530 research outputs found
MITK-ModelFit: A generic open-source framework for model fits and their exploration in medical imaging -- design, implementation and application on the example of DCE-MRI
Many medical imaging techniques utilize fitting approaches for quantitative
parameter estimation and analysis. Common examples are pharmacokinetic modeling
in DCE MRI/CT, ADC calculations and IVIM modeling in diffusion-weighted MRI and
Z-spectra analysis in chemical exchange saturation transfer MRI. Most available
software tools are limited to a special purpose and do not allow for own
developments and extensions. Furthermore, they are mostly designed as
stand-alone solutions using external frameworks and thus cannot be easily
incorporated natively in the analysis workflow. We present a framework for
medical image fitting tasks that is included in MITK, following a rigorous
open-source, well-integrated and operating system independent policy. Software
engineering-wise, the local models, the fitting infrastructure and the results
representation are abstracted and thus can be easily adapted to any model
fitting task on image data, independent of image modality or model. Several
ready-to-use libraries for model fitting and use-cases, including fit
evaluation and visualization, were implemented. Their embedding into MITK
allows for easy data loading, pre- and post-processing and thus a natural
inclusion of model fitting into an overarching workflow. As an example, we
present a comprehensive set of plug-ins for the analysis of DCE MRI data, which
we validated on existing and novel digital phantoms, yielding competitive
deviations between fit and ground truth. Providing a very flexible environment,
our software mainly addresses developers of medical imaging software that
includes model fitting algorithms and tools. Additionally, the framework is of
high interest to users in the domain of perfusion MRI, as it offers
feature-rich, freely available, validated tools to perform pharmacokinetic
analysis on DCE MRI data, with both interactive and automatized batch
processing workflows.Comment: 31 pages, 11 figures URL: http://mitk.org/wiki/MITK-ModelFi
Constrained Probabilistic Mask Learning for Task-specific Undersampled MRI Reconstruction
Undersampling is a common method in Magnetic Resonance Imaging (MRI) to
subsample the number of data points in k-space, reducing acquisition times at
the cost of decreased image quality. A popular approach is to employ
undersampling patterns following various strategies, e.g., variable density
sampling or radial trajectories. In this work, we propose a method that
directly learns the undersampling masks from data points, thereby also
providing task- and domain-specific patterns. To solve the resulting discrete
optimization problem, we propose a general optimization routine called ProM: A
fully probabilistic, differentiable, versatile, and model-free framework for
mask optimization that enforces acceleration factors through a convex
constraint. Analyzing knee, brain, and cardiac MRI datasets with our method, we
discover that different anatomic regions reveal distinct optimal undersampling
masks, demonstrating the benefits of using custom masks, tailored for a
downstream task. For example, ProM can create undersampling masks that maximize
performance in downstream tasks like segmentation with networks trained on
fully-sampled MRIs. Even with extreme acceleration factors, ProM yields
reasonable performance while being more versatile than existing methods, paving
the way for data-driven all-purpose mask generation.Comment: accepted at WACV 202
Recommended from our members
UV laser radiation for microstructuring of photostructurable glasses
Photostructurable glasses are important materials for applications in microsystems. They enable structures with high aspect ratios and a high dependability of mechanical, optical and chemical properties in a large range of temperatures. The exposure of photostructurable glasses to UV laser radiation, as a rapid prototyping technique, is an alternative method to the exposure by a mask aligner.
Α photostructurable glass (FS21) was exposed to UV laser radiation of the wavelengths 248, 308 and 355 nm. Investigated was the influenee of the exposure parameters wavelength of laser radiation and energy density on structuring results such as crystallization depth, lateral geometry of crystallized areas, structure of crystallized areas and etch angle for single pulse exposure
Exploring the Impact of Image Resolution on Chest X-ray Classification Performance
Deep learning models for image classification have often used a resolution of
pixels for computational reasons.
This study investigates the effect of image resolution on chest X-ray
classification performance, using the ChestX-ray14 dataset.
The results show that a higher image resolution, specifically
pixels, has the best overall classification performance, with
a slight decline in performance between to pixels
for most of the pathological classes.
Comparison of saliency map-generated bounding boxes revealed that commonly
used resolutions are insufficient for finding most pathologies
- …