Sajid Abbas

Bio: Sajid Abbas is an academic researcher from KAIST. The author has contributed to research in topics: Iterative reconstruction & Image quality. The author has an hindex of 3, co-authored 10 publications receiving 87 citations.

Effects of sparse sampling schemes on image quality in low-dose CT

Abstract: Purpose: Various scanning methods and image reconstruction algorithms are actively investigated for low-dose computed tomography (CT) that can potentially reduce a health-risk related to radiation dose. Particularly, compressive-sensing (CS) based algorithms have been successfully developed for reconstructing images from sparsely sampled data. Although these algorithms have shown promises in low-dose CT, it has not been studied how sparse sampling schemes affect image quality in CS-based image reconstruction. In this work, the authors present several sparse-sampling schemes for low-dose CT, quantitatively analyze their data property, and compare effects of the sampling schemes on the image quality.Methods: Data properties of several sampling schemes are analyzed with respect to the CS-based image reconstruction using two measures: sampling density and data incoherence. The authors present five different sparse sampling schemes, and simulated those schemes to achieve a targeted dose reduction. Dose reduction factors of about 75% and 87.5%, compared to a conventional scan, were tested. A fully sampled circular cone-beam CT data set was used as a reference, and sparse sampling has been realized numerically based on the CBCT data.Results: It is found that both sampling density and data incoherence affect the image quality in the CS-based reconstruction. Among the sampling schemes the authorsmore » investigated, the sparse-view, many-view undersampling (MVUS)-fine, and MVUS-moving cases have shown promising results. These sampling schemes produced images with similar image quality compared to the reference image and their structure similarity index values were higher than 0.92 in the mouse head scan with 75% dose reduction.Conclusions: The authors found that in CS-based image reconstructions both sampling density and data incoherence affect the image quality, and suggest that a sampling scheme should be devised and optimized by use of these indicators. With this strategic approach, one can acquire optimally sampled sparse data so that the CS-based algorithms can best perform in terms of image quality.« less

Super-sparsely view-sampled cone-beam CT by incorporating prior data

Abstract: Computed tomography (CT) is widely used in medicine for diagnostics or for image-guided therapies, and is also popular in industrial applications for nondestructive testing. CT conventionally requires a large number of projections to pro- duce volumetric images of a scanned object, because the conventional image reconstruction algorithm is based on filtered- backprojection. This requirement may result in relatively high radiation dose to the patients in medical CT unless the radiation dose at each view angle is reduced, and can cause expensive scanning time and efforts in industrial CT applications. Sparse- view CT may provide a viable option to address both issues including high radiation dose and expensive scanning efforts. However, image reconstruction from sparsely sampled data in CT is in general very challenging, and much efforts have been made to develop algorithms for such an image reconstruction problem. Image total-variation minimization algorithm inspired by compressive sensing theory has recently been developed, which exploits the sparseness of the image derivative magnitude and can reconstruct images from sparse-view data to a similar quality of the images conventionally reconstructed from many views. In successive CT scans, prior CT image of an object and its projection data may be readily available, and the current CT image may have not much difference from the prior image. Considering the sparseness of such a difference image between the successive scans, image reconstruction of the difference image may be achieved from very sparsely sampled data. In this work, we showed that one can further reduce the number of projections, resulting in a super-sparse scan, for a good quality image reconstruction with the aid of a prior data. Both numerical and experimental results are provided.

Experimental and analytical analysis of the impact of different base plate materials and design parameters on the performance of the photovoltaic/thermal system

Abstract: Solar energy is one of the most important and prospective renewable energy sources. Solar photovoltaic/thermal systems (PV/T), which could provide both heat and electricity at the same time, are becoming increasingly popular in households and commercial buildings. The PV/T system design parameters and base plate material selection are significant for achieving long-term dependability, longevity, and performance. The present work examines the effects of various base plate materials and design parameters on the performance of a PV/T assisted heat pump system, utilizing a validated mathematical model. Three different base plate materials, namely, aluminum (Al), copper (Cu), and Tedlar-Polyester-Tedlar (TPT), with a single-crystalline silicon photovoltaic module, were employed in the PV/T system design. According to the experimental results, the PV/T system with TPT base plate has a low photovoltaic module average temperature and a high average electrical efficiency which are 35 °C and 14.8%, respectively. The PV/T system with Cu base plate has the most optimal average thermal efficiency and coefficient of performance (COP) which are 48% and 3.93, respectively. In addition, the impact of the PV packing factor and the pitch of the heat pipe on the performance of PV/T system was examined and analyzed. For all types of base plate, the COP of the system decreases with the increase of PV packing factor and pitch of the heat pipe of the PV/T module.

