Reducing windmill artifacts in clinical spiral CT using a deep learning-based projection raw data upsampling: Method and robustness evaluation.

BACKGROUND: Multislice spiral computed tomography (MSCT) requires an interpolation between adjacent detector rows during backprojection. Not satisfying the Nyquist sampling condition along the z-axis results in aliasing effects, also known as windmill artifacts. These image distortions are characterized by bright streaks diverging from high contrast structures. PURPOSE: The z-flying focal spot (zFFS) is a well-established hardware-based solution that aims to double the sampling rate in longitudinal direction and therefore reduce aliasing artifacts. However, given the technical complexity of the zFFS, this work proposes a deep learning-based approach as an alternative solution. METHODS: We propose a supervised learning approach to perform a mapping between input projections and the corresponding rows required for double sampling in the z-direction. We present a comprehensive evaluation using both a clinical dataset obtained using raw data from 40 real patient scans acquired with zFFS and a synthetic dataset consisting of 100 simulated spiral scans using a phantom specifically designed for our problem. For the clinical dataset, we utilized 32 scans as training set and 8 scans as validation set, whereas for the synthetic dataset, we used 80 scans for training and 20 scans for validation purposes. Both qualitative and quantitative assessments are conducted on a test set consisting of nine real patient scans and six phantom measurements to validate the performance of our approach. A simulation study was performed to investigate the robustness against different scan configurations in terms of detector collimation and pitch value. RESULTS: In the quantitative comparison based on clinical patient scans from the test set, all network configurations show an improvement in the root mean square error (RMSE) of approximately 20% compared to neglecting the doubled longitudinal sampling by the zFFS. The results of the qualitative analysis indicate that both clinical and synthetic training data can reduce windmill artifacts through the application of a correspondingly trained network. Together with the qualitative results from the test set phantom measurements it is emphasized that a training of our method with synthetic data resulted in superior performance in windmill artifact reduction. CONCLUSIONS: Deep learning-based raw data interpolation has the potential to enhance the sampling in z-direction and thus minimize aliasing effects, as it is the case with the zFFS. Especially a training with synthetic data showed promising results. While it may not outperform zFFS, our method represents a beneficial solution for CT scanners lacking the necessary hardware components for zFFS.

Spatial resolution improvement and dose reduction potential for inner ear CT imaging using a z-axis deconvolution technique.

PURPOSE: To assess the z-axis resolution improvement and dose reduction potential achieved using a z-axis deconvolution technique with iterative reconstruction (IR) relative to filtered backprojection (FBP) images created with the use of a z-axis comb filter. METHODS: Each of three phantoms were scanned with two different acquisition modes: (1) an ultrahigh resolution (UHR) scan mode that uses a comb filter in the fan angle direction to increase in-plane spatial resolution and (2) a z-axis ultrahigh spatial resolution (zUHR) scan mode that uses comb filters in both the fan and cone angle directions to improve both in-plane and z-axis spatial resolution. All other scanning parameters were identical. First, the ACR CT Accreditation phantom, rotated by 90° so that the high-contrast spatial resolution targets were parallel to the coronal plane, was scanned to assess limiting spatial resolution and image noise. Second, section sensitivity profiles (SSPs) were measured using a copper foil embedded in an acrylic cylinder and the full-width-at-half-maximum (FWHM) and full-width-at-tenth-maximum (FWTM) of the SSPs were calculated. Third, an anthropomorphic head phantom containing a human skull was scanned to assess clinical acceptability for imaging of the temporal bone. For each scan, FBP images were reconstructed for the zUHR scan using the narrowest image thickness available. For the CT accreditation phantom, zUHR images were also reconstructed using an IR algorithm (SAFIRE, Siemens Healthcare, Forchheim, Germany) to assess the influence of the IR algorithm on image noise. A z-axis deconvolution technique combined with the IR algorithm was used to reconstruct images at the narrowest image thickness possible from the UHR scan data. Images of the ACR and head phantoms were reformatted into the coronal plane. The head phantom images were evaluated by a neuroradiologist to assess acceptability for use in patients undergoing clinically indicated CT imaging of the temporal bone. RESULTS: The limiting spatial resolution was 12 lp/cm for the FBP-zUHR images and the IR-UHR images, although visual assessment indicated a slight improvement for the IR-UHR images. Image noise was 213.0, 181.8, and 153.5 for the FBP-zUHR, IR-zUHR, and IR-UHR images, respectively. While the FWHM was essentially the same for the FBP-zUHR and IR-UHR images, the FWTM of the IR-UHR images was almost 50% smaller compared to the FBP-zUHR images (0.83 vs 1.25 mm, respectively). Images of the anthropomorphic head phantom were judged to be of higher quality for the IR-UHR images compared to the FBP-zUHR images. CONCLUSIONS: With use of a z-axis deconvolution technique, z-axis spatial resolution was improved for scans acquired using a comb filter only in the fan angle direction relative to FBP images acquired with a comb filter in both the fan and cone angle directions. By avoiding use of the comb filter in the cone angle direction and use of an IR algorithm, image noise was substantially reduced for the same scanner output (CTDIvol). Thus, overall image quality (spatial resolution and image noise) can be maintained relative to the FBP-zUHR technique at a lower radiation dose.

