Dr. Radin A. Nasirudin Profile

Dr. Radin A. Nasirudin

Reseach Fellow at Massachusetts General Hospital

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Publications (12)

Proceedings Article | 27 March 2019 Paper

Deep radiomic precision CT imaging for prognostic biomarkers for interstitial lung diseases

Tomoki Uemura, Mikio Matsuhiro, Chinatsu Watari, Janne Näppi, Radin Nasirudin, Toru Hironaka, Yoshiki Kawata, Noboru Niki, Hiroyuki Yoshida

Proceedings Volume 10954, 109541E (2019) https://doi.org/10.1117/12.2512058

KEYWORDS: Lung, Computed tomography, Image analysis, Performance modeling, Convolutional neural networks, Pulmonary function tests, Statistical analysis, Physiology, Gallium nitride, Feature extraction

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Proceedings Article | 6 March 2018 Paper

Radiomic texture-curvature (RTC) features for precision medicine of patients with rheumatoid arthritis-associated interstitial lung disease

Chinatsu Watari, Mikio Matsuhiro, Janne Näppi, Radin Nasirudin, Toru Hironaka, Yoshiki Kawata, Noboru Niki, Hiroyuki Yoshida

Proceedings Volume 10579, 105791N (2018) https://doi.org/10.1117/12.2294043

KEYWORDS: Lung, Computed tomography, Feature extraction, Chest, Pulmonary function tests

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Proceedings Article | 28 February 2018 Paper

Deep radiomic prediction with clinical predictors of the survival in patients with rheumatoid arthritis-associated interstitial lung diseases

Radin Nasirudina, Janne Näppi, Chinatsu Watari, Mikio Matsuhiro, Toru Hironaka, Shoji Kido, Hiroyuki Yoshida

Proceedings Volume 10575, 105753R (2018) https://doi.org/10.1117/12.2293374

KEYWORDS: Lung, Computed tomography, Feature extraction, Performance modeling, Pulmonary function tests, Convolutional neural networks

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We developed and evaluated the effect of our deep-learning-derived radiomic features, called deep radiomic features (DRFs), together with their combination with clinical predictors, on the prediction of the overall survival of patients with rheumatoid arthritis-associated interstitial lung disease (RA-ILD). We retrospectively identified 70 RA-ILD patients with thin-section lung CT and pulmonary function tests. An experienced observer delineated regions of interest (ROIs) from the lung regions on the CT images, and labeled them into one of four ILD patterns (ground-class opacity, reticulation, consolidation, and honeycombing) or a normal pattern. Small image patches centered at individual pixels on these ROIs were extracted and labeled with the class of the ROI to which the patch belonged. A deep convolutional neural network (DCNN), which consists of a series of convolutional layers for feature extraction and a series of fully connected layers, was trained and validated with 5-fold cross-validation for classifying the image patches into one of the above five patterns. A DRF vector for each patch was identified as the output of the last convolutional layer of the DCNN. Statistical moments of each element of the DRF vectors were computed to derive a DRF vector that characterizes the patient. The DRF vector was subjected to a Cox proportional hazards model with elastic-net penalty for predicting the survival of the patient. Evaluation was performed by use of bootstrapping with 2,000 replications, where concordance index (C-index) was used as a comparative performance metric. Preliminary results on clinical predictors, DRFs, and their combinations thereof showed (a) Gender and Age: C-index 64.8% [95% confidence interval (CI): 51.7, 77.9]; (b) gender, age, and physiology (GAP index): C-index: 78.5% [CI: 70.50 86.51], P < 0.0001 in comparison with (a); (c) DRFs: C-index 85.5% [CI: 73.4, 99.6], P < 0.0001 in comparison with (b); and (d) DRF and GAP: C-index 91.0% [CI: 84.6, 97.2], P < 0.0001 in comparison with (c). Kaplan-Meier survival curves of patients stratified to low- and high-risk groups based on the DRFs showed a statistically significant (P < 0.0001) difference. The DRFs outperform the clinical predictors in predicting patient survival, and a combination of the DRFs and GAP index outperforms either one of these predictors. Our results indicate that the DRFs and their combination with clinical predictors provide an accurate prognostic biomarker for patients with RA-ILD.

Proceedings Article | 10 March 2017 Paper

Enhancement of weakly tagged fecal materials in dual-energy CT colonography using spectral-driven iterative reconstruction technique

Radin Nasirudin, Janne Näppi, Toru Hironaka, Rie Tachibana, Hiroyuki Yoshida

Proceedings Volume 10132, 101323N (2017) https://doi.org/10.1117/12.2255673

KEYWORDS: Computed tomography, Image enhancement, Computer aided diagnosis and therapy, Virtual colonoscopy, Iodine, Computer aided design, CT reconstruction, Tissues, Visualization, Reconstruction algorithms

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Dual-energy computed tomography is used increasingly in CT colonography (CTC). The combination of computer-aided detection (CAD) and dual-energy CTC has a high clinical value because it can automatically detect clinically significant colonic lesions in CTC images with higher accuracy than does single-energy CTC. While CAD has demonstrated its ability to detect small polyps, its performance is highly dependent on the quality of the input images. The presence of artifacts such as beam hardening and image noise in ultra-low-dose CTC may severely degrade detection performance for small polyps. A further limitation to the effectiveness of CAD are the weakly tagged fecal materials in the colon that may cause false-positive detections. In this work, we developed a dual-energy method for enhancing the appearance of weakly tagged fecal materials in CTC images. The proposed method consists of two stages: 1) the detection of weakly tagged fecal materials by use of sinogram-based image decomposition and 2) the enhancement of the detected tagged fecal materials in the images using an iterative reconstruction method. In the first stage, the ultra-low-dose dual-energy projection data obtained from a CT scanner are decomposed into two basis materials – soft tissue and fecal-tagged material (iodine). Virtual monochromatic projection data are calculated from the material decomposition at a pre-determined energy. The iodine-decomposed sinogram and the virtual monochromatic projection data are then used as input to an iterative reconstruction method. In the second stage, virtual monochromatic images are reconstructed iteratively while the intensity of weakly tagged iodine in the images is enhanced. The performance of the proposed method was assessed qualitatively and quantitatively. Preliminary results show that our method effectively enhances the visual appearance of weakly tagged fecal materials in the reconstructed CT images while reducing noise and improving the overall quality of the reconstructed images.

