Seungryong Cho

Region‐of‐interest image reconstruction with intensity weighting in circular cone‐beam CT for image‐guided radiation therapy

Imaging plays a vital role in radiation therapy and with recent advances in technology considerable emphasis has been placed on cone-beam CT (CBCT). Attaching a kV x-ray source and a flat panel detector directly to the linear accelerator gantry has enabled progress in target localization techniques, which can include daily CBCT setup scans for some treatments. However, with an increasing number of CT scans there is also an increasing concern for patient exposure. An intensity-weighted region-of-interest (IWROI) technique, which has the potential to greatly reduce CBCT dose, in conjunction with the chord-based backprojection-filtration (BPF) reconstruction algorithm, has been developed and its feasibility in clinical use is demonstrated in this article. A nonuniform filter is placed in the x-ray beam to create regions of two different beam intensities. In this manner, regions outside the target area can be given a reduced dose but still visualized with a lower contrast to noise ratio. Image artifacts due to transverse data truncation, which would have occurred in conventional reconstruction algorithms, are avoided and image noise levels of the low- and high-intensity regions are well controlled by use of the chord-based BPF reconstruction algorithm. The proposed IWROI technique can play an important role in image-guided radiation therapy.

Region-of-interest image reconstruction in circular cone-beam microCT

Cone-beam microcomputed tomography (microCT) is one of the most popular choices for small animal imaging which is becoming an important tool for studying animal models with transplanted diseases. Region-of-interest (ROI) imaging techniques in CT, which can reconstruct an ROI image from the projection data set of the ROI, can be used not only for reducing imaging-radiation exposure to the subject and scatters to the detector but also for potentially increasing spatial resolution of the reconstructed images. Increasing spatial resolution in microCT images can facilitate improved accuracy in many assessment tasks. A method proposed previously for increasing CT image spatial resolution entails the exploitation of the geometric magnification in cone-beam CT. Due to finite detector size, however, this method can lead to data truncation for a large geometric magnification. The Feldkamp-Davis-Kress (FDK) algorithm yields images with artifacts when truncated data are used, whereas the recently developed backprojection filtration (BPF) algorithm is capable of reconstructing ROI images without truncation artifacts from truncated cone-beam data. We apply the BPF algorithm to reconstructing ROI images from truncated data of three different objects acquired by our circular cone-beam microCT system. Reconstructed images by use of the FDK and BPF algorithms from both truncated and nontruncated cone-beam data are compared. The results of the experimental studies demonstrate that, from certain truncated data, the BPF algorithm can reconstruct ROI images with quality comparable to that reconstructed from nontruncated data. In contrast, the FDK algorithm yields ROI images with truncation artifacts. Therefore, an implication of the studies is that, when truncated data are acquired with a configuration of a large geometric magnification, the BPF algorithm can be used for effective enhancement of the spatial resolution of a ROI image.

Exact reconstruction of volumetric images in reverse helical cone-beam CT

Helical scanning configuration has been used widely in diagnostic cone-beam computed tomography (CBCT) for acquiring data sufficient for exact image reconstruction over an extended volume. In image-guided radiation therapy (IGRT) and other applications of CBCT, it can be difficult, if not impossible, to implement mechanically a multiple-turn helical trajectory on the imaging systems due to hardware constraints. However, imaging systems in these applications often allow for the implementation of a reverse helical trajectory in which the rotation direction changes between two consecutive turns. Because the reverse helical trajectory satisfies Tuys condition, when projections of the imaged object are nontruncated, it yields data sufficient for exact image reconstruction within the reverse helix volume. The recently developed chord-based algorithms such as the backprojection filtration (BPF) algorithm can readily be applied to reconstructing images on chords of a reverse helical trajectory, and they can thus reconstruct an image within a volume covered by the chords. Conversely, the chord-based algorithms cannot reconstruct images within regions that are not intersected by chords. In a reverse helix volume, as shown below, chordless regions exist in which no images can thus be reconstructed by use of the chord-based algorithms. In this work, based upon Pack-Noos formula, a shift-invariant filtered backprojection (FBP) algorithm is derived for exact image reconstruction within the reverse helix volume, including the chordless region. Numerical studies have also been conducted to demonstrate the chordless region in a reverse helix volume and to validate the FBP algorithm for image reconstruction within the chordless region. Results of the numerical studies confirm that the FBP algorithm can exactly reconstruct an image within the entire reverse helix volume, including the chordless region. It is relatively straightforward to extend the FBP algorithm to reconstruct images for general trajectories, including reverse helical trajectories with variable pitch, tilted axis, and/or additional segments between turns.

Energy-Efficient Probabilistic Routing Algorithm for Internet of Things

In the future network with Internet of Things (IoT), each of the things communicates with the others and acquires information by itself. In distributed networks for IoT, the energy efficiency of the nodes is a key factor in the network performance. In this paper, we propose energy-efficient probabilistic routing (EEPR) algorithm, which controls the transmission of the routing request packets stochastically in order to increase the network lifetime and decrease the packet loss under the flooding algorithm. The proposed EEPR algorithm adopts energy-efficient probabilistic control by simultaneously using the residual energy of each node and ETX metric in the context of the typical AODV protocol. In the simulations, we verify that the proposed algorithm has longer network lifetime and consumes the residual energy of each node more evenly when compared with the typical AODV protocol.

