GIFTed Demons: deformable image registration with local structure-preserving regularization using supervoxels for liver applications.

Deformable image registration, a key component of motion correction in medical imaging, needs to be efficient and provides plausible spatial transformations that reliably approximate biological aspects of complex human organ motion. Standard approaches, such as Demons registration, mostly use Gaussian regularization for organ motion, which, though computationally efficient, rule out their application to intrinsically more complex organ motions, such as sliding interfaces. We propose regularization of motion based on supervoxels, which provides an integrated discontinuity preserving prior for motions, such as sliding. More precisely, we replace Gaussian smoothing by fast, structure-preserving, guided filtering to provide efficient, locally adaptive regularization of the estimated displacement field. We illustrate the approach by applying it to estimate sliding motions at lung and liver interfaces on challenging four-dimensional computed tomography (CT) and dynamic contrast-enhanced magnetic resonance imaging datasets. The results show that guided filter-based regularization improves the accuracy of lung and liver motion correction as compared to Gaussian smoothing. Furthermore, our framework achieves state-of-the-art results on a publicly available CT liver dataset

Adaption in Dynamic Contrast-Enhanced MRI

In breast DCE MRI, dynamic data are acquired to assess signal changes caused by contrast agent injection in order to classify lesions. Two approaches are used for data analysis. One is to fit a pharmacokinetic model, such as the Tofts model, to the data, providing physiological information. For accurate model fitting, fast sampling is needed. Another approach is to evaluate architectural features of the contrast agent distribution, for which high spatial resolution is indispensable. However, high temporal and spatial resolution are opposing aims and a compromise has to be found. A new area of research are adaptive schemes, which sample data at combined resolutions to yield both, accurate model fitting and high spatial resolution morphological information. In this work, adaptive sampling schemes were investigated with the objective to optimize fitting accuracy, whilst providing high spatial resolution images. First, optimal sampling design was applied to the Tofts model. By that it could be determined, based on an assumed parameter distribution, that time points during the onset and the initial fast kinetics, lasting for approximately two minutes, are most relevant for fitting. During this interval, fast sampling is required. Later time points during wash-out can be exploited for high spatial resolution images. To achieve fast sampling during the initial kinetics, data acquisition has to be accelerated. A common way to increase imaging speed is to use view-sharing methods, which omit certain k-space data and interpolate the missing data from neighboring time frames. In this work, based on phantom simulations, the influence of different view-sharing techniques during the initial kinetics on fitting accuracy was investigated. It was found that all view-sharing methods imposed characteristic systematic errors on the fitting results of Ktrans. The best fitting performance was achieved by the scheme ``modTRICKS'', which is a combination of the often used schemes keyhole and TRICKS. It is not known prior to imaging, when the contrast agent will arrive in the lesion or when the wash-out begins. Currently used adaptive sequences change resolutions a fixed time points. However, missing time points on the upslope may cause fitting errors and missing the signal peak may lead to a loss in morphological information. This problem was addressed with a new automatic resolution adaption (AURA) sequence. Acquired dynamic data were analyzed in real-time to find the onset and the beginning of the wash-out and consequently the temporal resolution was automatically adapted. Using a perfusion phantom it could be shown that AURA provides both, high fitting accuracy and reliably high spatial resolution images close to the signal peak. As alternative approach to AURA, a sequence which allows for retrospective resolution adaption, was assesses. Advantages are that adaption does not have to be a global process, and can be tailored regionally to local sampling requirements. This can be useful for heterogeneous lesions. For that, a 3D golden angle radial sequence was used, which acquires contrast information with each line and the golden angles allow arbitrary resolutions at arbitrary time points. Using a perfusion phantom, it could be shown that retrospective resolution adaption yields high fitting accuracy and relatively high spatial resolution maps

