Cheng-Ying Chou

An extended diffraction-enhanced imaging method for implementing multiple-image radiography

Diffraction-enhanced imaging (DEI) is an analyser-based x-ray imaging method that produces separate images depicting the projected x-ray absorption and refractive properties of an object. Because the imaging model of DEI does not account for ultra-small-angle x-ray scattering (USAXS), the images produced in DEI can contain artefacts and inaccuracies in medical imaging applications. In this work, we investigate an extended DEI method for concurrent reconstruction of three images that depict an objects projected x-ray absorption, refraction and USAXS properties. The extended DEI method can be viewed as an implementation of the recently proposed multiple-image radiography paradigm. Validation studies are conducted by use of computer-simulated and synchrotron measurement data.

Accelerating Image Reconstruction in Three-Dimensional Optoacoustic Tomography on Graphics Processing Units

PURPOSE Optoacoustic tomography (OAT) is inherently a three-dimensional (3D) inverse problem. However, most studies of OAT image reconstruction still employ two-dimensional imaging models. One important reason is because 3D image reconstruction is computationally burdensome. The aim of this work is to accelerate existing image reconstruction algorithms for 3D OAT by use of parallel programming techniques. METHODS Parallelization strategies are proposed to accelerate a filtered backprojection (FBP) algorithm and two different pairs of projection/backprojection operations that correspond to two different numerical imaging models. The algorithms are designed to fully exploit the parallel computing power of graphics processing units (GPUs). In order to evaluate the parallelization strategies for the projection/backprojection pairs, an iterative image reconstruction algorithm is implemented. Computer simulation and experimental studies are conducted to investigate the computational efficiency and numerical accuracy of the developed algorithms. RESULTS The GPU implementations improve the computational efficiency by factors of 1000, 125, and 250 for the FBP algorithm and the two pairs of projection/backprojection operators, respectively. Accurate images are reconstructed by use of the FBP and iterative image reconstruction algorithms from both computer-simulated and experimental data. CONCLUSIONS Parallelization strategies for 3D OAT image reconstruction are proposed for the first time. These GPU-based implementations significantly reduce the computational time for 3D image reconstruction, complementing our earlier work on 3D OAT iterative image reconstruction.

Digital holographic microtomography for high-resolution refractive index mapping of live cells

Quantification of three-dimensional (3D) refractive index (RI) with sub-cellular resolution is achieved by digital holographic microtomography (DHμT) using quantitative phase images measured at multiple illumination angles. The DHμT system achieves sensitive and fast phase measurements based on iterative phase extraction algorithm and asynchronous phase shifting interferometry without any phase monitoring or active control mechanism. A reconstruction algorithm, optical diffraction tomography with projection on convex sets and total variation minimization, is implemented to substantially reduce the number of angular scattered fields needed for reconstruction without sacrificing the accuracy and quality of the reconstructed 3D RI distribution. Tomogram of a living CA9-22 cell is presented to demonstrate the performance of the method. Further, a statistical analysis of the average RI of the nucleoli, the nucleus excluding the nucleoli and the cytoplasm of twenty CA9-22 cells is performed.

A fast forward projection using multithreads for multirays on GPUs in medical image reconstruction.

