Significant Loss Of Image Quality Research Articles

Application of low-dose CT in image-guided radiotherapy based on CT-linac

BackgroundImage-guided radiation therapy (IGRT) involves online medical imaging systems. CT-guided imaging systems not only have high image quality but also allow for direct dose calculation for radiation treatment planning, making it an ideal image guidance method. However, the imaging dose from CT images may pose additional risks to patients. Therefore, the aim of this study was to evaluate the substitutability of using low-dose CT images for IGRT. Materials and methodsThe CIRS-062 phantom, Catphan 604 phantom, CT dose index phantom were used to obtain the conversion curves of the CT values to the relative electron density (RED), image quality, and imaging dose of the low-dose CT, respectively. Catphan 604 phantom and clinical cases were used to analyze the feasibility of low-dose CT for dose calculation. ResultsNo significant differences were observed in the conversion curves of CT values to RED between low-dose and standard-dose CT scans. In terms of image quality, the geometric distortion of low-dose CT was 0.1 mm, which was the same as that of standard-dose CT. The spatial resolution of the images was 0.42–0.45 lp/mm, which was slightly better than that of standard-dose CT (0.40–0.44 lp/mm). The image uniformity was 98.4%–99.0%, slightly worse than that of standard-dose CT (99.4%–99.5%), but still met the clinical requirements. The imaging dose was 2.96–4.62 mGy, significantly lower than that of standard-dose CT (9.71–26.68 mGy), especially for head CT protocol. In terms of dose calculation, the γ passing rates were not lower than 98% for either the Catphan 604 phantom or the clinical cases. ConclusionLow-dose CT can significantly reduce the imaging dose without a significant loss of image quality and can be directly used for dose calculation in radiation treatment planning, providing a safer and more effective image guidance method for IGRT.

Towards real-time STEM simulations through targeted subsampling strategies.

Scanning transmission electron microscopy images can be complex to interpret on the atomic scale as the contrast is sensitive to multiple factors such as sample thickness, composition, defects and aberrations. Simulations are commonly used to validate or interpret real experimental images, but they come at a cost of either long computation times or specialist hardware such as graphics processing units. Recent works in compressive sensing for experimental STEM images have shown that it is possible to significantly reduce the amount of acquired signal and still recover the full image without significant loss of image quality, and therefore it is proposed here that similar methods can be applied to STEM simulations. In this paper, we demonstrate a method that can significantly increase the efficiency of STEM simulations through a targeted sampling strategy, along with a new approach to independently subsample each frozen phonon layer. We show the effectiveness of this method by simulating a SrTiO3 grain boundary and monolayer 2H-MoS2 containing a sulphur vacancy using the abTEM software. We also show how this method is not limited to only traditional multislice methods, but also increases the speed of the PRISM simulation method. Furthermore, we discuss the possibility for STEM simulations to seed the acquisition of real data, to potentially lead the way to self-driving (correcting) STEM.

Impact of high kilo-voltage peak technique on radiation dose for neonates undergoing chest radiography: Experimental study

IntroductionChest radiography remains the most often utilized radiological modality in the neonatal intensive care unit. Neonates are more radiosensitive and are at higher risk of cancer than adults. In addition, they have a longer life expectancy, which increases the risk of cancer. Therefore, it is essential to protect neonates from radiation hazards. PurposeThe study aims to assess the effectiveness of using high HKVp technique on reducing radiation dose to neonates undergoing Antero-posterior (AP) chest x-rays. Materials and methodsThe experiment used a newborn anthropomorphic phantom to simulate a neonate. Chest x-ray images were acquired using different combinations of KVp and mAs. Dose area product (DAP) was recorded in the Digital Imaging and Communications in Medicine (DICOM) header. The effective dose was determined using Monte Carlo simulation. Subjective assessment of image quality was evaluated by three radiologists blindly. The Objective assessment of image quality uses the calculation of the signal-to-noise ratio (SNR) and the contrast-to-noise ratio (CNR). ResultsThe reduction in the DAP and effective dose at 50 KVp was 26.7% and 19.2%, respectively and for 60 KVp it was 22.9 and 20.2, respectively. While, the reduction in the DAP and effective dose at 70 KVp was 40% and 26%, respectively. There were no significant differences in the means of the DAP, SNR, CNR, and effective dose among different combinations of KVp and mAs. ConclusionThe use of the high KVp technique can play an important role in reducing radiation dose without significant loss of image quality.

