Karl Stierstorfer, Rainer Raupach, Thomas Flohr, Siemens Healthcare Iterative Reconstruction in CT – the Siemens Approach © Siemens AG 2015 All rights reserved. Answers for life. Standard CT Reconstruction: Filtered Backprojection (FBP) raw data domain CT raw data image domain FBP CT image reconstruction © Siemens AG 2015 All rights reserved. K. Stierstorfer, Siemens Healthcare Page 2 Limitations of Filtered Backprojection (FBP) FBP is only an approximate realization of the inverse Radon transformation „geometric artifacts“ (cone beam artifacts) FBP uses raw data with equal weight irrespectively of their statistical quality streak artifacts Tradeoff between spatial resolution and noise (linear algorithm) © Siemens AG 2015 All rights reserved. Page 3 smooth low noise sharp high noise Handout 1 Iterative Reconstruction Basic Idea: Introduction of a Correction Loop raw data domain image domain correction image FBP CT raw data + reconstruction + forward projection CT image + Simulation of a CT scan from the images Modeling of system features (focus, detector, …) Validation of the reconstructed images w.r.t. the measured data © Siemens AG 2015 All rights reserved. Page 4 K. Stierstorfer, Siemens Healthcare axial axial Artifact Reduction by IR coronal coronal sagittal FBP © Siemens AG 2015 All rights reserved. Page 5 Standard I-FBP 2 iterations I-FBP, 2 iterations 2014-09-17 R. Raupach / H IM CR R&D PA Iterative Reconstruction with Statistical Optimization: Statistical Weighting and Regularization raw data domain CT raw + data statistical weighting image domain FBP correction image - forward projection Weight data according to their statistical quality, typically 1 / variance + CT image - regularization (prior knowledge) © Siemens AG 2015 All rights reserved. Page 6 K. Stierstorfer, Siemens Healthcare Handout 2 Regularization / Prior Knowledge Regularization + CT image Local smoothness constraint – this is where prior knowledge is introduced - Ensures convergence of the iterative reconstruction Is the essential mechanism for noise and dose reduction! regularization (prior knowledge) Has to be non-linear to get rid of the tradeoff between resolution and noise How it works Estimate/model the (local) image standard deviation Separate information and noise on the basis of statistical significance intelligent high-pass filter based on local contrast-to-noise Subtract the detected noise © Siemens AG 2015 All rights reserved. Page 7 K. Stierstorfer, Siemens Healthcare Advantages of Non-Linear Regularization versus Linear Filtering = 53 HU = 21 HU = 21 HU WFBP IR, 5 iterations WFBP, soft kernel (=WFBPsoft) © Siemens AG 2015 All rights reserved. Page 9 2014-09-17 (81,167): -110.464 (67,169): 3 Diff (IR – WFBP) Diff (WFBP R. Raupach / HWFBP) IM CR R&D PA soft – General Approach For the different objectives of iterative reconstruction, choose the domain most efficient to accomplish them: Need low signal data enhancement? work in the raw data domain; apply statistical modeling Need (cone beam) artifact reduction? need a full raw data/image loop including a forward projection of images Need noise reduction? best apply iterative regularization in image domain © Siemens AG 2015 All rights reserved. Page 10 K. Stierstorfer, Siemens Healthcare Handout 3 Iteration Scheme best image Maximum Likelihood: data image f arg max P( pmeas | f ) f “The best image is the most likely image based on the data” Additional assumption: Data are nearly Gaussian 0.14 Poisson Gauss 0.12 Problem reduces to a minimization problem (Penalized weighted least squares): 0.1 There is virtually no difference between Poisson and Gauss, even for as few as 10 photons… 0.08 0.06 best image data weight proj. operator regularizer 2 1 f arg min W ( pmeas Pf ) R( f ) f 2 0.04 0.02 0 0 5 10 15 20 25 30 35 40 45 50 Iterative solution e.g. by steepest descent: reconstruction (backprojection) f n f n 1 QW pmeas Pf n 1 R f n 1 © Siemens AG 2015 All rights reserved. Page 11 K. Stierstorfer, Siemens Healthcare Iteration Scheme Now we have: f n f n 1 QW pmeas Pf n 1 Rf n 1 correction image Define … regularization X W QWP 1. … the operator Performs forward projection, then weighting, followed by reconstruction, i.e. takes an image and returns an image XW depends on the raw data via W, but can be pre-calculated 2. … a „weighted“ image fW QWpmeas fW QWpmeas Qpmeas © Siemens AG 2015 All rights reserved. K. Stierstorfer, Siemens Healthcare Page 12 Iteration Scheme f n f n 1 QW pmeas Pf n 1 Rf n 1 correction image regularization X W QWP fW QWpmeas f n fW 1 X W R f n 1 No access to measured raw data necessary in the loop! Iterative image manipulation can be equivalent to a noise weighted raw data loop © Siemens AG 2015 All rights reserved. Page 13 K. Stierstorfer, Siemens Healthcare Handout 4 General Approach Is system modeling essential for noise reduction? No – noise reduction is achieved exclusively through the regularization term Does system modeling improve the resolution? No – for a stable reconstruction, the resolution is still limited by the focus size, the detector aperture and the data sampling Is a raw data loop essential for noise reduction? No – cf. previous slides (For a similar – and mathematically rigorous – approach, see also: G. L. Zeng, Noise-weighted spatial domain FBP algorithm, Med. Phys. 41, 051906 (2014)) © Siemens AG 2015 All rights reserved. Page 14 2015-07 K. Stierstorfer, Siemens