To develop and validate a deep learning-based reconstruction framework for highly accelerated two-dimensional (2D) phase contrast (PC-MRI) data with accurate and precise quantitative measurements. Methods:We propose a modified DL-ESPIRiT reconstruction framework for 2D PC-MRI, comprised of an unrolled neural network architecture with a Complex Difference estimation (CD-DL). CD-DL was trained on 155 fully sampled 2D PC-MRI pediatric clinical datasets. The fully sampled data (n = 29) was retrospectively undersampled (6-11×) and reconstructed using CD-DL and a parallel imaging and compressed sensing method (PICS). Measurements of peak velocity and total flow were compared to determine the highest acceleration rate that provided accuracy and precision within ±5%. Feasibility of CD-DL was demonstrated on prospectively undersampled datasets acquired in pediatric clinical patients (n = 5) and compared to traditional parallel imaging (PI) and PICS. Results:The retrospective evaluation showed that 9× accelerated 2D PC-MRI images reconstructed with CD-DL provided accuracy and precision (bias, [95% confidence intervals]) within ±5%. CD-DL showed higher accuracy and precision compared to PICS for measurements of peak velocity (2.8% [−2.9, 4.5] vs. 3.9% [−11.0, 4.9]) and total flow (1.8% [−3.9, 3.4] vs. 2.9% [−7.1, 6.9]). The prospective feasibility study showed that CD-DL provided higher accuracy and precision than PICS for measurements of peak velocity and total flow. Conclusion: In a retrospective evaluation, CD-DL produced quantitative measurements of 2D PC-MRI peak velocity and total flow with ≤ 5% error in both accuracy and precision for up to 9× acceleration. Clinical feasibility was demonstrated using a prospective clinical deployment of our 8× undersampled acquisition and CD-DL reconstruction in a cohort of pediatric patients.
Cardiac magnetic resonance (CMR) is an essential clinical tool for the assessment of cardiovascular disease. Deep learning (DL) has recently revolutionized the field through image reconstruction techniques that allow unprecedented data undersampling rates. These fast acquisitions have the potential to considerably impact the diagnosis and treatment of cardiovascular disease. Herein, we provide a comprehensive review of DL-based reconstruction methods for CMR. We place special emphasis on state-of-the-art unrolled networks, which are heavily based on a conventional image reconstruction framework. We review the main DL-based methods and connect them to the relevant conventional reconstruction theory. Next, we review several methods developed to tackle specific challenges that arise from the characteristics of CMR data. Then, we focus on DL-based methods developed for specific CMR applications, including flow imaging, late gadolinium enhancement, and quantitative tissue characterization. Finally, we discuss the pitfalls and future outlook of DL-based reconstructions in CMR, focusing on the robustness, interpretability, clinical deployment, and potential for new methods.
Breast cancer is the most common malignant tumor in women worldwide. In recent years, there has been an increasing use of immunohistochemistry (the process of detecting the expression of certain proteins in cytological images) to obtain useful information for diagnosis. This paper presents an efficient algorithm that automatically detects breast cancer cell nuclei and divides them into two groups: those that express the ER marker and those that do not. First, the areas that belong to the carcinoma are automatically identified. Then, the algorithm evaluates features such as size and shape to correctly segment the nuclei in these fields. Finally, the Fuzzy C-Means algorithm is used to classify the detected nuclei. The method proposed was evaluated with a set of 10 images which contained 4093 cell nuclei. The algorithm correctly identified 93.1% of the nuclei, and sensitivity and specificity of the classification were 95.7% and 93.2% respectively.
Cervical cancer is one of the main causes of death by disease worldwide. In Peru, it holds the first place in frequency and represents 8% of deaths caused by sickness. To detect the disease in the early stages, one of the most used screening tests is the cervix Papanicolaou test. Currently, digital images are increasingly being used to improve Pap test efficiency. This work develops an algorithm based on adaptive thresholds, which will be used in Pap smear assisted quality control software. The first stage of the method is a pre-processing step, in which noise and background removal is done. Next, a block is segmented for each one of the points selected as not background, and a local threshold per block is calculated to search for cell nuclei. If a nucleus is detected, an artifact rejection follows, where only cell nuclei and inflammatory cells are left for the doctors to interpret. The method was validated with a set of 55 images containing 2317 cells. The algorithm successfully recognized 92.3% of the total nuclei in all images collected.
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