BRIGHTVIEW XCT PDF

Find out about the Philips BrightView XCT system for SPECT/CT scanning at the Cancer Center of Santa Barbara. Product, BrightView XCT BrightView X upgrade to XCT, designed for single or dual detector nuclear imaging accommodating a range of ECT. Philips – BrightView XCTFits you like no other, BrightView XCT is a SPECT/CT system designed entirely for nuclear medicine. We took a deeper understanding .

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Da Silva, PhD, H. Sowards-Emmerd, PhD, and L. We summarize the current methods for X-ray data processing and reconstruction, including iterative reconstruction.

System performance measurements and example clinical cases are shown. This setup reduces room size requirements and system weight compared to using a separate CT gantry. Larger axial extents are imaged in a step-and-shoot fashion, by combining volumes from several XCT spins.

The flat-panel based system enables reconstruction xvt a 1 mm isotropic voxel size for the entire FoV and as small as 0. Either or X-ray projections are acquired for each spin during 12, 24, or 60 second rotation time depending on the protocol.

These rotation speeds allow for flexible breathing protocols: Since respiratory motion and peristalsis cause the structures in the chest, abdomen and pelvis to change position and shape, the best image quality for localization studies of the chest, abdomen and pelvis will be achieved during breath holding using the 12 second rotation. Shallow tidal breathing is usually adequate for extremity and head studies, using either the 12 or 24 second rotation, since these areas are generally not affected by respiratory motion.

For attenuation correction scans in myocardial perfusion studies, shallow tidal breathing with the 60 second rotation is preferred in order to average the attenuation data over multiple respiratory cycles to match the position of the heart during the SPECT study. Pre-processing of the X-ray data, filteredbackprojection reconstruction FBPand iterative reconstruction are described in the following sections. Pre- and post-processing The X-ray projection data of an XCT acquisition must be pre-processed prior to reconstruction, to calibrate system characteristics and to compensate for various physical effects: Detector offset, uniformity, and gain are corrected using dark detector frames and air scans as reference.

Defective detector pixels are masked. Incident intensity I 0 calibration: The measured projection values are normalized to the incident X-ray intensity as determined from calibration air scans. Projection regions with counts below a defined threshold are filtered to reduce streak artifacts in the reconstruction. Higher-count regions remain unchanged, to retain detail and sharpness. The data are corrected for blurring effects that occur close to the X-ray source and in the detector.

Scattered radiation in the X-ray projections is subtracted using an object-dependent scatter estimation and correction method. An object-dependent beam-hardening correction compensates for spectral characteristics of the source spectrum and of the absorption in the scanned object. A smoothing filter is applied to the data to reduce noise. Two default filter values are provided, one optimized for soft tissue imaging and the other for bone imaging. However, the filter sharpness can be adjusted by the user to achieve an optimum trade-off between noise reduction and sharpness for a given application.

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The system geometry for each projection, i. After reconstruction, ring artifacts are removed from the volume image in a post-processing step. Finally, the intensity of the volume image is calibrated to improve the accuracy of reported Hounsfield Unit HU values. Filtered-back projection reconstruction Almost all current CT scanners employ a filtered-back-projection method FBP for reconstruction.

For the BrightView XCT, with its offset-detector acquisition geometry, a special FBP method is used, 4 which is a variant of the widely used Feldkamp reconstruction method. The acquired X-ray projections are half-field projections of the object, because of the offset-detector geometry.

The resulting truncation in the object center is compensated by combining each projection with one from the respective opposite gantry angle. On the combined projection, ramp filtering is performed.

Additionally, a weighting function is applied to handle the central overlap region small orange disk in Figure 1.

BrightView X and XCT – Philips Healthcare – PDF Catalogs | Technical Documentation

The projection extension is discarded by the weighting. The thus prepared projections are back-projected with the Feldkamp method to generate the volume image.

Outline of the unique steps required for the FBP reconstruction method.

brigthview FBP reconstruction provides a quick volume image for a first review of the acquired data. It can also be used for high-resolution sub-volume reconstruction of a region of interest. Brightvisw, the method relies on certain mathematical assumptions relating to the cone-beam geometry and the truncation compensation at the center of the field of view.