Healthcare General Approach Relevant questions for choosing an IR algorithm are: How can the IR approach be implemented efficiently with clinically valuable results and acceptable computational performance? How can a natural noise texture be realized? (Radiologists reject “plasticlike” de-noised images) What is the benefit with routine reconstruction parameters (e.g. thick slices)? How can we predict/assess the achievable dose reduction in a meaningful way? © Siemens AG 2015 All rights reserved. Page 15 K. Stierstorfer, Siemens Healthcare Siemens Generations of IR: IRIS – Iterative Reconstruction in Image Space No reduction of geometric (cone beam) artifacts Limited potential for anisotropic noise distribution (e.g. shoulder) Master 3D Volume +/- Statistical Modeling (Image) © Siemens AG 2015 All rights reserved. Page 16 K. Stierstorfer, Siemens Healthcare Handout 5 Siemens Generations of IR: SAFIRE – Sinogram Affirmed Iterative Reconstruction FBP +/- Master 3D Volume +/Loop B Statistical Modeling (Image) Loop A System Modeling (FP) © Siemens AG 2015 All rights reserved. Page 17 K. Stierstorfer, Siemens Healthcare Siemens Generations of IR: ADMIRE – Advanced Modeling Iterative Reconstruction Improved performance at very low signals FBP Statistical Modeling (Raw) +/- Master 3D Volume +/Loop B Statistical Modeling (Image) Loop A System Modeling (FP) • Improved performance for thick slices • Improved noise texture © Siemens AG 2015 All rights reserved. Page 18 K. Stierstorfer, Siemens Healthcare ADMIRE – Advanced Modeling Iterative Reconstruction Standard Br40 ADMIRE Br40/5 © Siemens AG 2015 All rights reserved. Page 19 K. Stierstorfer, Siemens Healthcare Handout 6 ADMIRE – Advanced Modeling Iterative Reconstruction Standard Bv40 ADMIRE Bv40/5 © Siemens AG 2015 All rights reserved. Page 20 K. Stierstorfer, Siemens Healthcare ADMIRE – Advanced Modeling Iterative Reconstruction Standard Hc44 ADMIRE Hc44/5 © Siemens AG 2015 All rights reserved. Page 21 K. Stierstorfer, Siemens Healthcare ADMIRE – Advanced Modeling Iterative Reconstruction Low Dose CTDIvol 0.23 mGy / DLP 8.3 mGycm / eff. Dose 0.12 mSv Standard Bl64 ADMIRE Bl64/5 © Siemens AG 2015 All rights reserved. Page 22 K. Stierstorfer, Siemens Healthcare Handout 7 ADMIRE – Advanced Modeling Iterative Reconstruction Extremely Low Dose CTDIvol: 0.04 mGy / DLP 1.64 mGycm / eff. Dose 0.025 mSv Standard Bl64 SAFIRE Bl64/5 ADMIRE Bl64/5 © Siemens AG 2015 All rights reserved. Page 23 K. Stierstorfer, Siemens Healthcare What Should Be Compared? up to 55% less noise 30% less noise 35% less noise = 17.6 HU = 12.3 HU = 7.8 HU = 26.8 HU plain FBP Siemens WFBP reconstruction © Siemens AG 2015 All rights reserved. Page 24 2014-09-17 1st generation IR slice 0.75 mm W = 250 C = 40 2nd generation IR R. Raupach / H IM CR R&D PA Iterative Reconstruction Evaluation with Model or Human Observers How low can you go? IR leads to a noise level comparable with higher radiation dose, even at about 90% dose reduction But: diagnostic equivalence not necessarily given - some details may be lost. Technical metrics (noise, contrast, …) are not sufficient (cf. previous talk) More sophisticated objective tests are required Low Dose IR [2.3 mSv] Full dose FBP [19 mSv] © Siemens AG 2015 All rights reserved. Page 25 K. Stierstorfer, Siemens Healthcare Handout 8 Iterative Reconstruction Evaluation with Model Observers Definition of a meaningful task Usage of a „Channelized Hotelling Observer“ mimics visual perception positive negative Task: lesion present or not? true positive findings ROC analysis WFBP 120 mAs (100%) WFBP 48 mAs (40%) SAFIRE 48 mAs false positive findings © Siemens AG 2015 All rights reserved. Page 26 K. Stierstorfer, Siemens Healthcare What is a relevant imaging task? • Currently, all claims of dose reduction are based on studies with low contrast detection tasks („Is there a lesion or not?“), possibly with localization („Where is the lesion?“). • But: Is this the most relevant task in CT? There are other tasks like distinction of different shapes („Lesion round or hexagon?“, „Lesion fuzzy or sharply delineated?“) which might be closer to typical clinical applications of CT. However, they are less easy to standardize – and it may be more difficult to build phantoms! © Siemens AG 2015 All rights reserved. Page 27 2015-07 K. Stierstorfer, Siemens Healthcare Conclusion Iterative reconstruction is a meaningful way to improve various aspects of CT image quality. Various flavors of iterative reconstruction are available: data domain, image domain or data+image domain. They should be combined in a meaningful way to achieve the desired objectives efficiently. With respect to noise, IR is equivalent to a complex local, non-isotropic adaptive filter realizing a spatial resolution depending on the local contrast/noise ratio. Important questions from a clinical perspective: Image quality aspects: Natural noise texture? Edge appearance? Good also for thicker slice widths? What is the dose reduction potential for various tasks? Is it capable of routine usage? Dose reduction claims should be taken with a grain of salt: What is the point of comparison in a relative claim? Is the task clinically relevant? © Siemens AG 2015 All rights reserved. Page 28 K. Stierstorfer, Siemens Healthcare Handout 9 Thank you! © Siemens AG 2015 All rights reserved. Page 29 2015-07 K. Stierstorfer, Siemens Healthcare Handout 10