Metal artifact correction for x-ray computed tomography using kV and selective MV imaging

A kV-MV approach to CBCT metal artifact reduction using multi-layer MV-CBCT

Objective. To demonstrate that complete cone beam CT (CBCT) scans from both MV-energy and kV-energy LINAC sources can reduce metal artifacts in radiotherapy guidance, while maintaining standard-of-care x-ray doses levels.Approach. MV-CBCT and kV-CBCT scans are acquired at half normal dose. The impact of lowered dose on MV-CBCT data quality is mitigated by the use of a 4-layer MV-imager prototype and reduced LINAC energy settings (2.5 MV) to improve photon capture. Additionally, the MV-CBCT is used to determine the 3D position and pose of metal implants, which in turn is used to guide model-based poly-energetic correction and interleaving of the kV-CBCT and MV-CBCT data. Certain edge-preserving regularization steps incorporated into the model-based correction algorithm further reduce MV data noise.Main results. The method was tested in digital phantoms and a real pelvis phantom with large 2.5″ spherical inserts, emulating hip replacements of different materials. The proposed method demonstrated an appealing compromise between the high contrast of kV-CBCT and low artifact content of MV-CBCT. Contrast-to-noise improved 3-fold compared to MV-CBCT with a clinical 1-layer architecture at matched dose (37 mGy) and edge blur levels. Visual delineation of the bladder and prostate improved noteably over kV- or MV-CBCT alone.Significance. The proposed method demonstrates that a full MV-CBCT scan can be combined with kV-CBCT to reduce metal artifacts without resorting to complicated beam collimation strategies to limit the MV-CBCT dose contribution. Additionally, significant improvements in CNR can be achieved as compared to metal artifact reduction through current clinical MV-CBCT practices.

Non-coplanar lung SABR treatments delivered with a gantry-mounted x-ray tube

Objective.To create two non-coplanar, stereotactic ablative radiotherapy (SABR) lung patient treatment plans compliant with the radiation therapy oncology group (RTOG) 0813 dosimetric criteria using a simple, isocentric, therapy with kilovoltage arcs (SITKA) system designed to provide low cost external radiotherapy treatments for low- and middle-income countries (LMICs).Approach.A treatment machine design has been proposed featuring a 320 kVp x-ray tube mounted on a gantry. A deep learning cone-beam CT (CBCT) to synthetic CT (sCT) method was employed to remove the additional cost of planning CTs. A novel inverse treatment planning approach using GPU backprojection was used to create a highly non-coplanar treatment plan with circular beam shapes generated by an iris collimator. Treatments were planned and simulated using the TOPAS Monte Carlo (MC) code for two lung patients. Dose distributions were compared to 6 MV volumetric modulated arc therapy (VMAT) planned in Eclipse on the same cases for a Truebeam linac as well as obeying the RTOG 0813 protocols for lung SABR treatments with a prescribed dose of 50 Gy.Main results.The low-cost SITKA treatments were compliant with all RTOG 0813 dosimetric criteria. SITKA treatments showed, on average, a 6.7 and 4.9 Gy reduction of the maximum dose in soft tissue organs at risk (OARs) as compared to VMAT, for the two patients respectively. This was accompanied by a small increase in the mean dose of 0.17 and 0.30 Gy in soft tissue OARs.Significance.The proposed SITKA system offers a maximally low-cost, effective alternative to conventional radiotherapy systems for lung cancer patients, particularly in low-income countries. The system's non-coplanar, isocentric approach, coupled with the deep learning CBCT to sCT and GPU backprojection-based inverse treatment planning, offers lower maximum doses in OARs and comparable conformity to VMAT plans at a fraction of the cost of conventional radiotherapy.

Concurrent kilovoltage CBCT imaging and megavoltage beam delivery: suppression of cross-scatter with 2D antiscatter grids and grid-based scatter sampling

