Attenuation correction using SPECT emission data only

Gourion, Daniel; Noll, Dominikus; Gantet, Pierre; Celler, Anna; Esquerré, J. P.

doi:10.1109/nssmic.2001.1008627

Cited by 12 publications

(13 citation statements)

References 29 publications

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“…V. Dicken in [16] formulated this problem as an optimization problem and solved it via nonlinear Tikhonov regularization techniques. More information and references about simultaneous reconstruction of the unknown emission and attenuation maps can be found in [8,18,48,43,50]. But a satisfactory method for determining both unknown functions from the emission data has not yet been found.…”

Section: Introductionmentioning

confidence: 99%

Inversion of the scalar and vector attenuated X-ray transforms in a unit disc

Kazantsev

Bukhgeim

2007

Jiip

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This paper summarizes some of the old results obtained for the problems of inverting the two-dimensional attenuated X-ray transform and the attenuated vectorial X-ray transform, both using the fan-beam geometry. These inverse problems are considered on the language of the transport equation and two approaches are described for solving them. The first one, dating back to 1996, gives the inversion formulae on the basis of the theory of so-called A-analytic functions. And the second method (developed by the authors in 2002) yields the inversion formulae for the scalar and vector attenuated X-ray transforms without using the theory of A-analytic functions, but merely by reducing the arising inverse problems to the unattenuated case by the change of variables. Numerical implementation details are also provided.

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Section: Introductionmentioning

confidence: 99%

Inversion of the scalar and vector attenuated X-ray transforms in a unit disc

Kazantsev

Bukhgeim

2007

Jiip

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“…The first class of algorithms attempt to estimate the attenuation coefficients directly from the emission data. These algorithms either try to perform iterative inversion of the forward mathematical model, 16–19 or exploit the consistency conditions that are based on the forward model. 15, 20–22 However, these methods are considerably slow, and mostly use only the data around the photopeak events neglecting the scattered photons.…”

Section: Introductionmentioning

confidence: 99%

Joint reconstruction of activity and attenuation map using LM SPECT emission data

Jha

Clarkson

Kupinski

et al. 2013

Medical Imaging 2013: Physics of Medical Imaging

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Attenuation and scatter correction in single photon emission computed tomography (SPECT) imaging often requires a computed tomography (CT) scan to compute the attenuation map of the patient. This results in increased radiation dose for the patient, and also has other disadvantages such as increased costs and hardware complexity. Attenuation in SPECT is a direct consequence of Compton scattering, and therefore, if the scattered photon data can give information about the attenuation map, then the CT scan may not be required. In this paper, we investigate the possibility of joint reconstruction of the activity and attenuation map using list-mode (LM) SPECT emission data, including the scattered-photon data. We propose a path-based formalism to process scattered-photon data. Following this, we derive analytic expressions to compute the Cramér-Rao bound (CRB) of the activity and attenuation map estimates, using which, we can explore the fundamental limit of information-retrieval capacity from LM SPECT emission data. We then suggest a maximum-likelihood (ML) scheme that uses the LM emission data to jointly reconstruct the activity and attenuation map. We also propose an expectation-maximization (EM) algorithm to compute the ML solution.

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“…There has been much previous work on estimating attenuation from emission projection data, e.g., [10] [11] [12] [13] [14] [15] [16] [17] [18] [19] [20] [21] [22] [23] [24]. However, the task undertaken here differs from the tasks pursued in previous studies in a major way.…”

Section: Introductionmentioning

confidence: 99%

Anomaly Detection and Artifact Recovery in PET Attenuation-Correction Images Using the Likelihood Function

Laymon

Bowsher

2013

IEEE J. Sel. Top. Signal Process.

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In dual modality PET/CT, CT data are used to generate the attenuation correction applied in the reconstruction of the PET emission image. This requires converting the CT image into a 511-keV attenuation map. Algorithms for making this transformation require assumptions about the makeup of material within the patient. Anomalous material such as contrast agent administered to enhance the CT scan confounds conversion algorithms and has been observed to result in inaccuracies, i.e., inconsistencies with the true 511-keV attenuation present at the time of the PET emission scan. These attenuation artifacts carry through to the final attenuation-corrected PET emission image and can resemble diseased tissue. We propose an approach to correcting this problem that employs the attenuation information carried by the PET emission data. A likelihood-based algorithm for identifying and correcting of contrast is presented and tested. The algorithm exploits the fact that contrast artifacts manifest as too-high attenuation values in an otherwise high quality attenuation image. In a separate study, the performance of the loglikelihood as an objective-function component of a detection/correction algorithm, independent of any particular algorithm was mapped out for several imaging scenarios as a function of statistical noise. Both the full algorithm and the loglikelihood performed well in studies with simulated data. Additional studies including those with patient data are required to fully understand their capabilities.

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Attenuation correction using SPECT emission data only

Cited by 12 publications

References 29 publications

Inversion of the scalar and vector attenuated X-ray transforms in a unit disc

Inversion of the scalar and vector attenuated X-ray transforms in a unit disc

Joint reconstruction of activity and attenuation map using LM SPECT emission data

Anomaly Detection and Artifact Recovery in PET Attenuation-Correction Images Using the Likelihood Function

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