Adaption in Dynamic Contrast-Enhanced MRI

In breast DCE MRI, dynamic data are acquired to assess signal changes caused by contrast agent injection in order to classify lesions. Two approaches are used for data analysis. One is to fit a pharmacokinetic model, such as the Tofts model, to the data, providing physiological information. For accurate model fitting, fast sampling is needed. Another approach is to evaluate architectural features of the contrast agent distribution, for which high spatial resolution is indispensable. However, high temporal and spatial resolution are opposing aims and a compromise has to be found. A new area of research are adaptive schemes, which sample data at combined resolutions to yield both, accurate model fitting and high spatial resolution morphological information. In this work, adaptive sampling schemes were investigated with the objective to optimize fitting accuracy, whilst providing high spatial resolution images. First, optimal sampling design was applied to the Tofts model. By that it could be determined, based on an assumed parameter distribution, that time points during the onset and the initial fast kinetics, lasting for approximately two minutes, are most relevant for fitting. During this interval, fast sampling is required. Later time points during wash-out can be exploited for high spatial resolution images. To achieve fast sampling during the initial kinetics, data acquisition has to be accelerated. A common way to increase imaging speed is to use view-sharing methods, which omit certain k-space data and interpolate the missing data from neighboring time frames. In this work, based on phantom simulations, the influence of different view-sharing techniques during the initial kinetics on fitting accuracy was investigated. It was found that all view-sharing methods imposed characteristic systematic errors on the fitting results of Ktrans. The best fitting performance was achieved by the scheme ``modTRICKS'', which is a combination of the often used schemes keyhole and TRICKS. It is not known prior to imaging, when the contrast agent will arrive in the lesion or when the wash-out begins. Currently used adaptive sequences change resolutions a fixed time points. However, missing time points on the upslope may cause fitting errors and missing the signal peak may lead to a loss in morphological information. This problem was addressed with a new automatic resolution adaption (AURA) sequence. Acquired dynamic data were analyzed in real-time to find the onset and the beginning of the wash-out and consequently the temporal resolution was automatically adapted. Using a perfusion phantom it could be shown that AURA provides both, high fitting accuracy and reliably high spatial resolution images close to the signal peak. As alternative approach to AURA, a sequence which allows for retrospective resolution adaption, was assesses. Advantages are that adaption does not have to be a global process, and can be tailored regionally to local sampling requirements. This can be useful for heterogeneous lesions. For that, a 3D golden angle radial sequence was used, which acquires contrast information with each line and the golden angles allow arbitrary resolutions at arbitrary time points. Using a perfusion phantom, it could be shown that retrospective resolution adaption yields high fitting accuracy and relatively high spatial resolution maps

Brain Tumor Image Processing Using Fine-Tuned Resnet-101 Classification Model

Medical image processing relies heavily on the diagnosis of brain tumor images. It aids doctors in determining the correct diagnosis and management. One of the primary imaging methods for studying brain tissue is MR imaging. In recent years, deep learning techniques have shown significant potential in image processing. However, the modest quantity of medical images is a restriction of the classification of medical images. As a result of this restriction, fewer medical photos are available. Fine-tuned ResNet-101 (FR-101) is proposed to classify the brain tumor images to counteract this issue. Weiner filter is used to de-noise the acquired raw MR images, and the adaptive histogram equalization technique is used to improve contrast. A stacked autoencoder is utilized in the segmentation procedure to separate the tumor from healthy brain parts from the preprocessed data. The marker-based watershed technique is used to identify the tumor location and structure in the segmented data. The recommended approach is then used in the classification stage. To obtain the highest level of accuracy for our research, accuracy, precision, f1-score, recall, and mean absolute error are the measures of success are studied as well as a comparison of the suggested approach with a few other existing methods

Computational methods to predict and enhance decision-making with biomedical data.

The proposed research applies machine learning techniques to healthcare applications. The core ideas were using intelligent techniques to find automatic methods to analyze healthcare applications. Different classification and feature extraction techniques on various clinical datasets are applied. The datasets include: brain MR images, breathing curves from vessels around tumor cells during in time, breathing curves extracted from patients with successful or rejected lung transplants, and lung cancer patients diagnosed in US from in 2004-2009 extracted from SEER database. The novel idea on brain MR images segmentation is to develop a multi-scale technique to segment blood vessel tissues from similar tissues in the brain. By analyzing the vascularization of the cancer tissue during time and the behavior of vessels (arteries and veins provided in time), a new feature extraction technique developed and classification techniques was used to rank the vascularization of each tumor type. Lung transplantation is a critical surgery for which predicting the acceptance or rejection of the transplant would be very important. A review of classification techniques on the SEER database was developed to analyze the survival rates of lung cancer patients, and the best feature vector that can be used to predict the most similar patients are analyzed