Objective. The concept of using kilovoltage (kV) and megavoltage (MV) beams concurrently has potential applications in cone beam computed tomography (CBCT) guided radiation therapy, such as single breath hold scans, metal artifact reduction, and simultaneous imaging during MV treatment delivery. However, MV cross-scatter generated during MV beam delivery degrades CBCT image quality. To address this, a 2D antiscatter grid and a cross-scatter correction method were investigated in the context of high dose MV treatment delivery.Approach. A 3D printed, tungsten 2D antiscatter grid prototype was utilized in kV CBCT scans to reduce MV cross-scatter fluence during concurrent MV beam delivery. Remaining cross-scatter in projections was corrected by using the 2D grid itself as a cross-scatter intensity sampling device, referred to as grid-based scatter sampling (GSS). To test this approach, kV CBCT acquisitions were performed while delivering 6 and 10 MV beams, mimicking high dose rate treatment delivery scenarios. kV and MV beam deliveries were not synchronized to eliminate MV beam delivery interruption. MV cross-scatter suppression performance of the proposed approach was evaluated in projections and CBCT images of phantoms.Main results. 2D grid reduced the intensity of MV cross-scatter in kV projections by a factor of 3 on the average, when compared to conventional antiscatter grid. Remaining cross scatter as measured by the GSS method was within 7% of measured reference intensity values, and subsequently corrected. CBCT image quality was improved substantially during concurrent kV-MV beam delivery. Median Hounsfield Unit (HU) inaccuracy was up to 182 HU without our methods, and it was reduced to a median 6.5 HU with our 2D grid and scatter correction approach. Our methods provided a factor of 2-6 improvement in contrast-to-noise ratio.Significance. This investigation demonstrates the utility of 2D antiscatter grids and grid-based scatter sampling in suppressing MV cross-scatter. Our approach successfully minimized the effects of MV cross-scatter in concurrent kV CBCT imaging and high dose MV treatment delivery scenarios. Hence, robust MV cross-scatter suppression is potentially feasible without MV beam delivery interruption or compromising kV image acquisition rate.

Review of high energy x-ray computed tomography for non-destructive dimensional metrology of large metallic advanced manufactured components

Advanced manufacturing technologies, led by additive manufacturing, have undergone significant growth in recent years. These technologies enable engineers to design parts with reduced weight while maintaining structural and functional integrity. In particular, metal additive manufacturing parts are increasingly used in application areas such as aerospace, where a failure of a mission-critical part can have dire safety consequences. Therefore, the quality of these components is extremely important. A critical aspect of quality control is dimensional evaluation, where measurements provide quantitative results that are traceable to the standard unit of length, the metre. Dimensional measurements allow designers, manufacturers and users to check product conformity against engineering drawings and enable the same quality standard to be used across the supply chain nationally and internationally. However, there is a lack of development of measurement techniques that provide non-destructive dimensional measurements beyond common non-destructive evaluation focused on defect detection. X-ray computed tomography (XCT) technology has great potential to be used as a non-destructive dimensional evaluation technology. However, technology development is behind the demand and growth for advanced manufactured parts. Both the size and the value of advanced manufactured parts have grown significantly in recent years, leading to new requirements of dimensional measurement technologies. This paper is a cross-disciplinary review of state-of-the-art non-destructive dimensional measuring techniques relevant to advanced manufacturing of metallic parts at larger length scales, especially the use of high energy XCT with source energy of greater than 400 kV to address the need in measuring large advanced manufactured parts. Technologies considered as potential high energy x-ray generators include both conventional x-ray tubes, linear accelerators, and alternative technologies such as inverse Compton scattering sources, synchrotron sources and laser-driven plasma sources. Their technology advances and challenges are elaborated on. The paper also outlines the development of XCT for dimensional metrology and future needs.

QCBCT-NET for direct measurement of bone mineral density from quantitative cone-beam CT: a human skull phantom study

The purpose of this study was to directly and quantitatively measure BMD from Cone-beam CT (CBCT) images by enhancing the linearity and uniformity of the bone intensities based on a hybrid deep-learning model (QCBCT-NET) of combining the generative adversarial network (Cycle-GAN) and U-Net, and to compare the bone images enhanced by the QCBCT-NET with those by Cycle-GAN and U-Net. We used two phantoms of human skulls encased in acrylic, one for the training and validation datasets, and the other for the test dataset. We proposed the QCBCT-NET consisting of Cycle-GAN with residual blocks and a multi-channel U-Net using paired training data of quantitative CT (QCT) and CBCT images. The BMD images produced by QCBCT-NET significantly outperformed the images produced by the Cycle-GAN or the U-Net in mean absolute difference (MAD), peak signal to noise ratio (PSNR), normalized cross-correlation (NCC), structural similarity (SSIM), and linearity when compared to the original QCT image. The QCBCT-NET improved the contrast of the bone images by reflecting the original BMD distribution of the QCT image locally using the Cycle-GAN, and also spatial uniformity of the bone images by globally suppressing image artifacts and noise using the two-channel U-Net. The QCBCT-NET substantially enhanced the linearity, uniformity, and contrast as well as the anatomical and quantitative accuracy of the bone images, and demonstrated more accuracy than the Cycle-GAN and the U-Net for quantitatively measuring BMD in CBCT.

Multitask 3D CBCT-to-CT translation and organs-at-risk segmentation using physics-based data augmentation

PURPOSE

In current clinical practice, noisy and artifact-ridden weekly cone beam computed tomography (CBCT) images are only used for patient setup during radiotherapy. Treatment planning is performed once at the beginning of the treatment using high-quality planning CT (pCT) images and manual contours for organs-at-risk (OARs) structures. If the quality of the weekly CBCT images can be improved while simultaneously segmenting OAR structures, this can provide critical information for adapting radiotherapy mid-treatment as well as for deriving biomarkers for treatment response.