Improving throughput and image quality of high-resolution 3D X-ray microscopes using deep learning reconstruction techniques

In high-resolution X-ray computed tomography (CT), also known as 3D X-ray microscopy (XRM), low photon counts can lead to extremely long data acquisition times (in the order of hours). Reducing the number of radiographic projections (Np) acquired for CT reconstruction can be a cost-efficient solution in some cases. But the risk associated with reducing Np, if analytical filteredbackprojection algorithms are used for CT reconstruction, e.g., Feldkamp-Davis-Kress (FDK), is that it may produce a significant loss of image quality. Typical Np thresholds for a faithful 3D image reconstruction, required by the Nyquist-Shannon sampling theorem, are in the order of thousand projection views with modern XRM instruments. It is now well known, however, that deep learning (DL) based algorithms for CT reconstruction can improve the scan time (throughput) and image quality capabilities of XRM. This paper proposes the use of DL-based algorithms as an option for reducing Np, even down to a few hundred projections, without a significant loss of image quality. The integration of DL-based reconstruction techniques into 3D XRM workflows is presented throughout this article. It is shown that 3D XRM data reconstructions produced by DL-based workflows can provide up to 8X and 10X throughput improvement at similar or better image quality compared to standard FDK reconstruction.

Impact of dose reduction and iterative model reconstruction on multi-detector CT imaging of the brain in patients with suspected ischemic stroke

Non-contrast cerebral computed tomography (CT) is frequently performed as a first-line diagnostic approach in patients with suspected ischemic stroke. The purpose of this study was to evaluate the performance of hybrid and model-based iterative image reconstruction for standard-dose (SD) and low-dose (LD) non-contrast cerebral imaging by multi-detector CT (MDCT). We retrospectively analyzed 131 patients with suspected ischemic stroke (mean age: 74.2 ± 14.3 years, 67 females) who underwent initial MDCT with a SD protocol (300 mAs) as well as follow-up MDCT after a maximum of 10 days with a LD protocol (200 mAs). Ischemic demarcation was detected in 26 patients for initial and in 64 patients for follow-up imaging, with diffusion-weighted magnetic resonance imaging (MRI) confirming ischemia in all of those patients. The non-contrast cerebral MDCT images were reconstructed using hybrid (Philips “iDose4”) and model-based iterative (Philips “IMR3”) reconstruction algorithms. Two readers assessed overall image quality, anatomic detail, differentiation of gray matter (GM)/white matter (WM), and conspicuity of ischemic demarcation, if any. Quantitative assessment included signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) calculations for WM, GM, and demarcated areas. Ischemic demarcation was detected in all MDCT images of affected patients by both readers, irrespective of the reconstruction method used. For LD imaging, anatomic detail and GM/WM differentiation was significantly better when using the model-based iterative compared to the hybrid reconstruction method. Furthermore, CNR of GM/WM as well as the SNR of WM and GM of healthy brain tissue were significantly higher for LD images with model-based iterative reconstruction when compared to SD or LD images reconstructed with the hybrid algorithm. For patients with ischemic demarcation, there was a significant difference between images using hybrid versus model-based iterative reconstruction for CNR of ischemic/contralateral unaffected areas (mean ± standard deviation: SD_IMR: 4.4 ± 3.1, SD_iDose: 3.5 ± 2.3, P < 0.0001; LD_IMR: 4.6 ± 2.9, LD_iDose: 3.2 ± 2.1, P < 0.0001). In conclusion, model-based iterative reconstruction provides higher CNR and SNR without significant loss of image quality for non-enhanced cerebral MDCT.

