Accuracy of CT perfusion ischemic core volume and location estimation: A comparison between four ischemic core estimation approaches using syngo.via.

BACKGROUND AND OBJECTIVE: Computed tomography perfusion (CTP) is widely used in the evaluation of acute ischemic stroke patients for endovascular thrombectomy (EVT). The stability of CTP core estimation is suboptimal and varies between software packages. We aimed to quantify the volumetric and spatial agreement between the CTP ischemic core and follow-up infarct for four ischemic core estimation approaches using syngo.via. METHODS: We included successfully reperfused, EVT-treated patients with baseline CTP and 24h follow-up diffusion weighted magnetic resonance imaging (DWI) (November 2017-September 2020). Data were processed with syngo.via VB40 using four core estimation approaches based on: cerebral blood volume (CBV)<1.2mL/100mL with and without smoothing filter, relative cerebral blood flow (rCBF)<30%, and rCBF<20%. The follow-up infarct was segmented on DWI. RESULTS: In 59 patients, median estimated CTP core volumes for four core estimation approaches ranged from 12-39 mL. Median 24h follow-up DWI infarct volume was 11 mL. The intraclass correlation coefficient (ICC) showed moderate-good volumetric agreement for all approaches (range 0.61-0.76). Median Dice was low for all approaches (range 0.16-0.21). CTP core overestimation >10mL occurred least frequent (14/59 [24%] patients) using the CBV-based core estimation approach with smoothing filter. CONCLUSIONS: In successfully reperfused patients who underwent EVT, syngo.via CTP ischemic core estimation showed moderate volumetric and spatial agreement with the follow-up infarct on DWI. In patients with complete reperfusion after EVT, the volumetric agreement was excellent. A CTP core estimation approach based on CBV<1.2 mL/100mL with smoothing filter least often overestimated the follow-up infarct volume and is therefore preferred for clinical decision making using syngo.via.

Two methods for reducing moving metal artifacts in cone-beam CT.

PURPOSE: In image-guided radiation therapy, fiducial markers or clips are often used to determine the position of the tumor. These markers lead to streak artifacts in cone-beam CT (CBCT) scans. Standard inpainting-based metal artifact reduction (MAR) methods fail to remove these artifacts in cases of large motion. We propose two methods to effectively reduce artifacts caused by moving metal inserts. METHODS: The first method (MMAR) utilizes a coarse metal segmentation in the image domain and a refined segmentation in the rawdata domain. After an initial reconstruction, metal is segmented and forward projected giving a coarse metal mask in the rawdata domain. Inside the coarse mask, metal is segmented by utilizing a 2D Sobel filter. Metal is removed by linear interpolation in the refined metal mask. The second method (MoCoMAR) utilizes a motion compensation (MoCo) algorithm [Med Phys. 2013;40:101913] that provides us with a motion-free volume (3D) or with a time series of motion-free volumes (4D). We then apply the normalized metal artifact reduction (NMAR) [Med Phys. 2010;37:5482-5493] to these MoCo volumes. Both methods were applied to three CBCT data sets of patients with metal inserts in the thorax or abdomen region and a 4D thorax simulation. The results were compared to volumes corrected by a standard MAR1 [Radiology. 1987;164:576-577]. RESULTS: MMAR and MoCoMAR were able to remove all artifacts caused by moving metal inserts for the patients and the simulation. Both new methods outperformed the standard MAR1, which was only able to remove artifacts caused by metal inserts with little or no motion. CONCLUSIONS: In this work, two new methods to remove artifacts caused by moving metal inserts are introduced. Both methods showed good results for a simulation and three patients. While the first method (MMAR) works without any prior knowledge, the second method (MoCoMAR) requires a respiratory signal for the MoCo step and is computationally more demanding and gives no benefit over MMAR, unless MoCo images are desired.

A modified McKinnon-Bates (MKB) algorithm for improved 4D cone-beam computed tomography (CBCT) of the lung.