METHODS

Using a novel physics-based data augmentation strategy, we synthesize a large dataset of perfectly/inherently registered pCT and synthetic-CBCT pairs for locally advanced lung cancer patient cohort, which are then used in a multitask three-dimensional (3D) deep learning framework to simultaneously segment and translate real weekly CBCT images to high-quality pCT-like images.

RESULTS

We compared the synthetic CT and OAR segmentations generated by the model to real pCT and manual OAR segmentations and showed promising results. The real week 1 (baseline) CBCT images which had an average mean absolute error (MAE) of 162.77 HU compared to pCT images are translated to synthetic CT images that exhibit a drastically improved average MAE of 29.31 HU and average structural similarity of 92% with the pCT images. The average DICE scores of the 3D OARs segmentations are: lungs 0.96, heart 0.88, spinal cord 0.83, and esophagus 0.66.

CONCLUSIONS

We demonstrate an approach to translate artifact-ridden CBCT images to high-quality synthetic CT images, while simultaneously generating good quality segmentation masks for different OARs. This approach could allow clinicians to adjust treatment plans using only the routine low-quality CBCT images, potentially improving patient outcomes. Our code, data, and pre-trained models will be made available via our physics-based data augmentation library, Physics-ArX, at https://github.com/nadeemlab/Physics-ArX.

fastCAT: Fast cone beam CT (CBCT) simulation

PURPOSE

To develop fastCAT, a fast cone-beam computed tomography (CBCT) simulator. fastCAT uses pre-calculated Monte Carlo (MC) CBCT phantom-specific scatter and detector response functions to reduce simulation time for megavoltage (MV) and kilovoltage (kV) CBCT imaging.

METHODS

Pre-calculated x-ray beam energy spectra, detector optical spread functions and energy deposition, and phantom scatter kernels are combined with GPU raytracing to produce CBCT volumes. MV x-ray beam spectra are simulated with EGSnrc for 2.5- and 6 MeV electron beams incident on a variety of target materials and kV x-ray beam spectra are calculated analytically for an x-ray tube with a tungsten anode. Detectors were modeled in Geant4 extended by Topas and included optical transport in the scintillators. Two MV detectors were modeled-a standard Varian AS1200 GOS detector and a novel CWO high detective quantum efficiency detector. A kV CsI detector was also modeled. Energy-dependent scatter kernels were created in Topas for two 16 cm diameter phantoms: A Catphan 515 contrast phantom and an anthropomorphic head phantom. The Catphan phantom contained inserts of 1-5 mm in diameter of six different tissue types: brain, deflated lung, compact and cortical bone, adipose, and B-100.

RESULTS

fastCAT simulations retain high fidelity to measurements and MC simulations: MTF curves were within 3.5% and 1.2% of measured values for the CWO and GOS detectors, respectively. HU values and CNR in a fastCAT Catphan 515 simulation were seen to be within 95% confidence intervals of an equivalent MC simulation for all of the tissues with root mean squared errors less than 16 HU and 1.6 in HU values and CNR comparisons, respectively. The anthropomorphic head phantom CWO detector CBCT image resulted in much higher tissue contrast and lower noise compared to the GOS detector CBCT image. A fastCAT simulation of the Catphan 515 module with an image size of 1024  ×  1024  ×  10 voxels took 61 s on a GPU while the equivalent Topas MC was estimated to take more than 0.3 CPU years.

CONCLUSIONS

We present an open source fast CBCT simulation with high fidelity to MC simulations. The fastCAT python package can be found at https://github.com/jerichooconnell/fastCAT.git.

Synthetic CT generation from CBCT images via unsupervised deep learning

Adaptive-radiation-therapy (ART) is applied to account for anatomical variations observed over the treatment course. Daily or weekly cone-beam computed tomography (CBCT) is commonly used in clinic for patient positioning, but CBCT's inaccuracy in Hounsfield units (HU) prevents its application to dose calculation and treatment planning. Adaptive re-planning can be performed by deformably registering planning CT (pCT) to CBCT. However, scattering artifacts and noise in CBCT decrease the accuracy of deformable registration and induce uncertainty in treatment plan. Hence, generating from CBCT a synthetic CT (sCT) that has the same anatomical structure as CBCT but accurate HU values is desirable for ART. We proposed an unsupervised style-transfer-based approach to generate sCT based on CBCT and pCT. Unsupervised learning was desired because exactly matched CBCT and CT are rarely available, even when they are taken a few minutes apart. In the proposed model, CBCT and pCT are two inputs that provide anatomical structure and accurate HU information, respectively. The training objective function is designed to simultaneously minimize (1) contextual loss between sCT and CBCT to maintain the content and structure of CBCT in sCT and (2) style loss between sCT and pCT to achieve pCT-like image quality in sCT. We used CBCT and pCT images of 114 patients to train and validate the designed model, and another 29 independent patient cases to test the model's effectiveness. We quantitatively compared the resulting sCT with the original CBCT using the deformed same-day pCT as reference. Structure-similarity-index, peak-signal-to-noise-ratio, and mean-absolute-error in HU of sCT were 0.9723, 33.68, and 28.52, respectively, while those of CBCT were 0.9182, 29.67, and 49.90, respectively. We have demonstrated the effectiveness of the proposed model in using CBCT and pCT to synthesize CT-quality images. This model may permit using CBCT for advanced applications such as adaptive treatment planning.