Accelerating compressed sensing reconstruction of subsampled radial k-space data using geometrically-derived density compensation

Objective. To accelerate compressed sensing (CS) reconstruction of subsampled radial k-space data using a geometrically-derived density compensation function (gDCF) without significant loss in image quality. Approach. We developed a theoretical framework to calculate a gDCF based on Nyquist distance along the radial and circumferential directions of a discrete polar coordinate system. Our gDCF was compared against standard DCF (e.g. ramp filter) and another commonly used DCF (modified Shepp–Logan (SL) filter). The resulting image quality produced by each DCF was quantified using normalized root-mean-square-error (NRMSE), blur metric (1 = blurriest; 0 = sharpest), and structural similarity index (SSIM; 1 = perfect match; 0 = no match) compared with the reference. For filtered backprojection (FBP) of phantom data obtained at the Nyquist sampling rate, Cartesian k-space sampling was used as the reference. For CS reconstruction of subsampled cardiac magnetic resonance imaging datasets (real-time cardiac cine data with 11 projections per frame, n = 20 patients; cardiac perfusion data with 30 projections per frame, n = 19 patients), CS reconstruction without DCF was used as the reference. Main results. The NRMSE, SSIM, and blur metrics of the phantom data were good for all DCFs, confirming that our gDCF produces uniform densities at the upper limit (Nyquist). For CS reconstruction of subsampled real-time cine and cardiac perfusion datasets, the image quality metrics (SSIM, NRMSE) were significantly (p < 0.05) higher for our gDCF than other DCFs, and the reconstruction time was significantly (p < 0.05) faster for our gDCF (reference) than no DCF (11.9%–52.9% slower), standard DCF (23.9%–57.6% slower), and modified SL filter (13.5%–34.8% slower). Significance. The proposed gDCF accelerates CS reconstruction of subsampled radial k-space data without significant loss in image quality compared with no DCF as the reference.

Simulating dose reduction for myocardial perfusion SPECT using a Poisson resampling method.

The purpose of this study was to determine the lowest Tl-201 dose that does not reduce the image quality of myocardial perfusion SPECT (MPS) by Poisson resampling simulation. One hundred and twelve consecutive MPS data from patients with suspected or known coronary artery disease were collected retrospectively. Stress and rest MPS data were resampled using the Poisson method with 33%, 50%, 67%, and 100% count settings. Two nuclear medicine physicians assessed the image quality of reconstructed data visually by giving grades from - 2 to + 2. The summed stress score (SSS), summed rest score (SRS), and summed difference score (SDS) were obtained on the workstation. Image quality grades and semi-quantitative scores were then compared among these resampled images. The proportions of "adequate" image quality were 0.48, 0.75, 0.92, and 0.96 for the groups of images with 33%, 50%, 67%, and 100% data, respectively. The quality of the resampled images was significantly degraded at 50% and 33% count settings, while the image quality was not different between 67 and 100% count settings. We also found that high body mass index further decreased image quality at 33% count setting. Among the semi-quantitative parameters, SSS and SRS showed a tendency to increase with a decline in count. Based on the simulation results, Tl-201 dose for MPS can be reduced to 74MBq without significant loss of image quality. However, the SSS and SRS can be changed significantly, and it needs to be further verified under the different conditions.

A reduced-precision network for image reconstruction

Neural networks are often quantized to use reduced-precision arithmetic, as it greatly improves their storage and computational costs. This approach is commonly used in image classification and natural language processing applications. However, using a quantized network for the reconstruction of HDR images can lead to a significant loss in image quality. In this paper, we introduce QW-Net , a neural network for image reconstruction, in which close to 95% of the computations can be implemented with 4-bit integers. This is achieved using a combination of two U-shaped networks that are specialized for different tasks, a feature extraction network based on the U-Net architecture, coupled to a filtering network that reconstructs the output image. The feature extraction network has more computational complexity but is more resilient to quantization errors. The filtering network, on the other hand, has significantly fewer computations but requires higher precision. Our network recurrently warps and accumulates previous frames using motion vectors, producing temporally stable results with significantly better quality than TAA, a widely used technique in current games.

Rapid reconstruction of highly undersampled, non-Cartesian real-time cine k-space data using a perceptual complex neural network (PCNN).

