Reconstruction Field Of View Research Articles

Cone-beam CT with a noncircular (sine-on-sphere) orbit: imaging performance of a clinical system for image-guided interventions.

We aim to compare the imaging performance of a cone-beam CT (CBCT) imaging system with noncircular scan protocols (sine-on-sphere) to a conventional circular orbit. A biplane C-arm system (ARTIS Icono; Siemens Healthineers) capable of circular and noncircular CBCT acquisition was used, with the latter orbit (sine-on-sphere, "Sine Spin") executing a sinusoidal motion with tilt amplitude over the half-scan orbit. A test phantom was used for the characterization of image uniformity, noise, noise-power spectrum (NPS), spatial resolution [modulation transfer function (MTF) in axial and oblique directions], and cone-beam artifacts. Findings were interpreted using an anthropomorphic head phantom with respect to pertinent tasks in skull base neurosurgery. The noncircular scan protocol exhibited several advantages associated with improved 3D sampling-evident in the NPS as filling of the null cone about the spatial frequency axis and reduction of cone-beam artifacts. The region of support at the longitudinal extrema was reduced from 16 to at a radial distance of 6.5cm. Circular and noncircular orbits exhibited nearly identical image uniformity and quantum noise, demonstrating cupping of and overall noise of . Although both the radially averaged axial MTF ( ) and 45deg oblique MTF ( ) were lower for the noncircular orbit compared with the circular orbit at the default full reconstruction field of view (FOV), there was no difference in spatial resolution for the medium reconstruction FOV (smaller voxel size). Differences in the perceptual image quality for the anthropomorphic phantom reinforced the objective, quantitative findings, including reduced beam-hardening and cone-beam artifacts about structures of interest in the skull base. Image quality differences between circular and noncircular CBCT orbits were quantitatively evaluated on a clinical system in the context of neurosurgery. The primary performance advantage for the noncircular orbit was the improved sampling and elimination of cone-beam artifacts.

Evaluating the effects of variation in CT scanning parameters on the image quality and Hounsfield units for optimization of dose in radiotherapy treatment planning: A semi-anthropomorphic thorax phantom study

The diagnosis accuracy of computed tomography (CT) systems and the reliability of calculated Hounsfield Units (HUs) are critical in tumor detection and cancer patients' treatment planning. This study evaluated the effects of scan parameters (Kilovoltage peak or kVp, milli-Ampere-second or mAS reconstruction kernels and algorithms, reconstruction field of view, and slice thickness) on image quality, HUs, and the calculated dose in the treatment planning system (TPS). A quality dose verification phantom was scanned several times by a 16-slice Siemens CT scanner. The DOSIsoft ISO gray TPS was applied for dose calculations. The SPSS.24 software was used to analyze the results and the P-value <0.05 was considered significant. Reconstruction kernels and algorithms significantly affected noise, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR). The noise increased and CNR decreased by raising the sharpness of reconstruction kernels. SNR and CNR had considerable increments at iterative reconstruction compared with the filtered back-projection algorithm. The noise decreased by raising mAS in soft tissues. Also, KVp had a significant effect on HUs. TPS--calculated dose variations were less than 2% for mediastinum and backbone and less than 8% for rib. Although HU variation depends on image acquisition parameters across a clinically feasible range, its dosimetric impact on the calculated dose in TPS can be neglected. Hence, it can be concluded that the optimized values of scan parameters can be applied to obtain the maximum diagnostic accuracy and calculate HUs more precisely without affecting the calculated dose in the treatment planning of cancer patients.

Improving the quality of head and neck radiotherapy CT images using a second image reconstruction set

PurposeTo improve the quality of radiotherapy head and neck CT images through use of an additional image set reconstructed from the raw data of the primary scan, thus allowing parameters such as reconstruction field-of-view (FOV) and kernel to be optimised without impacting on the images used for treatment planning dose calculations. MethodsUsing a Catphan image quality phantom and a Toshiba Aquilion LB CT scanner, qualitative and quantitative measurements were made for different reconstruction kernels and FOV diameters. The preferred FOV diameter and kernels were selected. Clinical images from six patients were reconstructed using those kernels (FC13, FC41, FC44, FC64) and the chosen FOV, 200 mm. The images were ranked to choose the kernel which gave best image quality for organ delineation. The scanner workflow was adjusted to produce for every scan a second image set using the chosen kernel and FOV. Finally, for 10 patient scans, image quality was compared for the two reconstructed images. ResultsThe second image set was produced using kernel FC44 and 200 mm FOV. The primary image set using 550 mm FOV and FC13 was unchanged and contours from the second image set merged onto the first. Oncologists reported increased confidence in contouring in all cases using the new procedure. ConclusionProduction of a second image set, using a reduced reconstruction FOV and a kernel which optimises contrast and sharpness, significantly improves the quality of head and neck CT images for contouring, and avoids any dose increase.