[A Simple Method for Computationally Generating Metal Artifacts in CT Images for Treatment Planning: A Pilot Phantom Study]

PURPOSE

In treatment planning for radiation therapy, the use of computed tomography (CT) images including metal artifacts causes a reduction in the dose calculation accuracy. In clinical practice, the artifacts are manually contoured and assigned an appropriate fixed CT number. To validate the procedure, images taken before and after metal insertion into a patient are required, which may be impractical. We propose a simple method for computationally generating metal artifacts in clinical images.

METHODS

In the proposed method, a clinical image free of metal artifacts is used. To simulate metal inside a patient, CT numbers of a region in the image are replaced with a fixed extremely high value. A sinogram is created by the forward projection of the image. Data values of the sinogram in the metal region are converted into smaller values. From the sinogram, an image including artifacts is reconstructed with the filtered back projection.

RESULTS

The simulated artifacts consisted of dark and bright bands and were observed to be similar to the actual metal artifacts. CT numbers in multiple small regions of interest in the image obtained by the proposed method showed a good agreement with those in the actual image.

CONCLUSION

The proposed method was demonstrated to generate the metal artifacts additionally on the clinical images. The method would be potentially applicable to a validation study for the clinical procedure of manually contouring and assigning CT numbers to metal artifacts.

Clinical translation of a new flat-panel detector for beam's-eye-view imaging

Electronic portal imaging devices (EPIDs) lend themselves to beams-eye view clinical applications, such as tumor tracking, but are limited by low contrast and detective quantum efficiency (DQE). We characterize a novel EPID prototype consisting of multiple layers and investigate its suitability for use under clinical conditions. A prototype multi-layer imager (MLI) was constructed utilizing four conventional EPID layers, each consisting of a copper plate, a Gd2O2S:Tb phosphor scintillator, and an amorphous silicon flat panel array detector. We measured the detector's response to a 6 MV photon beam with regards to modulation transfer function, noise power spectrum, DQE, contrast-to-noise ratio (CNR), signal-to-noise ratio (SNR), and the linearity of the detector's response to dose. Additionally, we compared MLI performance to the single top layer of the MLI and the standard Varian AS-1200 detector. Pre-clinical imaging was done on an anthropomorphic phantom, and the detector's CNR, SNR and spatial resolution were assessed in a clinical environment. Images obtained from spine and liver patient treatment deliveries were analyzed to verify CNR and SNR improvements. The MLI has a DQE(0) of 9.7%, about 5.7 times the reference AS-1200 detector. Improved noise performance largely drives the increase. CNR and SNR of clinical images improved three-fold compared to reference. A novel MLI was characterized and prepared for clinical translation. The MLI substantially improved DQE and CNR performance while maintaining the same resolution. Pre-clinical tests on an anthropomorphic phantom demonstrated improved performance as predicted theoretically. Preliminary patient data were analyzed, confirming improved CNR and SNR. Clinical applications are anticipated to include more accurate soft tissue tracking.

In vivo noninvasive three-dimensional (3D) assessment of microwave thermal ablation zone using non-contrast-enhanced x-ray CT

PURPOSE

To develop an image processing methodology for noninvasive three-dimensional (3D) quantification of microwave thermal ablation zones in vivo using x-ray computed tomography (CT) imaging without injection of a contrast enhancing material.

METHODS

Six microwave (MW) thermal ablation procedures were performed in three pigs. The ablations were performed with a constant heating duration of 8 min and power level of 30 W. During the procedure images from sixty 1 mm thick slices were acquired every 30 s. At the end of all ablation procedures for each pig, a contrast-enhanced scan was acquired for reference. Special algorithms for addressing challenges stemming from the 3D in vivo setup and processing the acquired images were prepared. The algorithms first rearranged the data to account for the oblique needle orientation and for breathing motion. Then, the gray level variance changes were analyzed, and optical flow analysis was applied to the treated volume in order to obtain the ablation contours and reconstruct the ablation zone in 3D. The analysis also included a special correction algorithm for eliminating artifacts caused by proximal major blood vessels and blood flow. Finally, 3D reference reconstructions from the contrast-enhanced scan were obtained for quantitative comparison.