PURPOSE Iterative reconstruction techniques hold great potential to mitigate the effects of data noise and/or incompleteness, and hence can facilitate the patient dose reduction. However, they are not suitable for routine clinical practice due to their long reconstruction times. In this work, the authors accelerated the computations by fully taking advantage of the highly parallel computational power on single and multiple graphics processing units (GPUs). In particular, the forward projection algorithm, which is not included in the close-form formulas, will be accelerated and optimized by using GPU here. METHODS The main contribution is a novel forward projection algorithm that uses multithreads to handle the computations associated with a bunch of adjacent rays simultaneously. The proposed algorithm is free of divergence and bank conflict on GPU, and benefits from data locality and data reuse. It achieves the efficiency particularly by (i) employing a tiled algorithm with three-level parallelization, (ii) optimizing thread block size, (iii) maximizing data reuse on constant memory and shared memory, and (iv) exploiting built-in texture memory interpolation capability to increase efficiency. In addition, to accelerate the iterative algorithms and the Feldkamp-Davis-Kress (FDK) algorithm on GPU, the authors apply batched fast Fourier transform (FFT) to expedite filtering process in FDK and utilize projection bundling parallelism during backprojection to shorten the execution times in FDK and the expectation-maximization (EM). RESULTS Numerical experiments conducted on an NVIDIA Tesla C1060 GPU demonstrated the superiority of the proposed algorithms in computational time saving. The forward projection, filtering, and backprojection times for generating a volume image of 512 x 512 x 512 with 360 projection data of 512 x 512 using one GPU are about 4.13, 0.65, and 2.47 s (including distance weighting), respectively. In particular, the proposed forward projection algorithm is ray-driven and its paralleli-zation strategy evolves from single-thread-for-single-ray (38.56 s), multithreads-for-single-ray (26.05 s), to multithreads-for-multirays (4.13 s). For the voxel-driven backprojection, the use of texture memory reduces the reconstruction time from 4.95 to 3.35 s. By applying the projection bundle technique, the computation time is further reduced to 2.47 s. When employing multiple GPUs, near-perfect speedups were observed as the number of GPUs increases. For example, by using four GPUs, the time for the forward projection, filtering, and backprojection are further reduced to 1.11, 0.18, and 0.66 s. The results obtained by GPU-based algorithms are virtually indistinguishable with those by CPU. CONCLUSIONS The authors have proposed a highly optimized GPU-based forward projection algorithm, as well as the GPU-based FDK and expectation-maximization reconstruction algorithms. Our compute unified device architecture (CUDA) codes provide the exceedingly fast forward projection and backprojection that outperform those using the shading languages, cell broadband engine architecture and previous CUDA implementations. The reconstruction times in the FDK and the EM algorithms were considerably shortened, and thus can facilitate their routine usage in a variety of applications such as image quality improvement and dose reduction.

Ultrasound sonication with microbubbles disrupts blood vessels and enhances tumor treatments of anticancer nanodrug

Ultrasound (US) sonication with microbubbles (MBs) has the potential to disrupt blood vessels and enhance the delivery of drugs into the sonicated tissues. In this study, mouse ear tumors were employed to investigate the therapeutic effects of US, MBs, and pegylated liposomal doxorubicin (PLD) on tumors. Tumors started to receive treatments when they grew up to about 15 mm3 (early stage) with injection of PLD 10 mg/kg, or up to 50 mm3 (medium stage) with PLD 6 (or 4) mg/kg. Experiments included the control, PLD alone, PLD + MBs + US, US alone, and MBs + US groups. The procedure for the PLD + MBs + US group was that PLD was injected first, MB (SonoVue) injection followed, and then US was immediately sonicated on the tumor. The results showed that: (1) US sonication with MBs was always able to produce a further hindrance to tumor growth for both early and medium-stage tumors; (2) for the medium-stage tumors, 6 mg/kg PLD alone was able to inhibit their growth, while it did not work for 4 mg/kg PLD alone; (3) with the application of MBs + US, 4 mg/kg PLD was able to inhibit the growth of medium-stage tumors; (4) for early stage tumors after the first treatment with a high dose of PLD alone (10 mg/kg), the tumor size still increased for several days and then decreased (a biphasic pattern); (5) MBs + US alone was able to hinder the growth of early stage tumors, but unable to hinder that of medium stage tumors. The results of histological examinations and blood perfusion measurements indicated that the application of MBs + US disrupts the tumor blood vessels and enhances the delivery of PLD into tumors to significantly inhibit tumor growth.

Influence of imaging geometry on noise texture in quantitative in-line X-ray phase-contrast imaging

Quantitative in-line X-ray phase-contrast imaging methods seek to reconstruct separate images that depict an objects projected absorption and refractive properties. An understanding of the statistical properties of the reconstructed images can facilitate the identification of optimal imaging parameters for specific diagnostic tasks. However, the statistical properties of quantitative X-ray phase-contrast imaging remain largely unexplored. In this work, we derive analytic expressions that describe the second-order statistics of the reconstructed absorption and phase images. Concepts from statistical decision theory are applied to demonstrate how the statistical properties of images corresponding to distinct imaging geometries can influence signal detectability.