Sparse-view computed laminography with a spherical sinusoidal scan for nondestructive testing

Abstract: X-ray computed laminography is widely used in nondestructive testing of relatively flat objects using an oblique scanning configuration for data acquisition. In this work, a new scanning scheme is proposed in conjunction with the compressive-sensing-based image reconstruction for reducing imaging radiation dose and scanning time. We performed a numerical study comparing image qualities acquired by various scanning configurations that are practically implementable: single-arc, double-arc, oblique, and spherical-sinusoidal trajectories. A compressive-sensing-inspired total-variation (TV) minimization algorithm was used to reconstruct the images from the data acquired at only 40 projection views in those trajectories. It was successfully demonstrated that the proposed scanning scheme outperforms the others in terms of image contrast and spatial resolution, although the oblique scanning scheme showed a comparable resolution property. We believe that the proposed scanning method may provide a solution to fast and low-dose nondestructive testing of radiation-sensitive and highly integrated devices such as multilayer microelectronic circuit boards.

Fast and low-dose computed laminography using compressive sensing based technique

Abstract: Computed laminography (CL) is well known for inspecting microstructures in the materials, weldments and soldering defects in high density packed components or multilayer printed circuit boards. The overload problem on x-ray tube and gross failure of the radio-sensitive electronics devices during a scan are among important issues in CL which needs to be addressed. The sparse-view CL can be one of the viable option to overcome such issues. In this work a numerical aluminum welding phantom was simulated to collect sparsely sampled projection data at only 40 views using a conventional CL scanning scheme i.e. oblique scan. A compressive-sensing inspired total-variation (TV) minimization algorithm was utilized to reconstruct the images. It is found that the images reconstructed using sparse view data are visually comparable with the images reconstructed using full scan data set i.e. at 360 views on regular interval. We have quantitatively confirmed that tiny structures such as copper and tungsten slags, and copper flakes in the reconstructed images from sparsely sampled data are comparable with the corresponding structure present in the fully sampled data case. A blurring effect can be seen near the edges of few pores at the bottom of the reconstructed images from sparsely sampled data, despite the overall image quality is reasonable for fast and low-dose NDT.

The evolution of image reconstruction for CT—from filtered back projection to artificial intelligence

Abstract: The first CT scanners in the early 1970s already used iterative reconstruction algorithms; however, lack of computational power prevented their clinical use. In fact, it took until 2009 for the first iterative reconstruction algorithms to come commercially available and replace conventional filtered back projection. Since then, this technique has caused a true hype in the field of radiology. Within a few years, all major CT vendors introduced iterative reconstruction algorithms for clinical routine, which evolved rapidly into increasingly advanced reconstruction algorithms. The complexity of algorithms ranges from hybrid-, model-based to fully iterative algorithms. As a result, the number of scientific publications on this topic has skyrocketed over the last decade. But what exactly has this technology brought us so far? And what can we expect from future hardware as well as software developments, such as photon-counting CT and artificial intelligence? This paper will try answer those questions by taking a concise look at the overall evolution of CT image reconstruction and its clinical implementations. Subsequently, we will give a prospect towards future developments in this domain. KEY POINTS: • Advanced CT reconstruction methods are indispensable in the current clinical setting. • IR is essential for photon-counting CT, phase-contrast CT, and dark-field CT. • Artificial intelligence will potentially further increase the performance of reconstruction methods.

Framing U-Net via Deep Convolutional Framelets: Application to Sparse-view CT

Abstract: X-ray computed tomography (CT) using sparse projection views is a recent approach to reduce the radiation dose. However, due to the insufficient projection views, an analytic reconstruction approach using the filtered back projection (FBP) produces severe streaking artifacts. Recently, deep learning approaches using large receptive field neural networks such as U-Net have demonstrated impressive performance for sparse- view CT reconstruction. However, theoretical justification is still lacking. Inspired by the recent theory of deep convolutional framelets, the main goal of this paper is, therefore, to reveal the limitation of U-Net and propose new multi-resolution deep learning schemes. In particular, we show that the alternative U- Net variants such as dual frame and the tight frame U-Nets satisfy the so-called frame condition which make them better for effective recovery of high frequency edges in sparse view- CT. Using extensive experiments with real patient data set, we demonstrate that the new network architectures provide better reconstruction performance.