Highly accelerated real-time cine MRI using compressed sensing (CS) is a promising approach to achieve high spatio-temporal resolution and clinically acceptable image quality in patients with arrhythmia and/or dyspnea. However, its lengthy image reconstruction time may hinder its clinical translation. The purpose of this study was to develop a neural network for reconstruction of non-Cartesian real-time cine MRI k-space data faster (<1 min per slice with 80 frames) than graphics processing unit (GPU)-accelerated CS reconstruction, without significant loss in image quality or accuracy in left ventricular (LV) functional parameters. We introduce a perceptual complex neural network (PCNN) that trains on complex-valued MRI signal and incorporates a perceptual loss term to suppress incoherent image details. This PCNN was trained and tested with multi-slice, multi-phase, cine images from 40 patients (20 for training, 20 for testing), where the zero-filled images were used as input and the corresponding CS reconstructed images were used as practical ground truth. The resulting images were compared using quantitative metrics (structural similarity index (SSIM) and normalized root mean square error (NRMSE)) and visual scores (conspicuity, temporal fidelity, artifacts, and noise scores), individually graded on a five-point scale (1, worst; 3, acceptable; 5, best), and LV ejection fraction (LVEF). The mean processing time per slice with 80 frames for PCNN was 23.7 ± 1.9 s for pre-processing (Step 1, same as CS) and 0.822 ± 0.004 s for dealiasing (Step 2, 166 times faster than CS). Our PCNN produced higher data fidelity metrics (SSIM = 0.88 ± 0.02, NRMSE = 0.014 ± 0.004) compared with CS. While all the visual scores were significantly different (P < 0.05), the median scores were all 4.0 or higher for both CS and PCNN. LVEFs measured from CS and PCNN were strongly correlated (R2 = 0.92) and in good agreement (mean difference = -1.4% [2.3% of mean]; limit of agreement = 10.6% [17.6% of mean]). The proposed PCNN is capable of rapid reconstruction (25 s per slice with 80 frames) of non-Cartesian real-time cine MRI k-space data, without significant loss in image quality or accuracy in LV functional parameters.

Pushing the temporal resolution in absorption and Zernike phase contrast nanotomography: enabling fast in situ experiments.

Hard X-ray nanotomography enables 3D investigations of a wide range of samples with high resolution (<100 nm) with both synchrotron-based and laboratory-based setups. However, the advantage of synchrotron-based setups is the high flux, enabling time resolution, which cannot be achieved at laboratory sources. Here, the nanotomography setup at the imaging beamline P05 at PETRA III is presented, which offers high time resolution not only in absorption but for the first time also in Zernike phase contrast. Two test samples are used to evaluate the image quality in both contrast modalities based on the quantitative analysis of contrast-to-noise ratio (CNR) and spatial resolution. High-quality scans can be recorded in 15 min and fast scans down to 3 min are also possible without significant loss of image quality. At scan times well below 3 min, the CNR values decrease significantly and classical image-filtering techniques reach their limitation. A machine-learning approach shows promising results, enabling acquisition of a full tomography in only 6 s. Overall, the transmission X-ray microscopy instrument offers high temporal resolution inabsorption and Zernike phase contrast, enabling in situ experiments at thebeamline.

68Ga-PSMA-11 dose reduction for dedicated pelvic imaging with simultaneous PET/MR using TOF BSREM reconstructions.

When increasing the PET acquisition time to match the longer MRI protocol in simultaneous PET/MR, the injected PET tracer dose can possibly be lowered to reduce radiation exposure. Moreover, applying new commercially available time-of-flight (TOF) block sequential regularized expectation maximization (BSREM)-based reconstruction algorithms could allow for further dose reductions. The purpose of this study was to find the minimal dose of the tracer targeting the prostate specific membrane antigen (68Ga-PSMA-11) for a dedicated 15-min pelvic PET/MR scan that still matches the image quality of a reference 3-min scan at 100% (150MBq) dose. In this retrospective analysis, 25 patients were included. PET emission datasets were edited to simulate stepwise reductions of injected tracer dose. Reference TOF ordered subset expectation maximum (OSEM) and new TOF BSREM reconstructions were performed and differences in the resulting PET images were visually and quantitatively assessed. Visually, TOF BSREM reconstructions with relatively high regularization parameter (β) values are preferred. Quantitatively, however, high β-values result in lower lesion maximum standardized uptake values (SUVmax) compared to the reference. A β-value of 550 was considered the optimal compromise for the lowest possible 10% dose reconstructions, resulting in comparable visual assessment and lesion SUVmax. This study indicates that the injected 68Ga-PSMA-11 tracer dose for a standard 3-min PET scan can be reduced to approximately 10% (15MBq) when the PET acquisition time is matched to the 15-min pelvic MRI protocol, and when reconstructed with TOF BSREM using β = 550. This decreases the effective dose from 3.54 to 0.35mSv. • Low-dose dedicated pelvic68Ga-PSMA-11 PET/MR reduces radiation exposure for patients. • Retrospective study investigating the minimal dose needed for adequate image quality for 15-min PET frames over the pelvis showed using quantitative and qualitative analysis that a substantial dose reduction is possible without significant loss of image quality when using the TOF BSREM reconstruction algorithm. • With the introduction of low-dose pelvic68Ga-PSMA-11 PET/MR, new potential applications of68Ga-PSMA-11 PET for local staging or investigation of equivocal MRI findings could become applicable, even for patients without confirmed prostate cancer.

