Biology subjects

Hassan, M. W.

Publications and source records attributed to Hassan, M. W..

3 recordsLinked to original sources

bioRxiv2026

VESTA: Machine Learning-Enabled Estimation of ViscoElastic Ratios from On-Axis Spatio-Temporal ARFI Features

Viscoelastic characterization of tissue has significant diagnostic value in oncology, as tumor progression alters both elasticity and viscosity in ways that neither property alone can fully capture. Existing acoustic radiation force (ARF)-based methods such as Viscoelastic Response (VisR) ultrasound estimate relative elasticity and viscosity through per-A-line nonlinear model fitting, which is computationally intensive and requires auxiliary simulations to correct elasticity-dependent bias. This work presents VESTA (Machine Learning-Enabled Estimation of ViscoElastic Ratios from On-Axis Spatio-Temporal ARFI Features), a two-stage data-driven pipeline that predicts elasticity ratio (ER) and viscosity ratio (VR) directly from seven normalized ARFI displacement features at the A-line level, without model fitting or compensation. Stage 1 is an MLP classifier that detects inclusion boundaries from normalized peak displacement and negative peak velocity ratios; Stage 2 is a dilated Conv1D regression model that estimates ER and VR along the full axial sequence using the predicted mask alongside displacement features. The pipeline was trained on 500 simulated inclusion scenarios spanning three geometries, five focal depths, two F-numbers, and a broad range of material contrasts. In silico, mean predicted ER and VR were within 12% of ground truth across all geometries, with performance best when ER and VR were moderate or decoupled. Experimental validation on a chicken breast phantom demonstrated plausible generalization to real tissue heterogeneity. Applied to an in vivo murine 4T1 breast cancer model, the pipeline tracked treatment-related attenuation of mechanical contrast in paclitaxel-treated tumors relative to controls over a 36-day imaging period, supporting its relevance for tumor monitoring.

bioengineering↗

bioRxiv2026

APRIL: Adaptive Regression-Based Two-Dimensional Quantitative Anisotropy Imaging Using Acoustic Radiation Force Impulse

ObjectiveThis study aims to develop and validate a quantitative, depth-resolved anisotropy imaging framework that extends ARFI-based focal degree-of-anisotropy (DoA) estimation into two-dimensional mapping by modeling the depth-dependent relationship between shear modulus ratio (SMR) and peak displacement ratio (PDR). MethodsWe propose APRIL (Adaptive Polynomial Regression for anisotropy Imaging via ARFI-induced DispLacements), a framework for quantitative, depth-resolved DoA imaging that adaptively selects polynomial regression or shape-preserving spline interpolation based on excitation PSF asymmetry. Training data were generated using an LS-DYNA3D + Field II simulation pipeline in homogeneous transversely isotropic media (SMR 0.9-4.9). Testing included shifted SMRs under varied acoustic conditions and three heterogeneous inclusion configurations (anisotropic inclusion in isotropic background and vice versa). Experimental validation was performed in an in-vivo murine tumor model over the time, ex-vivo chicken breast, and tissue-mimicking gelatin phantoms, using a Verasonics system with an L11-5v transducer. ResultsAPRIL achieved depth-resolved SMR prediction errors below 9% over 10-30 mm, with highest accuracy in the focal region (MAE 2.3%, RMSE < 0.1) and stable performance across PSF transition zones. In heterogeneous phantoms, it reconstructed anisotropy maps with SSIM up to 86% and MPE below 7%, accurately delineating inclusion boundaries. Under acoustic parameter variations, mean absolute errors remained below 10%, demonstrating robustness to system and tissue heterogeneity. ConclusionAPRIL enables robust, two-dimensional anisotropy imaging beyond focal estimates. SignificanceThe method provides a physically grounded and generalizable framework for clinically viable anisotropy biomarkers in muscle, tendon, kidney, tumor and breast tissues. HighlightsO_LINovelty: APRIL introduces LoA-conditioned adaptive polynomial-spline regression to extend ARFI-based anisotropy estimation from focal point estimates into full 2D depth-resolved SMR imaging. C_LIO_LIResults: APRIL achieved SMR prediction errors below 9% over 10-30 mm, SSIM up to 86% in heterogeneous phantoms, MAE below 10% under acoustic variations, tracked tumor anisotropy progression in vivo, and differentiated anisotropic inclusion versus isotropic background in tissue-mimicking gelatin phantom. C_LIO_LISignificance: APRIL enables clinically viable, spatially resolved anisotropy biomarker imaging in muscle, tendon, kidney, and tumor tissues without requiring heterogeneous training data. C_LI

bioengineering↗

bioRxiv2024

Trace Elements in Fish: Assessment of bioaccumulation and associated health risks.

Industrial and agricultural water run-off are polluting the aquatic ecosystem by depositing different toxic trace elements (TTEs) in riverine system. It has become a global concern impacting not only the well-being of aquatic organisms but human health as well. Current study evaluated the impact of four TTEs (Cadmium (Cd), copper (Cu), lead (Pb), and nickel (Ni)) in three organs (liver, gills, and muscles) of five fish species viz, Rita rita, Sperata sarwari, Wallago attu, Mastacembelus armatus, and Cirrhinus mrigala collected from right and left banks of Punjnad headworks during winter, spring and summer. We investigated accumulation (mg/kg) of these TTEs in fish in addition to the human health risk assessment by estimating exposure hazards, hazardous index (THQ and TTHQ) and metal pollution index (MPI). The obtained results showed that W. attu accumulated significantly more TTEs (p < 0.00) as compared to other fish. Among seasons, summer had significantly more (p < 0.00) accumulation of TTEs than other seasons. Lead (Pb) accumulation was highest across TTEs in fish liver as compared to gills and muscles. Right bank showed higher accumulation (p < 0.00) of all TTEs in all fish species in contrast to the left bank. The human health risk assessment showed that Cd and Pb had higher exposure levels than Cu and Ni. Furthermore, the THQ was in the order of Cd > Pb > Ni > Cu. All fish species had THQ 1 for Cd and Pb and TTHQ > 1 for all fish. MPI index showed moderate to high level of TTE contamination if all fish species. The study concluded that right bank has higher metal accumulation than left bank. However, fish consumption from both of the study site was not safe for human consumption.

zoology↗