A BPF‐FBP tandem algorithm for image reconstruction in reverse helical cone‐beam CT

PURPOSE Reverse helical cone-beam computed tomography (CBCT) is a scanning configuration for potential applications in image-guided radiation therapy in which an accurate anatomic image of the patient is needed for image-guidance procedures. The authors previously developed an algorithm for image reconstruction from nontruncated data of an object that is completely within the reverse helix. The purpose of this work is to develop an image reconstruction approach for reverse helical CBCT of a long object that extends out of the reverse helix and therefore constitutes data truncation. METHODS The proposed approach comprises of two reconstruction steps. In the first step, a chord-based backprojection-filtration (BPF) algorithm reconstructs a volumetric image of an object from the original cone-beam data. Because there exists a chordless region in the middle of the reverse helix, the image obtained in the first step contains an unreconstructed central-gap region. In the second step, the gap region is reconstructed by use of a Pack-Noo-formula-based filteredback-projection (FBP) algorithm from the modified cone-beam data obtained by subtracting from the original cone-beam data the reprojection of the image reconstructed in the first step. RESULTS The authors have performed numerical studies to validate the proposed approach in image reconstruction from reverse helical cone-beam data. The results confirm that the proposed approach can reconstruct accurate images of a long object without suffering from data-truncation artifacts or cone-angle artifacts. CONCLUSIONS They developed and validated a BPF-FBP tandem algorithm to reconstruct images of a long object from reverse helical cone-beam data. The chord-based BPF algorithm was utilized for converting the long-object problem into a short-object problem. The proposed approach is applicable to other scanning configurations such as reduced circular sinusoidal trajectories.

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

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

Prior-image-based few-view cone beam CT for applications to daily scan in image-guided radiation therapy: preliminary study

Interfraction motion of a treatment target such as the prostate in radiation therapy (RT) is, in part, responsible for large planning target volume (PTV) margins and related side effects. Online adjustment of the treatment based on timely cone-beam CT (CBCT) images can be particularly useful for patients with large interfraction motion. However, radiation dose to the patient due to frequent CBCT poses a radiation safety concern. One unique feature of CBCT for interfraction motion detection is the availability of a prior anatomical image most of which has not changed. We propose an iterative algorithm, for image reconstruction from a very limited number of projections in CBCT, that is based on total variation (TV) minimization subject to the constraints of data fidelity and positivity and that utilizes anatomical image prior information. Numerical studies for a 2D fan-beam geometry suggests the proposed algorithm can potentially contribute to lowering the radiation dose to the patient by allowing satisfactory image reconstruction from a very limited number of projections.

Image reconstruction in reduced circular sinusoidal cone-beam CT

Non-circular scanning geometries such as helix or circular sinusoid have been used or proposed for cone-beam computed tomography (CBCT), because they provide sufficient data for numerically stable and exact image reconstruction within the scanned volume. Analytic algorithms have been developed for image reconstruction from cone-beam data acquired with a full-scan circular sinusoidal trajectory. In this work, we propose an innovative imaging approach in which a reduced-scan circular sinusoidal trajectory is used for acquiring data sufficient for exact 3D image reconstruction. A filtered backprojection (FBP) algorithm based on Pack-Noos reconstruction formula is applied for image reconstruction in reduced-scan circular sinusoidal scans. We have conducted numerical studies to demonstrate the reduced-scan approach and to validate the FBP reconstruction algorithm in the proposed approach. The proposed scanning method can contribute to increasing the throughput of a scanner, while improving the image quality compared to a conventional circular scan.

Initial performance evaluation of a modular, large-area detector PET scanner for small animal imaging

The biophysical conditions under which most positron emission tomography (PET) imaging of small animals takes place are often poorly characterized and highly variable. And as such, one frequently needs to balance the desire to achieve high spatial resolution with the need to, for example, capture transient phenomena or survey large regions in brief periods. Since these tradeoffs are application dependent, it is desirable that such imaging systems be flexible for meeting varying needs in imaging performance. To this end we describe a PET system under development that incorporates modular detectors and provides application-specific scanning configurations. Our current prototype consists of two opposing flat-panel single-layer LSO detector heads from a high resolution research tomograph system - with adjustable detector separation, energy and coincidence window widths, and scan geometry. Preliminary measurements demonstrate a spatial resolution of 1.6 mm and an absolute detection sensitivity of ~8% at the center of the prototype. For mouse-sized phantoms and 7 cm detector separation, the 250-750 keV energy window was found to yield the optimal noise equivalent count rate curve

Non-circular cone beam CT trajectories: A preliminary investigation on a clinical scanner

The use of cone beam CT (CBCT) image guidance in interventional and therapeutic procedures is becoming increasingly common. Clinical systems consist of a kV x-ray source mounted opposite a flat panel digital detector often on a C-arm system. The source and detector typically rotate in a circle about the patient. However data acquired in such a circular cone beam manner provides insufficient coverage of the object and thus the reconstructed image volume is degraded by artifacts. Several combinations of trajectories and exact analytical reconstruction algorithms have been proposed to overcome these artifacts, however to the knowledge of the authors few if any of these have been studied with data acquired from a real system, with the exception of the widely used standard helical trajectory. An apparatus for performing non-circular scans without altering a clinical scanner has been constructed and the accuracy of achieving a specified trajectory verified. A Defrise style disk phantom was fabricated to test the reduction of cone angle artifacts. Reconstruction results are shown.