RESULTS

For four ablations located >3 mm from a large blood vessel, the mean dice similarity coefficient (DSC) and the mean absolute radial discrepancy between the contours obtained from the reference contrast-enhanced images and the contours produced by the algorithm were 0.82 ± 0.03 and 1.92 ± 1.47 mm, respectively. In two cases of ablation adjacent to large blood vessels, the average DSC and discrepancy were: 0.67 ± 0.6 and 2.96 ± 2.15 mm, respectively. The addition of the special correction algorithm utilizing blood vessels mapping improved the mean DSC and the mean absolute discrepancy to 0.85 ± 0.02 and 1.19 ± 1.00 mm, respectively.

CONCLUSIONS

The developed algorithms provide highly accurate detailed contours in vivo (average error < 2.5 mm) and cope well with the challenges listed above. Clinical implementation of the developed methodology could potentially provide real time noninvasive 3D accurate monitoring of MW thermal ablation in-vivo, provided that the radiation dose can be reduced.

Quantification of metal artifacts in computed tomography: methodological considerations

Numerous methods for artifact quantification in computed tomography (CT) imaging have been suggested. This study evaluated their utility with regards to correspondence with visual artifact perception and reproducibility. Two titanium rods (5 and 10 mm) were examined with 25 different scanning- and image-reconstruction parameters resulting in different types and extents of artifacts. Four radiologists evaluated every image against each other using an in-house developed software. Rating was repeated two times (2,400 comparisons = 2 times × 4 readers × 300 comparisons). Rankings were combined to obtain a reference ranking. Proposed approaches for artifact quantification include manual measurement of attenuation, standard deviation and noise and sophisticated algorithm-based approaches within the image- and frequency-domain. Two radiologists conducted manual measurements twice while the aforementioned algorithms were implemented within the Matlab-Environment allowing for automated image analysis. The reference ranking was compared to all aforementioned methods for artifact quantification to identify suited approaches. Besides visual analysis, Kappa-statistics and intraclass correlation coefficients (ICC) were used. Intra- and Inter-reader agreements of visual artifact perception were excellent (ICC 0.85-0.92). No quantitative method was able to represent the exact ranking of visually perceived artifacts; however, ICC for manual measurements were low (ICC 0.25-0.97). The method that showed best correspondence and reproducibility used a Fourier-transformed linear ROI and lower-end frequency bins. Automated measurements of artifact extent should be preferred over manual measurements as the latter show a limited reproducibility. One method that allows for automated quantification of such artefacts is made available as an electronic supplement.

Synthetic CT generation from CBCT images via deep learning

PURPOSE

Cone-beam computed tomography (CBCT) scanning is used daily or weekly (i.e., on-treatment CBCT) for accurate patient setup in image-guided radiotherapy. However, inaccuracy of CT numbers prevents CBCT from performing advanced tasks such as dose calculation and treatment planning. Motivated by the promising performance of deep learning in medical imaging, we propose a deep U-net-based approach that synthesizes CT-like images with accurate numbers from planning CT, while keeping the same anatomical structure as on-treatment CBCT.

METHODS

We formulated the CT synthesis problem under a deep learning framework, where a deep U-net architecture was used to take advantage of the anatomical structure of on-treatment CBCT and image intensity information of planning CT. U-net was chosen because it exploits both global and local features in the image spatial domain, matching our task to suppress global scattering artifacts and local artifacts such as noise in CBCT. To train the synthetic CT generation U-net (sCTU-net), we include on-treatment CBCT and initial planning CT of 37 patients (30 for training, seven for validation) as the input. Additional replanning CT images acquired on the same day as CBCT after deformable registration are utilized as the corresponding reference. To demonstrate the effectiveness of the proposed sCTU-net, we use another seven independent patient cases (560 slices) for testing.

RESULTS

We quantitatively compared the resulting synthetic CT (sCT) with the original CBCT image using deformed same-day pCT images as reference. The averaged accuracy measured by mean absolute error (MAE) between sCT and reference CT (rCT) on testing data is 18.98 HU, while MAE between CBCT and rCT is 44.38 HU.

CONCLUSIONS

The proposed sCTU-net can synthesize CT-quality images with accurate CT numbers from on-treatment CBCT and planning CT. This potentially enables advanced CBCT applications for adaptive treatment planning.

Automated ultrafast kilovoltage-megavoltage cone-beam CT for image guided radiotherapy of lung cancer: System description and real-time results

PURPOSE

To establish a fully automated kV-MV CBCT imaging method on a clinical linear accelerator that allows image acquisition of thoracic targets for patient positioning within one breath-hold (∼15s) under realistic clinical conditions.

