Purpose: Evaluate the feasibility of using a Nakagami model to create an accurate parametric image from ultrasound imaging data for the differentiation of homogenous and heterogeneous texture phantoms. Analysis was done on the raw data i.e., radiofrequency (RF) data collected before any post processing that can affect the images. Materials and methods: The Nakagami parametric image was constructed on demodulated RF data with the sliding window technique to create a map of local parameters. The Nakagami parameter (m) for the entire image was found by averaging all values. By design, when m is greater than 1, the distribution is post-Rayleigh. When m is equal to 1, the distribution is Rayleigh. To test the technique, two agar phantoms were constructed, using varying amounts of flour as the scatterer. The higher amount of flour scatterer was meant to mimic heterogeneous texture and the lesser amount meant to mimic homogeneous texture. Scans were done on each phantom and analyzed for differences in the Nakagami parameter. Results: Phantom 1 displayed a post-Rayleigh distribution (m = 36.1±7.0), while phantom 2 did so, to a lesser extent (m = 1.64±0.12). As the distribution transitions from Rayleigh to post Rayleigh, the scatterers in the sample go from being periodically located/randomly distributed to large numbers of randomly distributed scatterers. Conclusion: Our study suggests that Nakagami parametric based metrics may be used to increase robustness of texture analysis, considering the analysis is done on the raw data before any post processing that can affect the images is introduced.
Purpose: To evaluate potential use of wavelets analysis in discriminating benign and malignant renal masses (RM) Materials and Methods: Regions of interest of the whole lesion were manually segmented and co-registered from multiphase CT acquisitions of 144 patients (98 malignant RM: renal cell carcinoma (RCC) and 46 benign RM: oncocytoma, lipid-poor angiomyolipoma). Here, the Haar wavelet was used to analyze the grayscale images of the largest segmented tumor in the axial direction. Six metrics (energy, entropy, homogeneity, contrast, standard deviation (SD) and variance) derived from 3-levels of image decomposition in 3 directions (horizontal, vertical and diagonal) respectively, were used to quantify tumor texture. Independent t-test or Wilcoxon rank sum test depending on data normality were used as exploratory univariate analysis. Stepwise logistic regression and receiver operator characteristics (ROC) curve analysis were used to select predictors and assess prediction accuracy, respectively. Results: Consistently, 5 out of 6 wavelet-based texture measures (except homogeneity) were higher for malignant tumors compared to benign, when accounting for individual texture direction. Homogeneity was consistently lower in malignant than benign tumors irrespective of direction. SD and variance measured in the diagonal direction on the corticomedullary phase showed significant (p<0.05) difference between benign versus malignant tumors. The multivariate model with variance (3 directions) and SD (vertical direction) extracted from the excretory and pre-contrast phase, respectively showed an area under the ROC curve (AUC) of 0.78 (p < 0.05) in discriminating malignant from benign. Conclusion: Wavelet analysis is a valuable texture evaluation tool to add to a radiomics platforms geared at reliably characterizing and stratifying renal masses.
Radiomics workflows are high-throughput disease descriptive or predictive tools that extract mineable quantitative data of pathological phenotypes from standard-of-care grayscale images using advanced image processing algorithms. The success of these workflows rely on establishing large image datasets from which diverse disease descriptors can be extracted, with the expectation that large numbers may be able to overcome some of the inherent heterogeneities inherent in standard-of-care medical imaging workflows. Here, we present such a radiomics platform which relies on a combination of existing standard-of-care imaging clinical and research software as well as custom written code. The key components of the workflow include a file organization schema for centralized data storage, deployment of image registration strategies, and frontend GUI design for ease of use by the clinical researcher, all of which increase the transparency, flexibility, and portability of our radiomics platform. Widespread establishment of such radiomics platform can greatly revolutionize radiomics research and aid in successful translation into clinical decision support systems.
Presented are three preliminary studies completed using our proposed radiomics research workflow to investigate various diseases. The radiomics research workflow is modality and disease independent which allow it to serve as a general platform for medical image post-processing experimentation.
Purpose: Evaluate the feasibility of spectral analysis, particularly fast fourier transform (FFT), to help clinicians differentiate clear cell renal cell carcinoma (ccRCC) tumor grades using contrast-enhanced computed tomography (CECT) images of renal masses, quantitatively, and compare their performance to the Fuhrman grading system. Materials and Methods: Regions of interest of the whole lesion were manually segmented and co-registered from multiphase CT acquisitions of 95 patients with ccRCC. Here, FFT is employed to objectively quantify the texture of a tumor surface by evaluating tissue gray-level patterns and automatically measure frequency-based texture metrics. An independent t-test or a Wilcoxon rank sum test (depending on the data distribution) was used to determine if the spectral analysis metrics would produce statistically significant differences between the tumor grades. Receiver operating characteristic (ROC) curve analysis was used to evaluate the usefulness of spectral metrics in predicting the ccRCC grade. Results: The Wilcoxon test showed that there was a significant difference in complexity index between the different tumor grades, p < 0.01 at all the four phases of CECT acquisition. In all cases a positive correlation was observed between tumor grade and complexity index. ROC analysis revealed the importance of the entropy of FFT amplitude, FFT phase and complexity index and its ability to identify grade 1 and grade 4 tumors from the rest of the population. Conclusion: Our study suggests that FFT-based spectral metrics can differentiate between ccRCC grades, and in combination with other metrics improve patient management and prognosis of renal masses.
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