The impact of the field of view (FOV) on image quality in MDCT angiography of the lower extremities

ObjectivesTo evaluate the impact of the reconstructed field-of-view (FOV) on image quality in computed-tomography angiography (CTA) of the lower extremities.MethodsA total of 100 CTA examinations of the lower extremities were acquired on a 2 × 192-slice multidetector CT (MDCT) scanner. Three different datasets were reconstructed covering both legs (standard FOV size) as well as each leg separately (reduced FOV size). The subjective image quality was evaluated for the different vessel segments (femoral, popliteal, crural, pedal) by three readers using a semi-quantitative Likert scale. Additionally, objective image quality was assessed using an automated image quality metric on a per-slice basis.ResultsThe subjective assessment of the image quality showed an almost perfect interrater agreement. The image quality of the small FOV datasets was rated significantly higher as compared to the large datasets for all patients and vessel segments (p < 0.05) with a tendency towards a higher effect in smaller vessels. The difference of the mean scores between the group with the large FOV and small FOV was 0.68 for the femoral level, 0.83 for the popliteal level, 1.12 for the crural level, and 1.08 for the pedal level. The objective image quality metric also demonstrated a significant improvement of image quality in the small FOV datasets.ConclusionsSide-separated reconstruction of each leg in CTA of the lower extremities using a small reconstruction FOV significantly improves image quality as compared to a standard reconstruction with a large FOV covering both legs.Key Points• In CT angiography of the lower legs, the side-separated reconstruction of each leg using a small field-of-views improves image quality as compared to a standard reconstruction covering both legs.• The side-separated reconstruction can be readily implemented at every commercially available CT scanner.• There is no need for additional hardware or software and no additional burden to the patient.

Influence of Reconstruction FOV and Matrix Size on the Quantitative Accuracy of FDG-PET: Comparison between OSEM and Bayesian Penalized Likelihood

Field of view (FOV) and matrix size determine the pixel size of positron emission tomography (PET) images; however, the effect of any variation in these parameters on the quantitative accuracy is unclear. The FOV and matrix size of PET images are adjusted as per each clinical objective. Therefore, this study aimed to evaluate the quantitative accuracy of PET images under different FOV and matrix sizes. A National Electrical Manufacturers Association (NEMA) body phantom set was filled with 18F-FDG solution, and imaging data were acquired for 30 min. Images were reconstructed using ordered subset expectation maximization (OSEM) and Bayesian penalized likelihood (BPL), both of which were combined with point spread function (PSF) and time of flight (TOF). In each reconstruction method, the image parameters were set to the following: FOV, 20-70 cm; matrix size, 128×128 to 384×384; and pixel size, 1-3 mm. The images were evaluated by physical assessment of the recovery coefficient (RC) and maximum standardized uptake value ratio (SUVmax ratio). The RC of OSEM images was not affected by changes in FOV, whereas the RC of BPL images decreased in small spheres, when FOV was 20 and 30 cm. The SUVmax ratio of the OSEM images was not affected by the difference in pixel size. However, the SUVmax ratio of BPL images degraded in the 1-mm pixel size; this influence was observed only when the FOV was changed.Conclusion: BPL images reconstructed using a small FOV might degrade the quantitative accuracy of small spheres.

Single full-FOV reconstruction Fourier ptychographic microscopy.