METHODS AND MATERIALS

Our previously developed FPGA-based hardware unit which allows synchronized kV-MV CBCT projection acquisition is connected to a clinical linear accelerator system via a multi-pin switch; i.e. either kV-MV imaging or conventional clinical mode can be selected. An application program was developed to control the relevant linac parameters automatically and to manage the MV detector readout as well as the gantry angle capture for each MV projection. The kV projections are acquired with the conventional CBCT system. GPU-accelerated filtered backprojection is performed separately for both data sets. After appropriate grayscale normalization both modalities are combined and the final kV-MV volume is re-imported in the CBCT system to enable image matching. To demonstrate adequate geometrical accuracy of the novel imaging system the Penta-Guide phantom QA procedure is performed. Furthermore, a human plastinate and different tumor shapes in a thorax phantom are scanned. Diameters of the known tumor shapes are measured in the kV-MV reconstruction.

RESULTS

An automated kV-MV CBCT workflow was successfully established in a clinical environment. The overall procedure, from starting the data acquisition until the reconstructed volume is available for registration, requires ∼90s including 17s acquisition time for 100° rotation. It is very simple and allows target positioning in the same way as for conventional CBCT. Registration accuracy of the QA phantom is within ±1mm. The average deviation from the known tumor dimensions measured in the thorax phantom was 0.7mm which corresponds to an improvement of 36% compared to our previous kV-MV imaging system.

CONCLUSIONS

Due to automation the kV-MV CBCT workflow is speeded up by a factor of >10 compared to the manual approach. Thus, the system allows a simple, fast and reliable imaging procedure and fulfills all requirements to be successfully introduced into the clinical workflow now, enabling single-breath-hold volume imaging.

Optical flow and image segmentation analysis for noninvasive precise mapping of microwave thermal ablation in X-ray CT scans - ex vivo study

PURPOSE

To develop image processing algorithms for noninvasive mapping of microwave thermal ablation using X-ray CT.

METHODS

Ten specimens of bovine liver were subjected to microwave ablation (20-80 W, 8 min) while scanned by X-ray CT at 5 s intervals. Specimens were cut and manually traced by two observers. Two algorithms were developed and implemented to map the ablation zone. The first algorithm utilises images segmentation of Hounsfield units changes (ISHU). The second algorithm utilises radial optical flow (ROF). Algorithm sensitivity to spatiotemporal under-sampling was assessed by decreasing the acquisition rate and reducing the number of acquired projections used for image reconstruction in order to evaluate the feasibility of implementing radiation reduction techniques.

RESULTS

The average radial discrepancy between the ISHU and ROF contours and the manual tracing were 1.04±0.74 and 1.16±0.79mm, respectively. When diluting the input data, the ISHU algorithm retained its accuracy, ranging from 1.04 to 1.79mm. By contrast, the ROF algorithm performance became inconsistent at low acquisition rates. Both algorithms were not sensitive to projections reduction, (ISHU: 1.24±0.83mm, ROF: 1.53±1.15mm, for reduction by eight fold). Ablations near large blood vessels affected the ROF algorithm performance (1.83±1.30mm; p < 0.01), whereas ISHU performance remained the same.

CONCLUSION

The two suggested noninvasive ablation mapping algorithms can provide highly accurate contouring of the ablation zone at low scan rates. The ISHU algorithm may be more suitable for clinical practice as it appears more robust when radiation dose reduction strategies are employed and when the ablation zone is near large blood vessels.

Image-based Metal Artifact Reduction in X-ray Computed Tomography utilizing Local Anatomical Similarity

X-ray computed tomography (CT) is widely used in radiation therapy treatment planning in recent years. However, metal implants such as dental fillings and hip prostheses can cause severe bright and dark streaking artifacts in reconstructed CT images. These artifacts decrease image contrast and degrade HU accuracy, leading to inaccuracies in target delineation and dose calculation. In this work, a metal artifact reduction method is proposed based on the intrinsic anatomical similarity between neighboring CT slices. Neighboring CT slices from the same patient exhibit similar anatomical features. Exploiting this anatomical similarity, a gamma map is calculated as a weighted summation of relative HU error and distance error for each pixel in an artifact-corrupted CT image relative to a neighboring, artifact-free image. The minimum value in the gamma map for each pixel is used to identify an appropriate pixel from the artifact-free CT slice to replace the corresponding artifact-corrupted pixel. With the proposed method, the mean CT HU error was reduced from 360 HU and 460 HU to 24 HU and 34 HU on head and pelvis CT images, respectively. Dose calculation accuracy also improved, as the dose difference was reduced from greater than 20% to less than 4%. Using 3%/3mm criteria, the gamma analysis failure rate was reduced from 23.25% to 0.02%. An image-based metal artifact reduction method is proposed that replaces corrupted image pixels with pixels from neighboring CT slices free of metal artifacts. This method is shown to be capable of suppressing streaking artifacts, thereby improving HU and dose calculation accuracy.