Noise texture and signal detectability in propagation-based x-ray phase-contrast tomography

PURPOSE X-ray phase-contrast tomography (PCT) is a rapidly emerging imaging modality for reconstructing estimates of an objects three-dimensional x-ray refractive index distribution. Unlike conventional x-ray computed tomography methods, the statistical properties of the reconstructed images in PCT remain unexplored. The purpose of this work is to quantitatively investigate noise propagation in PCT image reconstruction. METHODS The authors derived explicit expressions for the autocovariance of the reconstructed absorption and refractive index images to characterize noise texture and understand how the noise properties are influenced by the imaging geometry. Concepts from statistical detection theory were employed to understand how the imaging geometry-dependent statistical properties affect the signal detection performance in a signal-known-exactly/background-known-exactly task. RESULTS The analytical formulas for the phase and absorption autocovariance functions were implemented numerically and compared to the corresponding empirical values, and excellent agreement was found. They observed that the reconstructed refractive images are highly spatially correlated, while the absorption images are not. The numerical results confirm that the strength of the covariance is scaled by the detector spacing. Signal detection studies were conducted, employing a numerical observer. The detection performance was found to monotonically increase as the detector-plane spacing was increased. CONCLUSIONS The authors have conducted the first quantitative investigation of noise propagation in PCT image reconstruction. The reconstructed refractive images were found to be highly spatially correlated, while absorption images were not. This is due to the presence of a Fourier space singularity in the reconstruction formula for the refraction images. The statistical analysis may facilitate the use of task-based image quality measures to further develop and optimize this emerging modality for specific applications.

Image reconstruction in quantitative X-ray phase-contrast imaging employing multiple measurements

X-ray phase-contrast imaging is a technique that aims to reconstruct the projected absorption and refractive index distributions of an object. One common feature of reconstruction formulas for phase-contrast imaging is the presence of isolated Fourier domain singularities, which can greatly amplify the noise levels in the estimated Fourier domain and lead to noisy and/or distorted images in spatial domain. In this article, we develop a statistically optimal reconstruction method that employs multiple (>2) measurement states to mitigate the noise amplification effects due to singularities in the reconstruction formula. Computer-simulation studies are carried out to quantitatively and systematically investigate the developed method, within the context of propagation-based X-ray phase-contrast imaging. The reconstructed images are shown to possess dramatically reduced noise levels and greatly enhanced imaging contrast.

Analysis of ideal observer signal detectability in phase-contrast imaging employing linear shift-invariant optical systems

Phase-contrast imaging methods exploit variations in an objects refractive index distribution to permit the visualization of subtle features that may have very similar optical absorption properties. Although phase-contrast is often viewed as being desirable in many biomedical applications, its relative influence on signal detectability when both absorption- and phase-contrast are present remains relatively unexplored. In this work, we investigate the ideal Bayesian observer signal-to-noise ratio in phase-contrast imaging for a signal-known-exactly/background-known exactly detection task involving a weak signal. We demonstrate that this signal detectability measure can be decomposed into three contributions that have distinct interpretations associated with the imaging physics.

Image reconstruction in intravascular photoacoustic imaging

Intravascular photoacoustic (IVPA) imaging is a technique for visualizing atherosclerotic plaques with differential composition. Unlike conventional photoacoustic tomography scanning, where the scanning device rotates around the subject, the scanning aperture in IVPA imaging is enclosed within the imaged object. The display of the intravascular structure is typically obtained by converting detected photoacoustic waves into Cartesian coordinates, which can produce images with severe artifacts. Because the acquired data are highly limited, there does not exist a stable reconstruction algorithm for such imaging geometry. The purpose of this work was to apply image reconstruction concepts to explore the feasibility and efficacy of image reconstruction algorithms in IVPA imaging using traditional analytical formulas, such as a filtered back-projection (FBP) and the lambda-tomography method. Although the closed-form formulas are not exact for the IVPA system, a general picture of and interface information about objects are provided. To improve the quality of the reconstructed image, the iterative expectation maximization and penalized least-squares methods were adopted to minimize the difference between the measured signals and those generated by a reconstructed image. In this work, we considered both the ideal point detector and the acoustic transducers with finite- size aperture. The transducer effects including the spatial response of aperture and acoustoelectrical impulse responses were incorporated in the system matrix to reduce the aroused distortion in the IVPA reconstruction. Computer simulations and experiments were carried out to validate the methods. The applicability and the limitation of the reconstruction method were also discussed.