Fourier ptychographic microscopy (FPM) is a recently developed computational imaging technique that has high-resolution and wide field-of-view (FOV). FPM bypasses the NA limit of the system by stitching a number of variable-illuminated measured images in Fourier space. On the basis of the wide FOV of the low NA objective, the high-resolution image with a wide FOV can be reconstructed through the phase recovery algorithm. However, the high-resolution reconstruction images are affected by the LED array point light source. The results are: (1) the intensities collected by the sample are severely declined when edge LEDs illuminate the sample; (2) the multiple reconstructions are caused by wavevectors inconsistency for the full FOV images. Here, we propose a new lighting scheme termed full FOV Fourier ptychographic microscopy (F3PM). By combining the LED array and telecentric lens, the method can provide plane waves with different angles while maintaining uniform intensity. Benefiting from the telecentric performance and f‒θ property of the telecentric lens, the system stability is improved and the relationship between the position of LED and its illumination angle is simplified. The excellent plane wave provided by the telecentric lens guarantees the same wavevector in the full FOV, and we use this wavevector to reconstruct the full FOV during one time. The area and diameter of the single reconstruction FOV reached 14.6mm 2 and 5.4 mm, respectively, and the diameter is very close to the field number (5.5 mm) of the 4× objective. Compared with the traditional FPM, we have increased the diameter of FOV in a single reconstruction by ∼ 10 times, eliminating the complicated steps of computational redundancy and image stitching.

Sliding window reduced FOV reconstruction for real-time cardiac imaging

BackgroundCurrent functional cardiovascular imaging protocols mostly rely on electrocardiogram (ECG) gating and breathholding. The resulting image quality can substantially suffer from insufficient patient cooperation or severe arrhythmia. Real-time imaging can mitigate these effects but requires highly accelerated techniques, usually relying on non-cartesian trajectories and Compressed Sensing (CS). MethodsWe investigate a sliding window reduced field of view (FOV) Echo Planar Imaging (EPI) technique for real-time cardiac MRI. Segmented EPI has been combined with a subtraction technique for reducing the FOV in cardiac applications to the region of the beating heart. Residual respiratory motion, potentially impairing the image quality, has been addressed by continuous update of the static image fraction, which is derived from a low-temporal resolution sliding window reconstruction. For further acceleration, the proposed technique was combined with parallel imaging. ResultsThe sliding window reduced FOV technique was proven feasible to reconstruct images of diagnostic image quality at a temporal resolution of 36.5ms per image. Semi-quantitative evaluation of image quality showed significant improvement over the existing rFOV method (p=0.039). Derived functional parameters show comparable results as with the BH-CINE reference. However, a trend to a slight underestimation of the largest and smallest in-plane volumes is observed. ConclusionThe proposed technique is feasible of providing real-time cardiac MRI with a temporal resolution better than 40ms without the need of computably complex reconstruction techniques.

Assessment of the variation in CT scanner performance (image quality and Hounsfield units) with scan parameters, for image optimisation in radiotherapy treatment planning

PurposeTo define a method and investigate how the adjustment of scan parameters affected the image quality and Hounsfield units (HUs) on a CT scanner used for radiotherapy treatment planning. A lack of similar investigations in the literature may be a contributing factor in the apparent reluctance to optimise radiotherapy CT protocols. MethodA Catphan phantom was used to assess how image quality on a Toshiba Aquilion LB scanner changed with scan parameters. Acquisition and reconstruction field-of-view (FOV), collimation, image slice thickness, effective mAs per rotation and reconstruction algorithm were varied. Changes were assessed for HUs of different materials, high contrast spatial resolution (HCSR), contrast-noise ratio (CNR), HU uniformity, scan direction low contrast and CT dose-index. ResultsCNR and HCSR varied most with reconstruction algorithm, reconstruction FOV and effective mAs. Collimation, but not image slice width, had a significant effect on CT dose-index with narrower collimation giving higher doses. Dose increased with effective mAs. Highest HU differences were seen when changing reconstruction algorithm: 56 HU for densities close to water and 117 HU for bone-like materials. Acquisition FOV affected the HUs but reconstruction FOV and effective mAs did not. ConclusionsAll the scan parameters investigated affected the image quality metrics. Reconstruction algorithm, reconstruction FOV, collimation and effective mAs were most important. Reconstruction algorithm and acquisition FOV had significant effect on HU. The methodology is applicable to radiotherapy CT scanners when investigating image quality optimisation, prior to assessing the impact of scan protocol changes on clinical CT images and treatment plans.