PURPOSE: Four-dimensional (4D) cone-beam computed tomography (CBCT) of the lung is an effective tool for motion management in radiotherapy but presents a challenge because of slow gantry rotation times. Sorting the individual projections by breathing phase and using an established technique such as Feldkamp-Davis-Kress (FDK) to generate corresponding phase-correlated (PC) three-dimensional (3D) images results in reconstructions (FDK-PC) that often contain severe streaking artifacts due to the sparse angular sampling distributions. These can be reduced by further slowing down the gantry at the expense of incurring unwanted increases in scan times and dose. A computationally efficient alternative is the McKinnon-Bates (MKB) reconstruction algorithm that has shown promise in reducing view aliasing-induced streaking but can produce ghosting artifacts that reduce contrast and impede the determination of motion trajectories. The purpose of this work was to identify and correct shortcomings in the MKB algorithm. METHODS: In the general MKB approach, a time-averaged 3D prior image is first reconstructed. The prior is then forward-projected at the same angles as the original projection data creating time-averaged reprojections. These reprojections are subsequently subtracted from the original (unblurred) projections to create motion-encoded difference projections. The difference projections are reconstructed into PC difference images that are added to the well-sampled 3D prior to create the higher quality 4D image. The cause of the ghosting in the traditional 4D MKB images was studied and traced to motion-induced streaking in the prior that, when reprojected, has the undesirable effect of re-encoding for motion in what should be a purely time-averaged reprojection. A new method, designated as the modified McKinnon-Bates (mMKB) algorithm, was developed based on destreaking the prior. This was coupled with a postprocessing 4D bilateral filter for noise suppression and edge preservation (mMKBbf ). The algorithms were tested with the 4D XCAT phantom using four simulated scan times (57, 60, 120, 180 s) and with two in vivo thorax studies (acquisition time of 60 and 90 s). Contrast-to-noise ratios (CNRs) of the target lesions and overall visual quality of the images were assessed. RESULTS: Prior destreaking (mMKB algorithm) reduced ghosting artifacts and increased CNRs for all cases, with the biggest impacts seen in the end inhale (EI) and end exhale (EE) phases of the respiratory cycle. For the XCAT phantom, mMKB lesion CNR was 44% higher than the MKB lesion CNR and was 81% higher than the FDK-PC lesion CNR (EI and EE phases). The bilateral filter provided a further average CNR improvement of 87% with the highest increases associated with longer scan times. Across all phases and scan times, the maximum mMKBbf -to-FDK-PC CNR improvement was over 300%. In vivo results agreed with XCAT results. Significantly less ghosting was observed throughout the mMKB images including near the lesions-of-interest and the diaphragm allowing for, in one case, visualization of a small tumor with nearly 30 mm of motion. The maximum FDK-PC-to-MKBbf CNR improvement for Patient 1's lesion was 261% and for Patient 2's lesion was 318%. CONCLUSIONS: The 4D mMKB algorithm yields good quality coronal and sagittal images in the thorax that may provide sufficient information for patient verification.

Acuros CTS: A fast, linear Boltzmann transport equation solver for computed tomography scatter - Part II: System modeling, scatter correction, and optimization.

PURPOSE: To correct for scatter in kV cone-beam CT (CBCT) projection data on a clinical system using a new tool, Acuros® CTS, that estimates scatter images rapidly and accurately by deterministically solving the linear Boltzmann transport equation. METHODS: Phantom and patient CBCT scans were acquired on TrueBeam® radiotherapy machines. A first-pass reconstruction was used to create water and bone density maps of the imaged object, which was updated to include a more accurate representation of the patient couch. The imaging system model accounted for the TrueBeam x-ray source (polychromatic spectrum, beam filtration, bowtie filter, and collimation hardware) and x-ray detection system (antiscatter grid, flat-panel imager). Acuros CTS then used the system and object models to estimate the scatter component of each projection image, which was subtracted from the measured projections. The corrected projections were then reconstructed to produce the final result. We examined the tradeoff between run time and accuracy using a Pareto optimization of key parameters, including the voxel size of the down-sampled object model, the number of pixels in the down-sampled detector, and the number of scatter images (angular down-sampling). All computations and reconstructions were performed on a research workstation containing two graphics processing units (GPUs). In addition, we established a method for selecting a subset of projections for which scatter images were calculated. The projections were selected to minimize interpolation errors in the remaining projections. Image quality improvement was assessed by measuring the accuracy of the reconstructed phantom and patient images. RESULTS: The Pareto optimization yielded a set of parameters with an average run time of 26 seconds for scatter correction while maintaining high accuracy of scatter estimation. This was achieved in part by means of optimizing the projection angles that were processed, thus favoring the use of more angles in the lateral (i.e., horizontal) direction and fewer angles in the AP direction. In a 40 cm solid water phantom reconstruction, nonuniformities were decreased from 217 HU without scatter correction to 51 HU with conventional (kernel-based) scatter correction to 17 HU with Acuros CTS-based scatter correction. In clinical pelvis scans, nonuniformities in the bladder were reduced from 85 HU with conventional scatter correction to 14 HU with Acuros CTS. CONCLUSIONS: Acuros CTS is a promising new tool for fast and accurate scatter correction for CBCT imaging. By carefully modeling the imaging chain and optimizing several parameters, we achieved high correction accuracies with computation times compatible with the clinical workflow. The improvement in image quality enables better soft-tissue visualization and potentially enables applications such as adaptive radiotherapy.