Metal artifact reduction in spiral fan-beam CT using a new sinogram segmentation scheme

Objective of this study is to present and test a new method for metal artifact reduction (MAR) by segmenting raw CT data (sinogram). The artifact suppression technique incorporates two steps namely, metal projection segmentation in the sinogram and replacement of segmented regions by new values using an interpolation method. The proposed segmentation algorithm uses the sinogram instead of reconstructed CT slices. First, one of the best and newest region-based geometric active contour models is used to detect projection data affected by metal objects (missing projections). Then, the Hough-transform method is applied to detect all sinusoidal-like curves belonging to metal objects. Finally, a post image processing technique is used aiming to increase accuracy of the segmentation process. To provide a proof of performance, CT data of two patients with metallic teeth filling and pelvis prosthesis were included in the study as well as CT data of a phantom with metallic teeth inserts. Accuracy was determined by comparing mean, variance, mean squared error (MSE) and, peak signal to noise ratio (PSNR) as evaluation measurements of distortion in phantom images with respect to metallic teeth (original and suppressed) and without metallic teeth inserts. Quantitative results showed an average improvement of 12 dB in terms of PSNR and 517 in terms of MSE when the new MAR method was applied to remove metal artifacts. Qualitative improvement was also assessed by comparing uncorrected clinical images with artifact suppressed images. Moreover, qualitative comparison of the results of the proposed new method with the existing methods of MAR showed the superiority of the new method tested in this study.

Metal artifact reduction through MVCBCT and kVCT in radiotherapy

This study proposes a new method for removal of metal artifacts from megavoltage cone beam computed tomography (MVCBCT) and kilovoltage CT (kVCT) images. Both images were combined to obtain prior image, which was forward projected to obtain surrogate data and replace metal trace in the uncorrected kVCT image. The corrected image was then reconstructed through filtered back projection. A similar radiotherapy plan was designed using the theoretical CT image, the uncorrected kVCT image, and the corrected image. The corrected images removed most metal artifacts, and the CT values were accurate. The corrected image also distinguished the hollow circular hole at the center of the metal. The uncorrected kVCT image did not display the internal structure of the metal, and the hole was misclassified as metal portion. Dose distribution calculated based on the corrected image was similar to that based on the theoretical CT image. The calculated dose distribution also evidently differed between the uncorrected kVCT image and the theoretical CT image. The use of the combined kVCT and MVCBCT to obtain the prior image can distinctly improve the quality of CT images containing large metal implants.

High-kVp Assisted Metal Artifact Reduction for X-ray Computed Tomography

In X-ray computed tomography (CT), the presence of metallic parts in patients causes serious artifacts and degrades image quality. Many algorithms were published for metal artifact reduction (MAR) over the past decades with various degrees of success but without a perfect solution. Some MAR algorithms are based on the assumption that metal artifacts are due only to strong beam hardening and may fail in the case of serious photon starvation. Iterative methods handle photon starvation by discarding or underweighting corrupted data, but the results are not always stable and they come with high computational cost. In this paper, we propose a high-kVp-assisted CT scan mode combining a standard CT scan with a few projection views at a high-kVp value to obtain critical projection information near the metal parts. This method only requires minor hardware modifications on a modern CT scanner. Two MAR algorithms are proposed: dual-energy normalized MAR (DNMAR) and high-energy embedded MAR (HEMAR), aiming at situations without and with photon starvation respectively. Simulation results obtained with the CT simulator CatSim demonstrate that the proposed DNMAR and HEMAR methods can eliminate metal artifacts effectively.

Generation of hybrid sinograms for the recovery of kV-CT images with metal artifacts for helical tomotherapy

PURPOSE

The overall goal of this study is to restore kilovoltage computed tomography (kV-CT) images which are disfigured by patients' metal prostheses. By generating a hybrid sinogram that is a combination of kV and megavoltage (MV) projection data, the authors suggest a novel metal artifact-reduction (MAR) method that retains the image quality to match that of kV-CT and simultaneously restores the information of metal prostheses lost due to photon starvation.