A method to extract image noise level from patient images in CT.

Image quality in computed tomography (CT) is usually not quantified in real time from patient scans. Rather, phantom scans are employed to measure the contrast, noise, and spatial resolution properties of a given system. This approach, however, is not ideal because many factors are difficult to represent using phantoms like variations in patient size, composition, and position within the gantry. Several methods for measuring the noise found within patient scans have been proposed. All of these methods rely on segmenting out relatively uniform regions within the patient in order to differentiate between morphological variations and pixel differences due to photon noise. In order to avoid this segmentation step, we propose to analyze the noise signal in the air surrounding the patient. We will demonstrate that the air signal surrounding the patient acts as a surrogate for the noise within the contours of the patient. Our work builds off the global noise index (GNI) method. In the GNI method, adjacent axial CT image slices are subtracted to remove the majority of the morphological variations in the data. Remaining morphological variations are removed with an edge-finding algorithm. Then the image is divided up into small regions of interest (ROI) and the pixel standard deviation is computed for each ROI. Only those ROIs not containing bone or air are then used to make a histogram of the standard deviation within the image. The mode of this histogram is referred to as the GNI. We refer to this as the traditional GNI (tradGNI). Our modification to this workflow is to apply this metric to just the air signal surrounding the patient. We evaluate the correlation between using the air signal and the in-phantom metric value for titration over: dose level, image slice thickness, kernel sharpness, iterative reconstruction level, model based iterative reconstruction, reconstruction field of view, and reconstruction interval. 373 patient abdomen pelvis exams were collected and the air based noise estimation metrics applied. Liver standard deviation values were measured on 40 of the patients and correlated with air GNI calculations. Our results show excellent linear correlation (R2 > 0.99) between the tradGNI being applied inside and outside of a phantom object. Our results are also shown to still be predictive of the noise under all scan parameters studied, including iterative and model based reconstruction. Fits of tradGNI versus dose level exhibited the expected square root dependence. Air based GNI metrics were predictive of human patient noise level and also correlated with manual liver standard deviation measurement (R2 = 0.73). Our results demonstrate the signal in the air surrounding an imaging object can accurately be used as a surrogate for the image noise within the object. Our method should enable faster and more robust patient specific image quality assessment due to the lack of the need to segment noise from morphological variations within a patient.

Intrinsic dependencies of CT radiomic features on voxel size and number of gray levels.

Many radiomics features were originally developed for non-medical imaging applications and therefore original assumptions may need to be reexamined. In this study, we investigated the impact of slice thickness and pixel spacing (or pixel size) on radiomics features extracted from Computed Tomography (CT) phantom images acquired with different scanners as well as different acquisition and reconstruction parameters. The dependence of CT texture features on gray-level discretization was also evaluated. A texture phantom composed of 10 different cartridges of different materials was scanned on eight different CT scanners from three different manufacturers. The images were reconstructed for various slice thicknesses. For each slice thickness, the reconstruction Field Of View (FOV) was varied to render pixel sizes ranging from 0.39 to 0.98mm. A fixed spherical region of interest (ROI) was contoured on the images of the shredded rubber cartridge and the 3D printed, 20% fill, acrylonitrile butadiene styrene plastic cartridge (ABS20) for all phantom imaging sets. Radiomic features were extracted from the ROIs using an in-house program. Features categories were: shape (10), intensity (16), GLCM (24), GLZSM (11), GLRLM (11), and NGTDM (5), fractal dimensions (8) and first-order wavelets (128), for a total of 213 features. Voxel-size resampling was performed to investigate the usefulness of extracting features using a suitably chosen voxel size. Acquired phantom image sets were resampled to a voxel size of 1×1×2 mm3 using linear interpolation. Image features were therefore extracted from resampled and original datasets and the absolute value of the percent coefficient of variation (%COV) for each feature was calculated. Based on the %COV values, features were classified in 3 groups: (1) features with large variations before and after resampling (%COV >50); (2) features with diminished variation (%COV <30) after resampling; and (3) features that had originally moderate variation (%COV <50%) and were negligibly affected by resampling. Group 2 features were further studied by modifying feature definitions to include voxel size. Original and voxel-size normalized features were used for interscanner comparisons. A subsequent analysis investigated feature dependency on gray-level discretization by extracting 51 texture features from ROIs from each of the 10 different phantom cartridges using 16, 32, 64, 128, and 256 gray levels. Out of the 213 features extracted, 150 were reproducible across voxel sizes, 42 improved significantly (%COV <30, Group 2) after resampling, and 21 had large variations before and after resampling (Group 1). Ten features improved significantly after definition modification effectively removed their voxel-size dependency. Interscanner comparison indicated that feature variability among scanners nearly vanished for 8 of these 10 features. Furthermore, 17 out of 51 texture features were found to be dependent on the number of gray levels. These features were redefined to include the number of gray levels which greatly reduced this dependency. Voxel-size resampling is an appropriate pre-processing step for image datasets acquired with variable voxel sizes to obtain more reproducible CT features. We found that some of the radiomics features were voxel size and gray-level discretization-dependent. The introduction of normalizing factors in their definitions greatly reduced or removed these dependencies.