Motion vector field phase-to-amplitude resampling for 4D motion-compensated cone-beam CT.

We propose a phase-to-amplitude resampling (PTAR) method to reduce motion blurring in motion-compensated (MoCo) 4D cone-beam CT (CBCT) image reconstruction, without increasing the computational complexity of the motion vector field (MVF) estimation approach. PTAR is able to improve the image quality in reconstructed 4D volumes, including both regular and irregular respiration patterns. The PTAR approach starts with a robust phase-gating procedure for the initial MVF estimation and then switches to a phase-adapted amplitude gating method. The switch implies an MVF-resampling, which makes them amplitude-specific. PTAR ensures that the MVFs, which have been estimated on phase-gated reconstructions, are still valid for all amplitude-gated reconstructions. To validate the method, we use an artificially deformed clinical CT scan with a realistic breathing pattern and several patient data sets acquired with a TrueBeamTM integrated imaging system (Varian Medical Systems, Palo Alto, CA, USA). Motion blurring, which still occurs around the area of the diaphragm or at small vessels above the diaphragm in artifact-specific cyclic motion compensation (acMoCo) images based on phase-gating, is significantly reduced by PTAR. Also, small lung structures appear sharper in the images. This is demonstrated both for simulated and real patient data. A quantification of the sharpness of the diaphragm confirms these findings. PTAR improves the image quality of 4D MoCo reconstructions compared to conventional phase-gated MoCo images, in particular for irregular breathing patterns. Thus, PTAR increases the robustness of MoCo reconstructions for CBCT. Because PTAR does not require any additional steps for the MVF estimation, it is computationally efficient. Our method is not restricted to CBCT but could rather be applied to other image modalities.

Cardiorespiratory motion-compensated micro-CT image reconstruction using an artifact model-based motion estimation.

PURPOSE: Cardiac in vivo micro-CT imaging of small animals typically requires double gating due to long scan times and high respiratory rates. The simultaneous respiratory and cardiac gating can either be done prospectively or retrospectively. In any case, for true 5D imaging, i.e., three spatial dimensions plus one respiratory-temporal dimension plus one cardiac temporal dimension, the amount of information corresponding to a given respiratory and cardiac phase is orders of magnitude lower than the total amount of information acquired. Achieving similar image quality for 5D than for usual 3D investigations would require increasing the amount of data and thus the applied dose to the animal. Therefore, the goal is phase-correlated imaging with high image quality but without increasing the dose level. METHODS: To achieve this, the authors propose a new image reconstruction algorithm that makes use of all available projection data, also of that corresponding to other motion windows. In particular, the authors apply a motion-compensated image reconstruction approach that sequentially compensates for respiratory and cardiac motion to decrease the impact of sparsification. In that process, all projection data are used no matter which motion phase they were acquired in. Respiratory and cardiac motion are compensated for by using motion vector fields. These motion vector fields are estimated from initial phase-correlated reconstructions based on a deformable registration approach. To decrease the sensitivity of the registration to sparse-view artifacts, an artifact model-based approach is used including a cyclic consistent nonrigid registration algorithm. RESULTS: The preliminary results indicate that the authors' approach removes the sparse-view artifacts of conventional phase-correlated reconstructions while maintaining temporal resolution. In addition, it achieves noise levels and spatial resolution comparable to that of nongated reconstructions due to the improved dose usage. By using the proposed motion estimation, no sensitivity to streaking artifacts has been observed. CONCLUSIONS: Using sequential double gating combined with artifact model-based motion estimation allows to accurately estimate respiratory and cardiac motion from highly undersampled data. No sensitivity to streaking artifacts introduced by sparse angular sampling has been observed for the investigated dose levels. The motion-compensated image reconstruction was able to correct for both, respiratory and cardiac motion, by applying the estimated motion vector fields. The administered dose per animal can thus be reduced for 5D imaging allowing for longitudinal studies at the highest image quality.

