of parameter estimation. This study proposes a physics-assisted deep learning (DL)-based denoising framework designed to enhance dMRI signal quality and improve the robustness of subsequent biophysical model fitting, with potential relevance to low-SNR settings such as clinical 1.5 T MRI acquisitions. Approach:A dataset of paired noise-free and Rician-noise-corrupted dMRI signals was generated using the IMPULSED-dMRI signal model. Three denoising architectures were evaluated: Convolutional Neural Networks (CNN), Multilayer Perceptron (MLP), and Long Short-Term Memory (LSTM) networks. Denoised signals were then fitted to estimate cell diameter d , intracellular volume fraction V in , and extracellular apparent diffusion coefficient D e x . Performance was assessed using signal-domain Mean Absolute Error (MAE), fitted-parameter MAE, and fitting failure rate in synthetic IMPULSED-dMRI data with known ground-truth parameters and in in vitro evaluation using HeLa and MC38 cell lines. Main results:DL-based processing substantially improved dMRI signal denoising. The MLP and LSTM achieved similar performance, with the LSTM slightly better overall, and both outperformed the CNN. Averaged across all signals, the LSTM reduced denoising MAE from 5.97% to 1.58%. In the subsequent model fitting step, the LSTM produced modest reductions in parameter MAE, from 5.34 μm to 4.02 μm for d , from 15.00% to 11.96% for V i n , and from 0.77 to 0.63 μm2 /ms for D ex . The dominant benefit was fitting stabilization, with the overall fitting failure rate reduced from 57.6% to 17.7%. In in vitro experiments, relative to experimental references, the LSTM reduced MAE from 2.1 μm to 0.4 μm for d and from 7.8 to 5.4 percentage points for V i n , while reducing the mean overall fitting failure rate from 17.7% to 0%. Significance:The proposed framework improves dMRI signal quality and stabilizes subsequent IMPULSED-based microenvironmental parameter fitting. The primary value of this approach is improved fitting reliability under noisy dMRI conditions, with secondary gains in parameter accuracy, and it warrants further validation in heterogeneous tissues and in vivo datasets.
Ancient DNA and new inference methods have transformed the study of human origins, but consensus has not followed. Evidence increasingly indicates that hominin populations were pervasively structured and admixed, so complexity rather than simplicity is the appropriate prior. Here I highlight two blind spots that impede resolving that complexity. First, every inference passes through summaries of the data, and those summaries bound what can be recovered. Second, the space of candidate models is vast, yet competing model classes are rarely fit to common data, so a reported best model carries little evidence about untested model classes. This second blind spot reflects practice rather than data. It can be narrowed by testing competing models against withheld summaries and by reporting the models that were tried and rejected rather than only the winner.
We describe a systematic approach for spawning and aggregating multi-class cryo-EM reconstruction jobs. This approach formalizes standard ad hoc strategies of iterative classification and filtering typically used by practitioners to sort impure, heterogeneous samples. To our knowledge, this is the first method that can successfully perform ab initio reconstruction on datasets containing dozens of distinct species. We obtain 97% accuracy on ab initio reconstruction of a 45-class subset of Tomotwin-100, 75% accuracy on the full Tomotwin-100 dataset, and demonstrate recovery of ribosomal assembly states from an unfiltered experimental cryo-EM dataset. Our approach's capability scales with compute and lays the foundation for automated cryo-EM workflows in modern experimental settings.
Background:Establishing reliable reference dosimetry for ultra-high dose-rate (UHDR) beams (≥ 40 Gy/s) is challenging with conventional reference ionization chambers (IC) due to saturation effects arising from ion recombination. The Exradin A30 IC was introduced to overcome these challenges by utilizing an ultra-thin electrode spacing of 0.3 mm to enhance charge-collection efficiency (CCE). Purpose:The aim of this study is to investigate the commercial A30 IC as a reference dosimeter for UHDR electron beams. It explores the dosimetric properties of the A30 IC, including leakage current, CCE, polarity, and beam quality correction factors. Methods:Several measurements at the IntraOp® Mobetron® were acquired at a 1.5 cm distance from a medical UHDR electron accelerator head to achieve a maximum dose per pulse (DPP) of 9 Gy, and the instantaneous (or intra-pulse) dose rate (IDR) was 2.25 MGy/s using a 9 MeV electron beam. The measurements were obtained in a grounded water-equivalent plastic phantom, distilled water, and saline water. DPP values were adjusted by varying the SSD at a fixed pulse width (4 μs) while the pulse repetition frequency (PRF) was varied between 5 and 90 Hz. The CCE was calculated using EBT-XD radiochromic films under both UHDR and conventional beam conditions at identical dose and energy settings. Both CCE and Ppol were also measured as a function of DPP and PRF in distilled and saline water. Beam quality correction factors, k Q , were calculated using Monte Carlo and measured in electron beams from a TrueBeam linear accelerator. Results:The A30 IC exhibited a leakage current of less than 2 fA. Both Ppol and CCE decreased with increasing DPP, with CCE remaining in the 90-99% range across all three phantoms while polarity corrections decreased from 0.99 to 0.981 in both liquid and virtual water, respectively. Both CCE and Ppol were observed to be independent of the PRF, ranging from 5-90 Hz, in distilled and saline water. Measured k Q values agreed with calculation to within 0.8% for all energies except 9 MeV, which showed a 2% discrepancy. Conclusions:The commercial A30 ionization chamber exhibited 5% recombination with DPP of up to 5 Gy in both distilled and saline water. The IC signal in a solid phantom was found to be dependent on charge buildup effects, which can be mitigated by utilizing grounded phantoms. When appropriate CCE corrections are applied, the A30 IC is a suitable reference dosimeter for UHDR electron beams.
X-ray speckle-based dark-field imaging offers high sensitivity to sub-pixel structural features, yet its quantitative reliability in clinical and preclinical settings remains constrained by low photon flux and finite focal spot sizes. However, how hardware-induced noise and source blurring propagate through retrieval algorithms to degrade signal integrity is not fully understood. Here, we systematically evaluate algorithm robustness-quantified by signal linearity, sensitivity, and bias-under photon starvation and source blurring across two mathematically distinct frameworks: differential-based intrinsic tracking (Low-Coherence System, LCS) and patch-wise explicit tracking (X-ray Speckle-Tracking Speckle-Vector-Tracking, XST-XSVT). Our experimental results demonstrate that input speckle pattern distortions propagate through retrieval algorithms in fundamentally different ways depending on algorithm architecture. As an example, using our setup, under severe photon starvation (exposure reduced from 50 s to 1 s per mask step), derivative noise amplification in LCS causes its dark-field signal linearity and sensitivity to drop precipitously by 85.7% and 91.3%, respectively, while sharply elevating baseline bias. In contrast, XST-XSVT restricts these losses to 37.4% for linearity and 64.2% for sensitivity while maintaining a stable baseline, as its patch-wise variance calculation inherently suppresses stochastic noise. Similarly, under blur-limited conditions (expanding focal spot size from 7 μm to 50 μm), source blurring washes out the speckle pattern, directly reducing dark-field sensitivity for both LCS (by 47.8%) and XST-XSVT (by 42.6%). Beyond this shared sensitivity loss, the pattern smoothing causes the differential equations in LCS to become mathematically unstable, degrading its linearity by 5.3% and elevating baseline bias. Conversely, XST-XSVT robustly withstands the smoothed pattern, bypassing this instability to maintain linearity with a negligible 1.0% drop. This characterization establishes operational boundaries for low-power and low-coherence X-ray systems, guiding algorithm selection and framework optimization to realize quantitative dark-field imaging in preclinical and clinical applications.