Multi-resolution statistical image reconstruction for mitigation of truncation effects: application to cone-beam CT of the head

A prototype cone-beam CT (CBCT) head scanner featuring model-based iterative reconstruction (MBIR) has been recently developed and demonstrated the potential for reliable detection of acute intracranial hemorrhage (ICH), which is vital to diagnosis of traumatic brain injury and hemorrhagic stroke. However, data truncation (e.g. due to the head holder) can result in artifacts that reduce image uniformity and challenge ICH detection. We propose a multi-resolution MBIR method with an extended reconstruction field of view (RFOV) to mitigate truncation effects in CBCT of the head. The image volume includes a fine voxel size in the (inner) nontruncated region and a coarse voxel size in the (outer) truncated region. This multi-resolution scheme allows extension of the RFOV to mitigate truncation effects while introducing minimal increase in computational complexity. The multi-resolution method was incorporated in a penalized weighted least-squares (PWLS) reconstruction framework previously developed for CBCT of the head. Experiments involving an anthropomorphic head phantom with truncation due to a carbon-fiber holder were shown to result in severe artifacts in conventional single-resolution PWLS, whereas extending the RFOV within the multi-resolution framework strongly reduced truncation artifacts. For the same extended RFOV, the multi-resolution approach reduced computation time compared to the single-resolution approach (viz. time reduced by 40.7%, 83.0%, and over 95% for an image volume of 6003, 8003, 10003 voxels). Algorithm parameters (e.g. regularization strength, the ratio of the fine and coarse voxel size, and RFOV size) were investigated to guide reliable parameter selection. The findings provide a promising method for truncation artifact reduction in CBCT and may be useful for other MBIR methods and applications for which truncation is a challenge.

SU-F-R-09: Homogenization of CT Images for Radiomics Studies: It's Like Butter(worth)

Purpose: Previous studies have shown that differences in the CT image acquisition parameters produce variability in extracted quantitative image features. The purpose of our study was to determine if the variability due to inconsistencies in the reconstruction field of view (FOV), or pixel size, can be reduced by image filtering. Methods: We reconstructed the CT scans of 8 NSCLC patients 5 times, varying the FOV from 30 to 50 cm. We then calculated 150 radiomics features for each of the 40 CT image sets using 6 preprocessing methods. The preprocessing methods included re-sampling the images to a consistent 1 mm per pixel and Butterworth filters with cutoff frequencies from 75 to 200. We next calculated the overall concordance correlation coefficient (OCCC) for each feature to assess the intra-patient variability due to multiple FOVs relative to the inter-patient variability. To further explore how image preprocessing can reduce the effects of the reconstruction FOV, we performed hierarchical clustering. Results: 99% of all features had an OCCC of greater than 0.95 for images resampled and processed with a Butterworth filter with cutoff frequency 125, compared to only 21% of the features calculated without image preprocessing. Filtered images had consistently larger OCCC values except for shape related features which had all OCCC values > 0.95 regardless of the image preprocessing. Without filtering, hierarchical clustering was able to correctly group the 5 FOV scans for only 2 of the 8 patients. For images resample and processed with a Butterworth filter with cutoff frequency 125, the hierarchical clustering correctly grouped the 5 FOV scans for all 8 patients. Conclusion: Radiomics features, especially those that relate the image intensity and spatial information, show a strong dependence on the reconstruction FOV. However, our results demonstrate that resampling and filtering the images can greatly reduce this dependence.