These hold only approximately for the XCT acquisition geometry. Deviations can cause artifacts in FBP reconstructions, such as left-right intensity imbalance or HU jumps across borders between consecutive XCT segments. Such problems are mitigated by iterative reconstruction. Iterative reconstruction The basic principle of iterative reconstruction is outlined in Figure 3.

The process starts with an initial estimate of the volume image f. The estimated projections are brightvlew to the measured projection data y, and error projections c are computed from the difference. Here, A T signifies the brigytview transpose of A. The whole process is repeated in an iterative fashion, refining the volume image.

Outline of iterative reconstruction steps: There exist many variants of iterative reconstruction methods, differing mainly in how correction projections c are computed and applied to the volume.

For XCT reconstruction, an iterative method called SART 6 is used to create an initial image estimate, followed by several iterations of an ordered-subset maximum likelihood method. Additionally, the maximum-likelihood method includes a model of the acquisition noise, which can lead to reduced noise in the reconstruction.

Iterative reconstruction is challenging to implement in a clinical setting because it is very computationally intensive. As mitigation, the XCT iterative reconstruction computation was implemented on a graphics processing unit GPUresulting in reconstruction times of less than 5 minutes per XCT segment for 1 mm isotropic voxels, slices.

When this is done, the iterative reconstruction can be computed in the background during the SPECT acquisition, with all datasets being ready for review at the end of the study. Results and discussion System performance In the following, typical XCT system performance measurements are presented.

Noise, spatial uniformity, and water value accuracy can be read from the indicated measurements. The insert layer of the phantom, containing inserts of various materials, is shown in Figure 4.

Measured and expected HU values are summarized in Table 1. It should be noted that the HU specification range for the various materials is not as stringent as with conventional CT scanners. This reconstruction was performed on a 0. Patient studies Figure 6 and Figure 7 show the comparison of the quick review image reconstructed with FBP and the corresponding image obtained with iterative reconstruction for two patient studies.

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The quick review image provides almost immediate feedback to the user allowing them to quickly evaluate the CT acquisition. As illustrated by Figure 6, the quick review generally produces good results although some artifacts such as the knit lines between segments can be quite visible. In contrast, the knit lines are much less obvious and the overall uniformity of the image is improved with the iterative reconstruction.

Quick review image reconstructed with FBP left and corresponding image obtained with iterative reconstruction right. Quick review image reconstructed with FBP top and corresponding image obtained with iterative reconstruction bottom. Figure 7 demonstrates how the approximate nature of the FBP reconstruction algorithm coupled with the offset cone-beam geometry of the XCT system can lead to significant HU non-uniformities causing left to right variations in image intensity.

Philips BrightView XCT

These variations in image intensity tend to accentuate the knit lines. Since the iterative reconstruction algorithm can accurately model the offset cone-beam geometry, these HU non-uniformities are significantly reduced with the brihgtview reconstruction, making the knit lines between XCT segments almost disappear. The effective dose to the patient from the XCT portion of this study bfightview approximately 4.

Focal uptake of I indicates disease. The effective dose xcr the patient from the XCT portion of this study was approximately 3. These figures show high-resolution 0. They were reconstructed with FBP since iterative reconstruction currently supports only voxel sizes of 1 mm or larger. The effective dose to the patient from the XCT portion of this study was approximately 1.

Because these scans are acquired over 60 seconds with the patient breathing, brigbtview is significant blurring of the data due to motion. These scans are smoothed and down-sampled to match the SPECT voxel size during the generation of the attenuation map. Additionally, the image is segmented prior to converting from HU values to attenuation coefficients. Therefore, the non-uniformities in HU values that can exist with FBP reconstructions do not adversely impact the attenuation map. Furthermore, the iterative reconstruction is more sensitive to motion artifacts than the FBP reconstruction.

Image reconstruction with FBP and iterative reconstruction are offered to the user. FBP brightvlew used to provide a quick volume image for a first review of the acquired data.

It is also used for high-resolution 0.

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Iterative reconstruction can be used to improve image quality in localization studies. Acknowledgements We would like to thank the following institutions for providing clinical data: Adaptive streak artifact reduction in computed tomography resulting from excessive X-ray photon noise. Improved Briggtview image quality using a new fully physical imaging chain. Pelc, editors, Medical Imaging Physics of Medical Imaging, Proc. Cone-beam CT from width-truncated projections. Simultaneous algebraic reconstruction technique SART: A superior implementation of the ART algorithm.

Erdogan H and Fessler JA.