Rapid dealiasing of undersampled, non-Cartesian cardiac perfusion images using U-net.

Compressed sensing (CS) is a promising method for accelerating cardiac perfusion MRI to achieve clinically acceptable image quality with high spatial resolution (1.6 × 1.6 × 8 mm3 ) and extensive myocardial coverage (6-8 slices per heartbeat). A major disadvantage of CS is its relatively lengthy processing time (~8 min per slice with 64 frames using a graphics processing unit), thereby making it impractical for clinical translation. The purpose of this study was to implement and test whether an image reconstruction pipeline including a neural network is capable of reconstructing 6.4-fold accelerated, non-Cartesian (radial) cardiac perfusion k-space data at least 10 times faster than CS, without significant loss in image quality. We implemented a 3D (2D + time) U-Net and trained it with 132 2D + time datasets (coil combined, zero filled as input; CS reconstruction as reference) with 64 time frames from 28 patients (8448 2D images in total). For testing, we used 56 2D + time coil-combined, zero-filled datasets (3584 2D images in total) from 12 different patients as input to our trained U-Net, and compared the resulting images with CS reconstructed images using quantitative metrics of image quality and visual scores (conspicuity of wall enhancement, noise, artifacts; each score ranging from 1 (worst) to 5 (best), with 3 defined as clinically acceptable) evaluated by readers. Including pre- and post-processing steps, compared with CS, U-Net significantly reduced the reconstruction time by 14.4-fold (32.1 ± 1.4 s for U-Net versus 461.3 ± 16.9 s for CS, p < 0.001), while maintaining high data fidelity (structural similarity index = 0.914 ± 0.023, normalized root mean square error = 1.7 ± 0.3%, identical mean edge sharpness of 1.2 mm). The median visual summed score was not significantly different (p = 0.053) between CS (14; interquartile range (IQR) = 0.5) and U-Net (12; IQR = 0.5). This study shows that the proposed pipeline with a U-Net is capable of reconstructing 6.4-fold accelerated, non-Cartesian cardiac perfusion k-space data 14.4 times faster than CS, without significant loss in data fidelity or image quality.

A study on changes in image quality with dose reduction in digital panoramic radiographs

Introduction: Panoramic radiography is frequently used for dental diagnosis and treatment planning. While smallest radiation exposure also being harmful to patients, every attempt must be made to reduce it as much as possible. Aims: The aim of the study was to assess effects of dose reduction on image quality of panoramic radiographs. Materials and Methods: The sample of 75 patients was selected from the patients coming at the outpatient department of the institute. Two panoramic radiographs were taken for each patient. A total of 150 panoramic radiographs were taken which were evaluated by two blinded observers for image quality. Results: Statistically nonsignificant difference was found between the image qualities of radiographs of the same patient with recommended exposures and those with altered exposure. Conclusion: Dose reduction did not cause significant loss of image quality using digital panoramic radiography machine when the dose was reduced by 36% by decreasing exposure time only.

Achromatic approach to phase-based multi-modal imaging with conventional X-ray sources.