MO-DE-207B-04: Impact of Reconstruction Field of View On Radiomics Features in Computed Tomography (CT) Using a Texture Phantom

Purpose: To investigate the impact of reconstruction Field of View on Radiomics features in computed tomography (CT) using a texture phantom. Methods: A rectangular Credence Cartridge Radiomics (CCR) phantom, composed of 10 different cartridges, was scanned on four different CT scanners from two manufacturers. A pre-defined scanning protocol was adopted for consistency. The slice thickness and reconstruction interval of 1.5 mm was used on all scanners. The reconstruction FOV was varied to result a voxel size ranging from 0.38 to 0.98 mm. A spherical region of interest (ROI) was contoured on the shredded rubber cartridge from CCR phantom CT scans. Ninety three Radiomics features were extracted from ROI using an in-house program. These include 10 shape, 22 intensity, 26 GLCM, 11 GLZSM, 11 RLM, 5 NGTDM and 8 fractal dimensional features. To evaluate the Interscanner variability across three scanners, a coefficient of variation (COV) was calculated for each feature group. Each group was further classified according to the COV by calculating the percentage of features in each of the following categories: COV≤ 5%, between 5 and 10% and ≥ 10%. Results: Shape features were the most robust, as expected, because of the spherical contouring of ROI. Intensity features were the second most robust with 54.5 to 64% of features with COV < 5%. GLCM features ranged from 31 to 35% for the same category. RLM features were sensitive to specific scanner and 5% variability was 9 to 54%. Almost all GLZM and NGTDM features showed COV ≥10% among the scanners. The dependence of fractal dimensions features on FOV was not consistent across different scanners. Conclusion: We concluded that reconstruction FOV greatly influence Radiomics features. The GLZSM and NGTDM are highly sensitive to FOV. funded in part by Grant NIH/NCI R01CA190105-01

Computed tomography imaging parameters for inhomogeneity correction in radiation treatment planning.

Modern treatment planning systems provide accurate dosimetry in heterogeneous media (such as a patient' body) with the help of tissue characterization based on computed tomography (CT) number. However, CT number depends on the type of scanner, tube voltage, field of view (FOV), reconstruction algorithm including artifact reduction and processing filters. The impact of these parameters on CT to electron density (ED) conversion had been subject of investigation for treatment planning in various clinical situations. This is usually performed with a tissue characterization phantom with various density plugs acquired with different tube voltages (kilovoltage peak), FOV reconstruction and different scanners to generate CT number to ED tables. This article provides an overview of inhomogeneity correction in the context of CT scanning and a new evaluation tool, difference volume dose-volume histogram (DVH), dV-DVH. It has been concluded that scanner and CT parameters are important for tissue characterizations, but changes in ED are minimal and only pronounced for higher density materials. For lungs, changes in CT number are minimal among scanners and CT parameters. Dosimetric differences for lung and prostate cases are usually insignificant (<2%) in three-dimensional conformal radiation therapy and < 5% for intensity-modulated radiation therapy (IMRT) with CT parameters. It could be concluded that CT number variability is dependent on acquisition parameters, but its dosimetric impact is pronounced only in high-density media and possibly in IMRT. In view of such small dosimetric changes in low-density medium, the acquisition of additional CT data for financially difficult clinics and countries may not be warranted.

Aliased noise in X-ray CT images and band-limiting processing as a preventive measure.

X-ray CT projection data often include components with frequencies that are markedly higher than the pixel Nyquist frequency f PN, which is determined by the pixel size. Noise components higher than f PN are folded back into a region lower than f PN through the backprojection process, thereby creating aliased noise. With clinical CT scanners, we evaluated the aliased noise using an aliasing prevention measure, band-limiting processing (BLP), which suppresses frequency components higher than f PN in the projection data. Indices we used to evaluate improvement by BLP were the noise power spectrum (NPS), modulation transfer function (MTF), signal-to-noise-ratio (SNR) spectrum, matched filter SNR (MF SNR), and two-alternative forced-choice (2-AFC) test. With BLP, the NPS was decreased not only beyond f PN, but also within f PN. The same level of MTF was maintained as that without BLP within f PN. No remarkable reduction in spatial resolution was observed. The SNR spectrum and the MF SNR of the BLP image nearly agreed with those of an ideal state without aliased noise. A notable improvement in the visuoperceptual image quality by BLP was recognized with a reconstruction field of view (FOV) of more than 45cm. We then applied BLP to clinical data and confirmed that significant aliased noise of a large FOV image was removed without notable side effects. The results showed that at least some CTs suffering from aliased noise can be improved by proper band-limiting.