Compressive sensing in medical imaging

Abstract: The promise of compressive sensing, exploitation of compressibility to achieve high quality image reconstructions with less data, has attracted a great deal of attention in the medical imaging community. At the Compressed Sensing Incubator meeting held in April 2014 at OSA Headquarters in Washington, DC, presentations were given summarizing some of the research efforts ongoing in compressive sensing for x-ray computed tomography and magnetic resonance imaging systems. This article provides an expanded version of these presentations. Sparsity-exploiting reconstruction algorithms that have gained popularity in the medical imaging community are studied, and examples of clinical applications that could benefit from compressive sensing ideas are provided. The current and potential future impact of compressive sensing on the medical imaging field is discussed.

Effects of sparse sampling schemes on image quality in low-dose CT

Abstract: Purpose: Various scanning methods and image reconstruction algorithms are actively investigated for low-dose computed tomography (CT) that can potentially reduce a health-risk related to radiation dose. Particularly, compressive-sensing (CS) based algorithms have been successfully developed for reconstructing images from sparsely sampled data. Although these algorithms have shown promises in low-dose CT, it has not been studied how sparse sampling schemes affect image quality in CS-based image reconstruction. In this work, the authors present several sparse-sampling schemes for low-dose CT, quantitatively analyze their data property, and compare effects of the sampling schemes on the image quality.Methods: Data properties of several sampling schemes are analyzed with respect to the CS-based image reconstruction using two measures: sampling density and data incoherence. The authors present five different sparse sampling schemes, and simulated those schemes to achieve a targeted dose reduction. Dose reduction factors of about 75% and 87.5%, compared to a conventional scan, were tested. A fully sampled circular cone-beam CT data set was used as a reference, and sparse sampling has been realized numerically based on the CBCT data.Results: It is found that both sampling density and data incoherence affect the image quality in the CS-based reconstruction. Among the sampling schemes the authorsmore » investigated, the sparse-view, many-view undersampling (MVUS)-fine, and MVUS-moving cases have shown promising results. These sampling schemes produced images with similar image quality compared to the reference image and their structure similarity index values were higher than 0.92 in the mouse head scan with 75% dose reduction.Conclusions: The authors found that in CS-based image reconstructions both sampling density and data incoherence affect the image quality, and suggest that a sampling scheme should be devised and optimized by use of these indicators. With this strategic approach, one can acquire optimally sampled sparse data so that the CS-based algorithms can best perform in terms of image quality.« less

High quality imaging from sparsely sampled computed tomography data with deep learning and wavelet transform in various domains

Abstract: Purpose Sparsely sampled computed tomography (CT) has been attracting attention as a technique that can reduce the high radiation dose of conventional CT. In general, iterative reconstruction techniques have been applied to sparsely sampled CT to realize high quality images. These methodologies require high computing power due to the modeling of the system and the trajectory of radiation rays. Therefore, the purpose of this study was to obtain high quality three-dimensional (3D) reconstructed images with deep learning under sparse sampling conditions. Methods We used a deep learning model based on a fully convolutional network and a wavelet transform to predict high quality images. To reduce the spatial resolution loss of predicted images, we replaced the pooling layer with a wavelet transform. Three different domains were evaluated - the sinogram domain, the image domain, and the hybrid domain - to optimize a reconstruction technique based on deep learning. To train and develop a deep learning model, The Cancer Imaging Archive (TCIA) dataset was used. Results Streak artifacts, which generally occur under sparse sampling conditions, were effectively removed from deep learning-based sparsely sampled reconstructed images. However, image characteristics of fine structures varied depending on the application of deep learning technologies. The use of deep learning techniques in the sinogram domain removed streak artifacts well, but some image noise remained. Likewise, when applying deep learning technology to the image domain, a blurring effect occurred. The proposed hybrid domain sparsely sampled reconstruction based on deep learning was able to restore images to a quality similar to fully sampled images. The structural similarity (SSIM) index values of sparsely sampled CT reconstruction based on deep learning technology were 0.85 or higher. Among the three domains studied, the hybrid domain techniques achieved the highest SSIM index values (0.9 or more). Conclusion We proposed a method of sparsely sampled CT reconstruction from a new perspective - unlike iterative reconstruction. In addition, we developed an optimal deep learning-based sparse sampling reconstruction technique by evaluating image quality with deep learning technologies.