Dr. Nikita Moriakov Profile

Dr. Nikita Moriakov

at Radboud Univ Medical Ctr

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Publications (4)

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Proceedings Article | 4 April 2022 Poster + Paper

Development and validation of a clinical CT system simulator

S. A. Tunissen, L. Oostveen, N. Moriakov, J. Teuwen, K. Michielsen, E. Smit, I. Sechopoulos

Proceedings Volume 12031, 120312G (2022) https://doi.org/10.1117/12.2611323

KEYWORDS: Computed tomography, Computer simulations

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Many advanced reconstruction and image processing methods are being developed with the aim of improving image quality in CT. Development and testing of these methods is aided by the ability to simulate realistic images, to both control the acquisition process and be able to use digital phantoms with known ground truth. Therefore, in this work, we present a method to simulate realistic scanner-specific sinograms from digital phantoms. For this, a series of measurements was conducted on a clinical CT system to characterize resolution loss, noise characteristics, and the exposure-to-detector output relationship. These measurements were used to develop a simulation pipeline, which involves raytracing of a digital phantom, taking into account the focal spot size and gantry rotation, followed by the use of Lambert’s Law to determine the amount of energy arriving at each detector element. The spectrum for the specific tube voltage and current was modeled using previously published spectral models. The resulting sinogram was then corrupted, by applying the measured detector Modulation Transfer Function (MTF), and adding noise based on the Noise Power Spectrum (NPS) and mean-variance relationship. Simulator results were compared to those acquired with the CT system in our clinic, showing an average difference of 2.1% in the off-center MTF magnitude, 0.048 in normalized NPS magnitude and only 6 and 5 Hounsfield Units (HU) difference in the voxel values for respectively water and air. The developed simulator seems capable of generating realistic CT images, which can help researchers develop and test their algorithms.

Proceedings Article | 4 April 2022 Poster + Paper

Adding patient motion from DCE-MRI to anthropomorphic phantoms for dedicated breast CT

Mikhail Mikerov, Koen Michielsen, Nikita Moriakov, Ioannis Sechopoulos

Proceedings Volume 12032, 1203224 (2022) https://doi.org/10.1117/12.2611487

KEYWORDS: Breast, Computed tomography, Motion measurement, Motion estimation

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Extending dedicated breast CT to dynamic contrast-enhanced breast CT will allow functional data by following contrast distribution over multiple time points. As a result of longer scans, unwanted patient motion becomes more likely and motion correction is necessary to avoid artifacts. A crucial part of the development of a motion compensation method is its validation on data with known ground truth. Thus, anthropomorphic phantoms with realistic motion patterns are needed. We present a method to combine motion vector fields, extracted from dynamic contrast-enhanced breast magnetic resonance imaging (DCE-MRI), with digital breast phantoms. DCE-MRI is an ideal source for these data due to its frequent clinical use and a similar prone patient positioning compared to breast CT. Our algorithm consists of three steps. First, the inter-scan motion vector fields are obtained by registration of consecutive images in the DCE-MRI sequence. In the second stage, the digital breast phantoms are aligned on MRI scans using an affine transformation. Finally, the obtained motion vector fields are transformed and applied to the phantoms after parameterization such that the total motion is distributed smoothly in time. The applied motion is evaluated in reconstructions of the simulated breast CT acquisition by qualitative comparison to clinical cases with intra-scan motion. We show that phantoms with simulated motion exhibit the same artifacts as in clinical data such as smooth transitions at the tissue interfaces and ghosting of fine structures.

Proceedings Article | 13 March 2019 Presentation + Paper

Vendor-independent soft tissue lesion detection using weakly supervised and unsupervised adversarial domain adaptation

Joris van Vugt, Elena Marchiori, Ritse Mann, Albert Gubern-Mérida, Nikita Moriakov, Jonas Teuwen

Proceedings Volume 10950, 1095002 (2019) https://doi.org/10.1117/12.2512940

KEYWORDS: Mammography, Data modeling, Breast, Digital mammography, Machine learning, Diagnostics, Breast cancer

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Computer-aided detection aims to improve breast cancer screening programs by helping radiologists to evaluate digital mammography (DM) exams. DM exams are generated by devices from different vendors, with diverse characteristics between and even within vendors. Physical properties of these devices and postprocessing of the images can greatly influence the resulting mammogram. This results in the fact that a deep learning model trained on data from one vendor cannot readily be applied to data from another vendor. This paper investigates the use of tailored transfer learning methods based on adversarial learning to tackle this problem. We consider a database of DM exams (mostly bilateral and two views) generated by Hologic and Siemens vendors. We analyze two transfer learning settings: 1) unsupervised transfer, where Hologic data with soft lesion annotation at pixel level and Siemens unlabelled data are used to annotate images in the latter data; 2) weak supervised transfer, where exam level labels for images from the Siemens mammograph are available. We propose tailored variants of recent state-of-the-art methods for transfer learning which take into account the class imbalance and incorporate knowledge provided by the annotations at exam level. Results of experiments indicate the beneficial effect of transfer learning in both transfer settings. Notably, at 0.02 false positives per image, we achieve a sensitivity of 0.37, compared to 0.30 of a baseline with no transfer. Results indicate that using exam level annotations gives an additional increase in sensitivity.

Proceedings Article | 1 March 2019 Presentation + Paper

Deep learning framework for digital breast tomosynthesis reconstruction

Nikita Moriakov, Koen Michielsen, Jonas Adler, Ritse Mann, Ioannis Sechopoulos, Jonas Teuwen

Proceedings Volume 10948, 1094804 (2019) https://doi.org/10.1117/12.2512912

KEYWORDS: Reconstruction algorithms, Breast, Digital breast tomosynthesis, Neural networks

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Digital breast tomosynthesis is rapidly replacing digital mammography as the basic x-ray technique for evaluation of the breasts. However, the sparse sampling and limited angular range gives rise to different artifacts, which manufacturers try to solve in several ways. In this study we propose an extension of the Learned Primal- Dual algorithm for digital breast tomosynthesis. The Learned Primal-Dual algorithm is a deep neural network consisting of several ‘reconstruction blocks’, which take in raw sinogram data as the initial input, perform a forward and a backward pass by taking projections and back-projections, and use a convolutional neural network to produce an intermediate reconstruction result which is then improved further by the successive reconstruction block. We extend the architecture by providing breast thickness measurements as a mask to the neural network and allow it to learn how to use this thickness mask. We have trained the algorithm on digital phantoms and the corresponding noise-free/noisy projections, and then tested the algorithm on digital phantoms for varying level of noise. Reconstruction performance of the algorithms was compared visually, using MSE loss and Structural Similarity Index. Results indicate that the proposed algorithm outperforms the baseline iterative reconstruction algorithm in terms of reconstruction quality for both breast edges and internal structures and is robust to noise.

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