METHODS

CT projection data contain information about attenuation coefficients and the total length of the attenuation. By normalizing raw kV projections with their own total lengths of attenuation, mean attenuation projections were obtained. In the same manner, mean density projections of MV-CT were obtained by the normalization of MV projections resulting from the forward projection of density-calibrated MV-CT images with the geometric parameters of the kV-CT device. To generate the hybrid sinogram, metal-affected signals of the kV sinogram were identified and replaced by the corresponding signals of the MV sinogram following a density calibration step with kV data. Filtered backprojection was implemented to reconstruct the hybrid CT image. To validate the authors' approach, they simulated four different scenarios for three heads and one pelvis using metallic rod inserts within a cylindrical phantom. Five inserts describing human body elements were also included in the phantom. The authors compared the image qualities among the kV, MV, and hybrid CT images by measuring the contrast-to-noise ratio (CNR), the signal-to-noise ratio (SNR), the densities of all inserts, and the spatial resolution. In addition, the MAR performance was compared among three existing MAR methods and the authors' hybrid method. Finally, for clinical trials, the authors produced hybrid images of three patients having dental metal prostheses to compare their MAR performances with those of the kV, MV, and three existing MAR methods.

RESULTS

The authors compared the image quality and MAR performance of the hybrid method with those of other imaging modalities and the three MAR methods, respectively. The total measured mean of the CNR (SNR) values for the nonmetal inserts was determined to be 14.3 (35.3), 15.3 (37.8), and 25.5 (64.3) for the kV, MV, and hybrid images, respectively, and the spatial resolutions of the hybrid images were similar to those of the kV images. The measured densities of the metal and nonmetal inserts in the hybrid images were in good agreement with their true densities, except in cases of extremely low densities, such as air and lung. Using the hybrid method, major streak artifacts were suitably removed and no secondary artifacts were introduced in the resultant image. In clinical trials, the authors verified that kV and MV projections were successfully combined and turned into the resultant hybrid image with high image contrast, accurate metal information, and few metal artifacts. The hybrid method also outperformed the three existing MAR methods with regard to metal information restoration and secondary artifact prevention.

CONCLUSIONS

The authors have shown that the hybrid method can restore the overall image quality of kV-CT disfigured by severe metal artifacts and restore the information of metal prostheses lost due to photon starvation. The hybrid images may allow for the improved delineation of structures of interest and accurate dose calculations for radiation treatment planning for patients with metal prostheses.

A piecewise-focused high DQE detector for MV imaging

PURPOSE

Electronic portal imagers (EPIDs) with high detective quantum efficiencies (DQEs) are sought to facilitate the use of the megavoltage (MV) radiotherapy treatment beam for image guidance. Potential advantages include high quality (treatment) beam's eye view imaging, and improved cone-beam computed tomography (CBCT) generating images with more accurate electron density maps with immunity to metal artifacts. One approach to increasing detector sensitivity is to couple a thick pixelated scintillator array to an active matrix flat panel imager (AMFPI) incorporating amorphous silicon thin film electronics. Cadmium tungstate (CWO) has many desirable scintillation properties including good light output, a high index of refraction, high optical transparency, and reasonable cost. However, due to the 0 1 0 cleave plane inherent in its crystalline structure, the difficulty of cutting and polishing CWO has, in part, limited its study relative to other scintillators such as cesium iodide and bismuth germanate (BGO). The goal of this work was to build and test a focused large-area pixelated "strip" CWO detector.

METHODS

A 361 × 52 mm scintillator assembly that contained a total of 28 072 pixels was constructed. The assembly comprised seven subarrays, each 15 mm thick. Six of the subarrays were fabricated from CWO with a pixel pitch of 0.784 mm, while one array was constructed from BGO for comparison. Focusing was achieved by coupling the arrays to the Varian AS1000 AMFPI through a piecewise linear arc-shaped fiber optic plate. Simulation and experimental studies of modulation transfer function (MTF) and DQE were undertaken using a 6 MV beam, and comparisons were made between the performance of the pixelated strip assembly and the most common EPID configuration comprising a 1 mm-thick copper build-up plate attached to a 133 mg/cm(2) gadolinium oxysulfide scintillator screen (Cu-GOS). Projection radiographs and CBCT images of phantoms were acquired. The work also introduces the use of a lightweight edge phantom to generate MTF measurements at MV energies and shows its functional equivalence to the more cumbersome slit-based method.

RESULTS

Measured and simulated DQE(0)'s of the pixelated CWO detector were 22% and 26%, respectively. The average measured and simulated ratios of CWO DQE(f) to Cu-GOS DQE(f) across the frequency range of 0.0-0.62 mm(-1) were 23 and 29, respectively. 2D and 3D imaging studies confirmed the large dose efficiency improvement and that focus was maintained across the field of view. In the CWO CBCT images, the measured spatial resolution was 7 lp/cm. The contrast-to-noise ratio was dramatically improved reflecting a 22 × sensitivity increase relative to Cu-GOS. The CWO scintillator material showed significantly higher stability and light yield than the BGO material.

CONCLUSIONS

An efficient piecewise-focused pixelated strip scintillator for MV imaging is described that offers more than a 20-fold dose efficiency improvement over Cu-GOS.