First pass cable artefact correction for cardiac C-arm CT imaging

Cardiac C-arm CT imaging delivers a tomographic region-of-interest reconstruction of the patient's heart during image guided catheter interventions. Due to the limited size of the flat detector a volume image is reconstructed, which is truncated in the cone-beam (along the patient axis) and the fan-beam (in the transaxial plane) direction. To practically address this local tomography problem correction methods, like projection extension, are available for first pass image reconstruction. For second pass correction methods, like metal artefact reduction, alternative correction schemes are required when the field of view is limited to a region-of-interest of the patient. In classical CT imaging metal artefacts are corrected by metal identification in a first volume reconstruction and generation of a corrected projection data set followed by a second reconstruction. This approach fails when the metal structures are located outside the reconstruction field of view. When a C-arm CT is performed during a cardiac intervention pacing leads and other cables are frequently positioned on the patients skin, which results in propagating streak artefacts in the reconstruction volume. A first pass approach to reduce this type of artefact is introduced and evaluated here. It makes use of the fact that the projected position of objects outside the reconstruction volume changes with the projection perspective. It is shown that projection based identification, tracking and removal of high contrast structures like cables, only detected in a subset of the projections, delivers a more consistent reconstruction volume with reduced artefact level. The method is quantitatively evaluated based on 50 simulations using cardiac CT data sets with variable cable positioning. These data sets are forward projected using a C-arm CT system geometry and generate artefacts comparable to those observed in clinical cardiac C-arm CT acquisitions. A C-arm CT simulation of every cardiac CT data set without cables served as a ground truth. The 3D root mean square deviation between the simulated data set with and without cables could be reduced for 96% of the simulated cases by an average of 37% (min −9%, max 73%) when using the first pass correction method. In addition, image quality improvement is demonstrated for clinical whole heart C-arm CT data sets when the cable removal algorithm was applied.

SU‐F‐18C‐03: Loss of Image Sharpness in CT Bone Imaging Due to Positioning Within the Scan Field of View – Possible Solutions

Purpose:To demonstrate how both the positioning of an object in the scan field of view (SFOV) and the size of the reconstructed field of view (RFOV) can both significantly affect and degrade the image sharpness.Methods:Clinical CT scans of the shoulders and elbows were inspected for image sharpness. GE scanners were used for the imaging with a “Bone Plus” reconstruction algorithm. In all scans the anatomy of interest was significantly displaced from the center of the SFOV (away from the scanner iso‐center). Radial and azimuthal image sharpness measurements using the Modulation Transfer Function (MTF) were made using the same RFOV at the center of the SFOV and at varying distances from the center. These MTF measurements were also made with varying values of the RFOV.Results:Clinical images with the bone anatomy of interest positioned farther from the center of the SFOV showed increasing image softness occasionally to the point of being non‐diagnostic. The MTF measurements showed that the resolution degraded significantly, by more than a factor of 2, moving from the center toward the periphery of the SFOV: from 12 cycles/cm to less than 6 cycles/cm at distances of 15 cm or more from the center of the SFOV. Increasing the RFOV beyond 20 cm also degraded the resolution, though less significantly.Conclusion:When preservation of bone detail is important one must not only use the proper bone reconstruction algorithm, but also must keep the body part as close to the center of the SFOV as possible. Though this may be difficult with some imaging tasks, such as imaging of the shoulder and elbow, every effort should be made to this end. For the best resolution, the RFOV should also be kept small, ideally to 20 cm or less.Partial research funding from GE HealthCare.