Compatibility with polychromatic radiation is an important requirement for an imaging system using conventional rotating anode X-ray sources. With a commercially available energy-resolving single-photon-counting detector we investigated how broadband radiation affects the performance of a multi-modal edge-illumination phase-contrast imaging system. The effect of X-ray energy on phase retrieval is presented, and the achromaticity of the method is experimentally demonstrated. Comparison with simulated measurements integrating over the energy spectrum shows that there is no significant loss of image quality due to the use of polychromatic radiation. This means that, to a good approximation, the imaging system exploits radiation in the same way at all energies typically used in hard-X-ray imaging.

Fast 2-D ultrasound strain imaging: the benefits of using a GPU

Deformation of tissue can be accurately estimated from radio-frequency ultrasound data using a 2-dimensional normalized cross correlation (NCC)-based algorithm. This procedure, however, is very computationally time-consuming. A major time reduction can be achieved by parallelizing the numerous computations of NCC. In this paper, two approaches for parallelization have been investigated: the OpenMP interface on a multi-CPU system and Compute Unified Device Architecture (CUDA) on a graphics processing unit (GPU). The performance of the OpenMP and GPU approaches were compared with a conventional Matlab implementation of NCC. The OpenMP approach with 8 threads achieved a maximum speed-up factor of 132 on the computing of NCC, whereas the GPU approach on an Nvidia Tesla K20 achieved a maximum speed-up factor of 376. Neither parallelization approach resulted in a significant loss in image quality of the elastograms. Parallelization of the NCC computations using the GPU, therefore, significantly reduces the computation time and increases the frame rate for motion estimation.

Novel Intermode Prediction Algorithm for High Efficiency Video Coding Encoder

The joint collaborative team on video coding (JCT-VC) is developing the next-generation video coding standard which is called high efficiency video coding (HEVC). In the HEVC, there are three units in block structure: coding unit (CU), prediction unit (PU), and transform unit (TU). The CU is the basic unit of region splitting like macroblock (MB). Each CU performs recursive splitting into four blocks with equal size, starting from the tree block. In this paper, we propose a fast CU depth decision algorithm for HEVC technology to reduce its computational complexity. In2N×2N PU, the proposed method compares the rate-distortion (RD) cost and determines the depth using the compared information. Moreover, in order to speed up the encoding time, the efficient merge SKIP detection method is developed additionally based on the contextual mode information of neighboring CUs. Experimental result shows that the proposed algorithm achieves the average time-saving factor of 44.84% in the random access (RA) at Main profile configuration with the HEVC test model (HM) 10.0 reference software. Compared to HM 10.0 encoder, a small BD-bitrate loss of 0.17% is also observed without significant loss of image quality.

Fast 2-D ultrasound strain imaging: the benefits of using a GPU

Deformation of tissue can be accurately estimated from radio-frequency ultrasound data using a 2-dimensional normalized cross correlation (NCC)-based algorithm. This procedure, however, is very computationally time-consuming. A major time reduction can be achieved by parallelizing the numerous computations of NCC. In this paper, two approaches for parallelization have been investigated: the OpenMP interface on a multi-CPU system and Compute Unified Device Architecture (CUDA) on a graphics processing unit (GPU). The performance of the OpenMP and GPU approaches were compared with a conventional Matlab implementation of NCC. The OpenMP approach with 8 threads achieved a maximum speed-up factor of 132 on the computing of NCC, whereas the GPU approach on an Nvidia Tesla K20 achieved a maximum speed-up factor of 376. Neither parallelization approach resulted in a significant loss in image quality of the elastograms. Parallelization of the NCC computations using the GPU, therefore, significantly reduces the computation time and increases the frame rate for motion estimation.