Moving metal artifact reduction in cone-beam CT scans with implanted cylindrical gold markers.

PURPOSE: Implanted gold markers for image-guided radiotherapy lead to streaking artifacts in cone-beam CT (CBCT) scans. Several methods for metal artifact reduction (MAR) have been published, but they all fail in scans with large motion. Here the authors propose and investigate a method for automatic moving metal artifact reduction (MMAR) in CBCT scans with cylindrical gold markers. METHODS: The MMAR CBCT reconstruction method has six steps. (1) Automatic segmentation of the cylindrical markers in the CBCT projections. (2) Removal of each marker in the projections by replacing the pixels within a masked area with interpolated values. (3) Reconstruction of a marker-free CBCT volume from the manipulated CBCT projections. (4) Reconstruction of a standard CBCT volume with metal artifacts from the original CBCT projections. (5) Estimation of the three-dimensional (3D) trajectory during CBCT acquisition for each marker based on the segmentation in Step 1, and identification of the smallest ellipsoidal volume that encompasses 95% of the visited 3D positions. (6) Generation of the final MMAR CBCT reconstruction from the marker-free CBCT volume of Step 3 by replacing the voxels in the 95% ellipsoid with the corresponding voxels of the standard CBCT volume of Step 4. The MMAR reconstruction was performed retrospectively using a half-fan CBCT scan for 29 consecutive stereotactic body radiation therapy patients with 2-3 gold markers implanted in the liver. The metal artifacts of the MMAR reconstructions were scored and compared with a standard MAR reconstruction by counting the streaks and by calculating the standard deviation of the Hounsfield units in a region around each marker. RESULTS: The markers were found with the same autosegmentation settings in 27 CBCT scans, while two scans needed slightly changed settings to find all markers automatically in Step 1 of the MMAR method. MMAR resulted in 15 scans with no streaking artifacts, 11 scans with 1-4 streaks, and 3 scans with severe streaking artifacts. The corresponding numbers for MAR were 8 (no streaks), 1 (1-4 streaks), and 20 (severe streaking artifacts). The MMAR method was superior to MAR in scans with more than 8 mm 3D marker motion and comparable to MAR for scans with less than 8 mm motion. In addition, the MMAR method was tested on a 4D CBCT reconstruction for which it worked equally well as for the 3D case. The markers in the 4D case had very low motion blur. CONCLUSIONS: An automatic method for MMAR in CBCT scans was proposed and shown to effectively remove almost all streaking artifacts in a large set of clinical CBCT scans with implanted gold markers in the liver. Residual streaking artifacts observed in three CBCT scans may be removed with better marker segmentation.

Deformable 3D-2D registration for CT and its application to low dose tomographic fluoroscopy.

