Johan Sunnegardh

Adaptive iterative reconstruction

It is well known that, in CT reconstruction, Maximum A Posteriori (MAP) reconstruction based on a Poisson noise model can be well approximated by Penalized Weighted Least Square (PWLS) minimization based on a data dependent Gaussian noise model. We study minimization of the PWLS objective function using the Gradient Descent (GD) method, and show that if an exact inverse of the forward projector exists, the PWLS GD update equation can be translated into an update equation which entirely operates in the image domain. In case of non-linear regularization and arbitrary noise model this means that a non-linear image filter must exist which solves the optimization problem. In the general case of non-linear regularization and arbitrary noise model, the analytical computation is not trivial and might lead to image filters which are computationally very expensive. We introduce a new iteration scheme in image space, based on a regularization filter with an anisotropic noise model. Basically, this approximates the statistical data weighting and regularization in PWLS reconstruction. If needed, e.g. for compensation of the non-exactness of backprojector, the image-based regularization loop can be preceded by a raw data based loop without regularization and statistical data weighting. We call this combined iterative reconstruction scheme Adaptive Iterative Reconstruction (AIR). It will be shown that in terms of low-contrast visibility, sharpness-to-noise and contrast-to-noise ratio, PWLS and AIR reconstruction are similar to a high degree of accuracy. In clinical images the noise texture of AIR is also superior to the more artificial texture of PWLS.

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.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.

Novel iterative reconstruction method for optimal dose usage in redundant CT - acquisitions

In CT imaging, a variety of applications exist where reconstructions are SNR and/or resolution limited. However, if the measured data provide redundant information, composite image data with high SNR can be computed. Generally, these composite image volumes will compromise spectral information and/or spatial resolution and/or temporal resolution. This brings us to the idea of transferring the high SNR of the composite image data to low SNR (but high resolution) ‘source’ image data. It was shown that the SNR of CT image data can be improved using iterative reconstruction [1] .We present a novel iterative reconstruction method enabling optimal dose usage of redundant CT measurements of the same body region. The generalized update equation is formulated in image space without further referring to raw data after initial reconstruction of source and composite image data. The update equation consists of a linear combination of the previous update, a correction term constrained by the source data, and a regularization prior initialized by the composite data. The efficiency of the method is demonstrated for different applications: (i) Spectral imaging: we have analysed material decomposition data from dual energy data of our photon counting prototype scanner: the material images can be significantly improved transferring the good noise statistics of the 20 keV threshold image data to each of the material images. (ii) Multi-phase liver imaging: Reconstructions of multi-phase liver data can be optimized by utilizing the noise statistics of combined data from all measured phases (iii) Helical reconstruction with optimized temporal resolution: splitting up reconstruction of redundant helical acquisition data into a short scan reconstruction with Tam window optimizes the temporal resolution The reconstruction of full helical data is then used to optimize the SNR. (iv) Cardiac imaging: the optimal phase image (‘best phase’) can be improved by transferring all applied over radiation into that image. In all these cases, we show that - at constant patient dose - SNR can efficiently be transferred from the composite data to the source data while maintaining spatial, temporal and contrast resolution properties of the source data.