Development of a geometric model for an all-reflective camera system

Currently in photogrammetry conventional camera optics, which are based on a combination of lenses, are solely used. These systems are also called refractive systems. The usage of refractive systems implies a general drawback for some applications. Due to the chromatic aberration of lenses, i.e. slightly different imaging functions for different spectral bands, a significant loss of image quality and geometric accuracy has to be accepted. This fact is important especially for applications that require imaging a wide spectral range. Conventional cameras are not able to satisfactorily capture the ultraviolet or near infrared spectral range in addition to the visible.These chromatic aberration problems can be completely be avoided in all-reflective optical systems, i.e. camera objectives which are completely based on mirrors. The paper will briefly describe the developed all-reflective optical systems designed for optical metrology purposes.A general disadvantage of the design of normal or wide angle all-reflective systems is the asymmetry of the mirror arrangement, which leads to large asymmetric geometric image distortions. These distortions cannot be modeled with standard methods of photogrammetry. Furthermore, the complete system is also more sensitive to local deviations from the ideal mirror surface. Therefore we developed a suitable geometric model, which is adapted to the special case. The model is based on the collinearity condition, extended by a specific additional parameter set optimized with regard to the characteristics of an all-reflective unobscured system. We will show various model variants based on the additional parameter sets of Brown, Ebner and Grün as well as Legendre polynomials, Chebyshev polynomials and Fourier series. The paper discusses the potential of these models to correct the distortion of an all-reflective unobscured optical system prototype based on four aspherical mirrors on the basis of test field self-calibration and describes different approaches to consider local deviations from the nominal aspherical mirror surface with the help of the finite elements method.

Multi-transmit beam forming for fast cardiac imaging--a simulation study.

Imaging at high temporal resolution is critical for a better understanding of transient cardiac phases with potential diagnostic value. Typically, parallel receive beam forming is used to achieve this. As an alternative, transmitting multiple lines simultaneously [i.e., multi-line transmit (MLT)] has been proposed. However, this approach has received less attention, most likely because of potential cross-talk artifacts between beams. In this study, based on different transducer configurations, the cross-talk level of different MLT systems was investigated and their point spread functions (PSFs) were compared with that of conventional beam forming (single-line transmit, SLT) by computer simulation. To reduce cross-talk artifacts, 7 different windowing functions were tested on transmit and receive: rectangular, Tukey (α = 0.5), Hann, cosine, Hamming, Gaussian (α = 0.4), and Nuttall. The simulation results showed the cross-talk varied inversely with the MLT beam opening angle and apodization could significantly reduce these artifacts at distinct opening angles, which were dependent on the transducer configuration. The optimal settings for an MLT system were highly dependent on the exact transducer configuration and must be deduced based on a given transducer. In particular, for a typical cardiac transducer configuration, a 4MLT imaging system with an opening angle of 22.73° and a Tukey (α = 0.5)-Tukey (α = 0.5) windowing scheme provided very similar image quality to SLT but with a 4 times higher frame rate. In addition, the MLT approach can be combined with (multiple) parallel receive beamforming to increase frame rate further. With these methods, a frame rate of approximately 300 Hz can be achieved to generate a 90° sector image without significant loss in image quality.

Impact of iterative reconstruction on CNR and SNR in dynamic myocardial perfusion imaging in an animal model

To evaluate a new iterative reconstruction (IR) algorithm for radiation dose, image quality (IQ), signal-to-noise-ratio (SNR), and contrast-to-noise-ratio (CNR) in multidetector computed tomography (MDCT) dynamic myocardial perfusion imaging (MPI). ECG-gated 256-slice MDCT dynamic MPI was performed in six pigs after subtotal balloon occlusion of one artery. Two 100 kVp protocols were compared: high dose (HD): 150 mAs; low dose (LD): 100 mAs. HD images were reconstructed with filtered back projection (FBP), LD images with FBP and different strengths of IR (L1, L4, and L7). IQ (5-point scale), SNR, and CNR (ischemic vs. normal myocardium) values derived from the HD (FBP) images and the different LD images were compared. Mean SNR values for myocardium were 16.3, 11.3, 13.1, 17.1, and 28.9 for the HD, LD (FBP), LD (L1), LD (L4), and LD (L7) reconstructions, respectively. Mean CNR values were 8.9, 6.3, 7.8, 9.3, and 12.8. IQ was scored as 4.6, 3.3, 4.4, 4.7, and 3.4, respectively. A significant loss of IQ was observed for the LD (L7) images compared to the HD (FBP) images (P < 0.05). Appropriate levels of iterative reconstruction can improve SNR and CNR, facilitating radiation dose savings in CT-MPI without influencing diagnostic quality. Iterative reconstruction (IR) can reduce radiation dose in myocardial perfusion CT. Our study also demonstrated improvements in image quality (noise, SNR, and CNR). Dynamic CT-MPI could help determine the hemodynamic significance of coronary artery disease. With dynamic CT MPI, myocardial blood flow can be determined quantitatively.