Many applications in medical imaging include image registration for matching of images from the same or different modalities. In the case of full data sampling, the respective reconstructed images are usually of such a good image quality that standard deformable volume-to-volume (3D-3D) registration approaches can be applied. But research in temporal-correlated image reconstruction and dose reductions increases the number of cases where rawdata are available from only few projection angles. Here, deteriorated image quality leads to non-acceptable deformable volume-to-volume registration results. Therefore a registration approach is required that is robust against a decreasing number of projections defining the target position. We propose a deformable volume-to-rawdata (3D-2D) registration method that aims at finding a displacement vector field maximizing the alignment of a CT volume and the acquired rawdata based on the sum of squared differences in rawdata domain. The registration is constrained by a regularization term in accordance with a fluid-based diffusion. Both cost function components, the rawdata fidelity and the regularization term, are optimized in an alternating manner. The matching criterion is optimized by a conjugate gradient descent for nonlinear functions, while the regularization is realized by convolution of the vector fields with Gaussian kernels. We validate the proposed method and compare it to the demons algorithm, a well-known 3D-3D registration method. The comparison is done for a range of 4-60 target projections using datasets from low dose tomographic fluoroscopy as an application example. The results show a high correlation to the ground truth target position without introducing artifacts even in the case of very few projections. In particular the matching in the rawdata domain is improved compared to the 3D-3D registration for the investigated range. The proposed volume-to-rawdata registration increases the robustness regarding sparse rawdata and provides more stable results than volume-to-volume approaches. By applying the proposed registration approach to low dose tomographic fluoroscopy it is possible to improve the temporal resolution and thus to increase the robustness of low dose tomographic fluoroscopy.

Prior-based artifact correction (PBAC) in computed tomography.

PURPOSE: Image quality in computed tomography (CT) often suffers from artifacts which may reduce the diagnostic value of the image. In many cases, these artifacts result from missing or corrupt regions in the projection data, e.g., in the case of metal, truncation, and limited angle artifacts. The authors propose a generalized correction method for different kinds of artifacts resulting from missing or corrupt data by making use of available prior knowledge to perform data completion. METHODS: The proposed prior-based artifact correction (PBAC) method requires prior knowledge in form of a planning CT of the same patient or in form of a CT scan of a different patient showing the same body region. In both cases, the prior image is registered to the patient image using a deformable transformation. The registered prior is forward projected and data completion of the patient projections is performed using smooth sinogram inpainting. The obtained projection data are used to reconstruct the corrected image. RESULTS: The authors investigate metal and truncation artifacts in patient data sets acquired with a clinical CT and limited angle artifacts in an anthropomorphic head phantom data set acquired with a gantry-based flat detector CT device. In all cases, the corrected images obtained by PBAC are nearly artifact-free. Compared to conventional correction methods, PBAC achieves better artifact suppression while preserving the patient-specific anatomy at the same time. Further, the authors show that prominent anatomical details in the prior image seem to have only minor impact on the correction result. CONCLUSIONS: The results show that PBAC has the potential to effectively correct for metal, truncation, and limited angle artifacts if adequate prior data are available. Since the proposed method makes use of a generalized algorithm, PBAC may also be applicable to other artifacts resulting from missing or corrupt data.

Low dose tomographic fluoroscopy: 4D intervention guidance with running prior.

PURPOSE: Today's standard imaging technique in interventional radiology is the single- or biplane x-ray fluoroscopy which delivers 2D projection images as a function of time (2D+T). This state-of-the-art technology, however, suffers from its projective nature and is limited by the superposition of the patient's anatomy. Temporally resolved tomographic volumes (3D+T) would significantly improve the visualization of complex structures. A continuous tomographic data acquisition, if carried out with today's technology, would yield an excessive patient dose. Recently the authors proposed a method that enables tomographic fluoroscopy at the same dose level as projective fluoroscopy which means that if scanning time of an intervention guided by projective fluoroscopy is the same as that of an intervention guided by tomographic fluoroscopy, almost the same dose is administered to the patient. The purpose of this work is to extend authors' previous work and allow for patient motion during the intervention. METHODS: The authors propose the running prior technique for adaptation of a prior image. This adaptation is realized by a combination of registration and projection replacement. In a first step the prior is deformed to the current position via affine and deformable registration. Then the information from outdated projections is replaced by newly acquired projections using forward and backprojection steps. The thus adapted volume is the running prior. The proposed method is validated by simulated as well as measured data. To investigate motion during intervention a moving head phantom was simulated. Real in vivo data of a pig are acquired by a prototype CT system consisting of a flat detector and a continuously rotating clinical gantry. RESULTS: With the running prior technique it is possible to correct for motion without additional dose. For an application in intervention guidance both steps of the running prior technique, registration and replacement, are necessary. Reconstructed volumes based on the running prior show high image quality without introducing new artifacts and the interventional materials are displayed at the correct position. CONCLUSIONS: The running prior improves the robustness of low dose 3D+T intervention guidance toward intended or unintended patient motion.