Analysis of modal tomography for three-dimensional wavefront sensing of atmosphere turbulence

Three-dimensional (3D) wavefront sensing of atmosphere turbulence is a key step to realize multi-conjugate adaptive optics technology. In this article, model tomography, which is one of the most important algorithms for 3D wavefront sensing, is analyzed in theory, the principle limitation of model tomography is proposed. Based on this view the reason for tomography error is discussed, and the simulation results for different errors are produced finally. The analysis shows that part Zernike model basis is used as a new basis of polynomial decomposition in model tomography, which leads to modal coupling error and aliasing error. The un-correlation of part Zernike model basis is the prerequisite to avoid modal aliasing error, but modal coupling error cannot be removed and we can only restrain its influence. Combined with simulation result a method with large field of view (FOV) sensing and small FOV reconstruction is proposed and gives a good performance to control modal coupling error.

SU‐E‐I‐110: Do Reconstruction Parameters Affect CT‐Based Kidney Stone Volume Quantification?

Purpose: To investigate the effects of reconstruction field‐of‐view (FOV), slice increment and kernel on the quantification of kidney stone volume. Methods: Three concentrations (200, 400 and 800 mg/cc) and two sizes (21.87 and 99.9 mm3) of hydroxyapatite (HA: Ca5(PO4)OH) cylinders were used to simulate kidney stones of 3 and 5 mm diameter, respectively). These were submerged in a 30 cm wide water phantom and scanned on a dual‐ source CT scanner operating in dual‐energy mode. Reconstruction was performed with a) FOVs from 50 to 300 mm at 50 mm interval; b) image reconstruction increments of 0.1, 0.3 and 0.6 mm; c) 6 recon kernels, from very smooth to medium sharp, all using a 0.6 mm image thickness. CT data were segmented and volumes calculated using volume‐rendered 3D images of each stone, commercial software (Analyze, version10.0) and fixed CT number thresholds (130, 250 and 550 HU for HA200, HA400 and HA800, respectively). Differences in volume between measured and reference values, normalized to the reference values were calculated. The range (maximum — minimum) in normalized volume differences were used to assess the overall influence of various reconstruction parameters. Results: Although normalized volume differences varied with cylinder concentration and size, the differences among FOVs were not substantial (&lt;10.61%). The normalized volume differences among image increments were very small (&lt; 2.45%), except for the lowest concentration/smallest size (differences up to 8.14%), where the smallest image increment was most accurate (differences &lt; 3.02%). Very smooth kernels decreased volume accuracy by as much as 16.19%. Conclusions: Reducing reconstruction FOV did not increase accuracy of volume measurements. Overlap between reconstructed images was beneficial for volume measurement of low concentrations of HA (200 mg/cc) and small size (3 mm diameter) objects. Very smooth kernels were not suitable for volume measurement due to too much edge blurring.

SU‐EE‐A4‐03: Metal Artifact Reduction Algorithm for X‐Ray CT Using a Three‐Pass Approach

Purpose: To investigate a three‐pass reconstruction approach for metal artifact reduction in x‐ray CT. Method and Materials: The algorithm consists of: 1) Initial reconstruction from the original sinogram data; 2) Simple thresholding to identify high‐density regions (e.g. metal) that can cause artifacts; 3) Delineation of corresponding regions in the original sinogram that are replaced using linear interpolation; 4) Second reconstruction after the interpolation; 5) All pixels in the second image that lie between −500 and +500 HU are replaced with the mean of these pixels; 6) Rays in the sinogram through the metal are estimated a second time through forward projection of the segmented second image; 7) Third and final reconstruction. To avoid the need for forward projection across the entire native field‐of‐view (FOV) during step 6 above, a double‐wedge filter is applied in the 2DFT space of the sinogram so that objects outside of the reconstruction FOV are filtered out of the original sinogram. If k and p are the view‐angle and fan‐angle frequency variables, respectively, the double‐wedge filter consists of setting to zero all frequencies in the 2DFT of the sinogram for which |k/(k+p)|&gt;R/L, where R is the reconstruction FOV and L is the source‐to‐isocenter distance. Results: The algorithm substantially reduces streak and blooming artifacts that are present in the original reconstruction for three scans with dental fillings, and performs better than linear interpolation across missing regions in the sinogram. The double‐wedge filter is effective in removing contributions to the sinogram from objects outside of the reconstruction FOV. Conclusion: The algorithm is effective for reducing metal artifacts as well as computationally practical. Conflicts of Interest: Funding was provided by GE Healthcare.