Proceedings Article | 3 March 2017 Paper

Electronic cleansing for CT colonography using spectral-driven iterative reconstruction

Radin Nasirudin, Janne Näppi, Toru Hironaka, Rie Tachibana, Hiroyuki Yoshida

Proceedings Volume 10134, 1013436 (2017) https://doi.org/10.1117/12.2255678

KEYWORDS: Image quality, Computer aided diagnosis and therapy, Colon, CT reconstruction, Virtual colonoscopy, Computed tomography, Computer-aided diagnosis, Reconstruction algorithms, X-ray computed tomography

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Proceedings Article | 29 March 2016 Paper

Effect of low-dose CT and iterative reconstruction on trabecular bone microstructure assessment

Felix Kopp, Thomas Baum, Radin Nasirudin, Kai Mei, Eduardo Garcia, Rainer Burgkart, Ernst Rummeny, Jan Bauer, Peter Noël

Proceedings Volume 9788, 97881R (2016) https://doi.org/10.1117/12.2216175

KEYWORDS: Bone, Computed tomography, Spine, Analytical research, Diagnostics, Minerals, Magnetic resonance imaging, Statistical analysis, Optical filters, Image filtering, X-ray computed tomography, Image quality, Calibration, Biomedical optics

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Proceedings Article | 18 March 2015 Paper

Reduction of iodinated contrast medium in CT: feasibility study

Radin Nasirudin, Kai Mei, Felix Kopp, Petar Penchev, Ernst Rummeny, Martin Fiebich, Peter Noël

Proceedings Volume 9412, 94122L (2015) https://doi.org/10.1117/12.2082143

KEYWORDS: Iodine, Reconstruction algorithms, Image enhancement, Curium, Diagnostics, Photons, Sensors, Image quality, Image restoration, Monte Carlo methods

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Proceedings Article | 18 March 2015 Paper

A comparison of material decomposition techniques for dual-energy CT colonography

Radin Nasirudin, Rie Tachibana, Janne Näppi, Kai Mei, Felix Kopp, Ernst Rummeny, Hiroyuki Yoshida, Peter Noël

Proceedings Volume 9412, 94124F (2015) https://doi.org/10.1117/12.2081982

KEYWORDS: Iodine, Computer aided diagnosis and therapy, Virtual colonoscopy, Computer aided design, Diagnostics, Computed tomography, Signal attenuation, Bone, Colon, Image segmentation

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Proceedings Article | 18 March 2015 Paper

Region of interest processing for iterative reconstruction in x-ray computed tomography

Felix Kopp, Radin Nasirudin, Kai Mei, Andreas Fehringer, Franz Pfeiffer, Ernst Rummeny, Peter Noël

Proceedings Volume 9412, 94122E (2015) https://doi.org/10.1117/12.2081911

KEYWORDS: Reconstruction algorithms, Signal to noise ratio, X-ray computed tomography, Image quality, CT reconstruction, Diagnostics, Visualization, Scanners, Interference (communication), Algorithm development

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Proceedings Article | 19 March 2014 Paper

A comparison of simulation tools for photon-counting spectral CT

Radin Nasirudin, Petar Penchev, Kai Mei, Ernst Rummeny, Martin Fiebich, Peter Noël

Proceedings Volume 9033, 90334W (2014) https://doi.org/10.1117/12.2043538

KEYWORDS: Monte Carlo methods, Image quality, X-ray computed tomography, Sensors, X-rays, Photodetectors, Computer simulations, Iodine, Scanners, Target detection

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Proceedings Article | 6 March 2013 Paper

Characterization of spectral x-ray imaging for dental cone-beam computed tomography

Radin Adi Aizudin Bin Radin Nasirudin, Petar Penchev, Kai Mei, Ernst Rummeny, Martin Fiebich, Peter Noël

Proceedings Volume 8668, 86684S (2013) https://doi.org/10.1117/12.2008072

KEYWORDS: Monte Carlo methods, Gold, Metals, Sensors, Reconstruction algorithms, Signal attenuation, Image quality, X-ray computed tomography, Computed tomography, X-ray imaging

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Proceedings Article | 9 March 2011 Paper

Preliminary clinical results: an analyzing tool for 2D optical imaging in detection of active inflammation in rheumatoid arthritis

Radin Adi Aizudin Bin Radin Nasirudin, Reinhard Meier, Carmen Ahari, Matti Sievert, Martin Fiebich, Ernst Rummeny, Peter Noël

Proceedings Volume 7965, 796512 (2011) https://doi.org/10.1117/12.878021

KEYWORDS: Image fusion, Inflammation, Magnetic resonance imaging, Image analysis, Optical imaging, Diagnostics, Software development, X-ray imaging, X-rays, Luminescence

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Showing 5 of 12 publications

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