Enhanced temporal resolution at cardiac CT with a novel CT image reconstruction algorithm: initial patient experience.

OBJECTIVE: To evaluate the effect of a temporal resolution improvement method (TRIM) for cardiac CT on diagnostic image quality for coronary artery assessment. MATERIALS AND METHODS: The TRIM-algorithm employs an iterative approach to reconstruct images from less than 180° of projections and uses a histogram constraint to prevent the occurrence of limited-angle artifacts. This algorithm was applied in 11 obese patients (7 men, 67.2 ± 9.8 years) who had undergone second generation dual-source cardiac CT with 120 kV, 175-426 mAs, and 500 ms gantry rotation. All data were reconstructed with a temporal resolution of 250 ms using traditional filtered-back projection (FBP) and of 200 ms using the TRIM-algorithm. Contrast attenuation and contrast-to-noise-ratio (CNR) were measured in the ascending aorta. The presence and severity of coronary motion artifacts was rated on a 4-point Likert scale. RESULTS: All scans were considered of diagnostic quality. Mean BMI was 36 ± 3.6 kg/m(2). Average heart rate was 60 ± 9 bpm. Mean effective dose was 13.5 ± 4.6 mSv. When comparing FBP- and TRIM reconstructed series, the attenuation within the ascending aorta (392 ± 70.7 vs. 396.8 ± 70.1 HU, p>0.05) and CNR (13.2 ± 3.2 vs. 11.7 ± 3.1, p>0.05) were not significantly different. A total of 110 coronary segments were evaluated. All studies were deemed diagnostic; however, there was a significant (p<0.05) difference in the severity score distribution of coronary motion artifacts between FBP (median=2.5) and TRIM (median=2.0) reconstructions. CONCLUSION: The algorithm evaluated here delivers diagnostic imaging quality of the coronary arteries despite 500 ms gantry rotation. Possible applications include improvement of cardiac imaging on slower gantry rotation systems or mitigation of the trade-off between temporal resolution and CNR in obese patients.

Regularized iterative weighted filtered backprojection for helical cone-beam CT.

Contemporary reconstruction methods employed for clinical helical cone-beam computed tomography (CT) are analytical (noniterative) but mathematically nonexact, i.e., the reconstructed image contains so called cone-beam artifacts, especially for higher cone angles. Besides cone artifacts, these methods also suffer from windmill artifacts: alternating dark and bright regions creating spiral-like patterns occurring in the vicinity of high z-direction derivatives. In this article, the authors examine the possibility to suppress cone and windmill artifacts by means of iterative application of nonexact three-dimensional filtered backprojection, where the analytical part of the reconstruction brings about accelerated convergence. Specifically, they base their investigations on the weighted filtered backprojection method [Stierstorfer et al., Phys. Med. Biol. 49, 2209-2218 (2004)]. Enhancement of high frequencies and amplification of noise is a common but unwanted side effect in many acceleration attempts. They have employed linear regularization to avoid these effects and to improve the convergence properties of the iterative scheme. Artifacts and noise, as well as spatial resolution in terms of modulation transfer functions and slice sensitivity profiles have been measured. The results show that for cone angles up to +/-2.78 degrees, cone artifacts are suppressed and windmill artifacts are alleviated within three iterations. Furthermore, regularization parameters controlling spatial resolution can be tuned so that image quality in terms of spatial resolution and noise is preserved. Simulations with higher number of iterations and long objects (exceeding the measured region) verify that the size of the reconstructible region is not reduced, and that the regularization greatly improves the convergence properties of the iterative scheme. Taking these results into account, and the possibilities to extend the proposed method with more accurate modeling of the acquisition process, the authors believe that iterative improvement with non-exact methods is a promising technique for medical CT applications.

Handling of long objects in iterative improvement of nonexact reconstruction in helical cone-beam CT.

In medical helical cone-beam CT, it is common that the region-of-interest (ROI) is contained inside the helix cylinder, while the complete object is long and extends outside the top and the bottom of the cylinder. This is the Long Object Problem. Analytical reconstruction methods for helical cone-beam CT have been designed to handle this problem. It has been shown that a moderate amount of over-scanning is sufficient for reconstruction of a certain ROI. The over-scanning projection rays travel both through the ROI, as well as outside the ROI. This is unfortunate for iterative methods since it seems impossible to compute accurate values for the projection rays which travel partly inside and partly outside the ROI. Therefore, it seems that the useful ROI will diminish for every iteration step. We propose the following solution to the problem. First, we reconstruct volume regions also outside the ROI. These volume regions will certainly be incompletely reconstructed, but our experimental results show that they serve well for projection generation. This is rather counter-intuitive and contradictory to our initial assumptions. Second, we use careful extrapolation and masking of projection data. This is not a general necessity, but needed for the chosen iterative algorithm, which includes rebinning and iterative filtered backprojection. Our idea here was to use an approximate reconstruction method which gives cone-beam artifacts and then improve the reconstructed result by iterative filtered backprojection. The experimental results seem very encouraging. The cone-beam artifacts can indeed be removed. Even voxels close to the boundary of the ROI are as well enhanced by the iterative loop as those in the middle of the ROI.