Artifact-resistant motion estimation with a patient-specific artifact model for motion-compensated cone-beam CT.

PURPOSE: In image-guided radiation therapy (IGRT) valuable information for patient positioning, dose verification, and adaptive treatment planning is provided by an additional kV imaging unit. However, due to the limited gantry rotation speed during treatment the typical acquisition time is quite long. Tomographic images of the thorax suffer from motion blurring or, if a gated 4D reconstruction is performed, from significant streak artifacts. Our purpose is to provide a method that reliably estimates respiratory motion in presence of severe artifacts. The estimated motion vector fields are then used for motion-compensated image reconstruction to provide high quality respiratory-correlated 4D volumes for on-board cone-beam CT (CBCT) scans. METHODS: The proposed motion estimation method consists of a model that explicitly addresses image artifacts because in presence of severe artifacts state-of-the-art registration methods tend to register artifacts rather than anatomy. Our artifact model, e.g., generates streak artifacts very similar to those included in the gated 4D CBCT images, but it does not include respiratory motion. In combination with a registration strategy, the model gives an error estimate that is used to compensate the corresponding errors of the motion vector fields that are estimated from the gated 4D CBCT images. The algorithm is tested in combination with a cyclic registration approach using temporal constraints and with a standard 3D-3D registration approach. A qualitative and quantitative evaluation of the motion-compensated results was performed using simulated rawdata created on basis of clinical CT data. Further evaluation includes patient data which were scanned with an on-board CBCT system. RESULTS: The model-based motion estimation method is nearly insensitive to image artifacts of gated 4D reconstructions as they are caused by angular undersampling. The motion is accurately estimated and our motion-compensated image reconstruction algorithm can correct for it. Motion artifacts of 3D standard reconstruction are significantly reduced, while almost no new artifacts are introduced. CONCLUSIONS: Using the artifact model allows to accurately estimate and compensate for patient motion, even if the initial reconstructions are of very low image quality. Using our approach together with a cyclic registration algorithm yields a combination which shows almost no sensitivity to sparse-view artifacts and thus ensures both high spatial and high temporal resolution.

Self-adapting cyclic registration for motion-compensated cone-beam CT in image-guided radiation therapy.

PURPOSE: In image-guided radiation therapy an additional kV imaging system next to the linear particle accelerator provides information for an accurate patient positioning. However, the acquisition time of the system is much longer than the patient's breathing cycle due to the low gantry rotation speed. Our purpose is a cyclic registration in the context of motion-compensated image reconstruction that provides high quality respiratory-correlated 4D volumes for on-board flat panel detector cone-beam CT scans. METHODS: Based on the small motion assumption, widely used within registration algorithms, a strategy is developed for motion estimation. In this strategy temporal restrictions are incorporated, for example, the cyclic motion patterns of respiration. The resultant cyclic registration method is to show less sensitivity on image artifacts, in particular on artifacts due to projection data sparsification. Using a new cyclic registration method a motion estimation is performed on respiratory-correlated reconstructions, and the obtained motion vector fields are used for motion compensation. RESULTS: The proposed cyclic registration is evaluated in the context of motion-compensated image reconstruction using simulated data and patient data. Motion artifacts of 3D standard reconstructions can be significantly reduced by the resulting cyclic motion compensation. The method outperforms the respiratory-correlated reconstructions regarding sparse-view artifacts and maintains the high temporal resolution at the same time. Image artifacts show only minor to almost no effect on the motion estimation using the cyclic registration. CONCLUSIONS: The cyclic motion compensation approach provides respiratory-correlated volumes with high image quality. The cyclic motion estimation is of such low sensitivity to sparse-view artifacts, that it is capable to determine high quality motion vector fields based on reconstructions of low sampled data.