Regional MLEM reconstruction strategy for PET-based treatment verification in ion beam radiotherapy

In ion beam radiotherapy, PET-based treatment verification provides a consistency check of the delivered treatment with respect to a simulation based on the treatment planning. In this work the region-based MLEM reconstruction algorithm is proposed as a new evaluation strategy in PET-based treatment verification. The comparative evaluation is based on reconstructed PET images in selected regions, which are automatically identified on the expected PET images according to homogeneity in activity values. The strategy was tested on numerical and physical phantoms, simulating mismatches between the planned and measured β+ activity distributions. The region-based MLEM reconstruction was demonstrated to be robust against noise and the sensitivity of the strategy results were comparable to three voxel units, corresponding to 6 mm in numerical phantoms. The robustness of the region-based MLEM evaluation outperformed the voxel-based strategies. The potential of the proposed strategy was also retrospectively assessed on patient data and further clinical validation is envisioned.

[1]  Katia Parodi,et al.  A filtering approach based on Gaussian–powerlaw convolutions for local PET verification of proton radiotherapy , 2006, Physics in medicine and biology.

[2]  Nassir Navab,et al.  Joint Reconstruction of Image and Motion in Gated Positron Emission Tomography , 2010, IEEE Transactions on Medical Imaging.

[3]  K Parodi,et al.  Dose quantification from in-beam positron emission tomography. , 2004, Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology.

[4]  Christopher Kurz,et al.  An experimental approach to improve the Monte Carlo modelling of offline PET/CT-imaging of positron emitters induced by scanned proton beams , 2013, Physics in medicine and biology.

[5]  M. Conti.  Focus on time-of-flight PET: the benefits of improved time resolution , 2011, European Journal of Nuclear Medicine and Molecular Imaging.

[6]  Christopher Kurz,et al.  Implementation and initial clinical experience of offline PET/CT-based verification of scanned carbon ion treatment. , 2013, Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology.

[7]  Katia Parodi,et al.  Accuracy of proton beam range verification using post-treatment positron emission tomography/computed tomography as function of treatment site. , 2011, International journal of radiation oncology, biology, physics.

[8]  Anil K. Jain,et al.  Data clustering: a review , 1999, CSUR.

[9]  Katia Parodi,et al.  Patient study of in vivo verification of beam delivery and range, using positron emission tomography and computed tomography imaging after proton therapy. , 2007, International journal of radiation oncology, biology, physics.

[10]  Julia Bauer,et al.  Hypofractionated carbon ion therapy delivered with scanned ion beams for patients with hepatocellular carcinoma – feasibility and clinical response , 2013, Radiation Oncology.

[11]  H Paganetti,et al.  Clinical CT-based calculations of dose and positron emitter distributions in proton therapy using the FLUKA Monte Carlo code , 2007, Physics in medicine and biology.

[12]  Wolfgang Birkfellner,et al.  Using statistical measures for automated comparison of in-beam PET data. , 2012, Medical physics.

[13]  R M Lewitt,et al.  Performance of the Fourier rebinning algorithm for PET with large acceptance angles. , 1998, Physics in medicine and biology.

[14]  Magdalena Rafecas,et al.  Joint image and motion reconstruction for PET using a B-spline motion model , 2012, Physics in medicine and biology.

[15]  H Paganetti,et al.  Systematic analysis of biological and physical limitations of proton beam range verification with offline PET/CT scans , 2009, Physics in medicine and biology.

[16]  Christopher Kurz,et al.  PET-CT scanner characterization for PET raw data use in biomedical research , 2014, Comput. Medical Imaging Graph..

[17]  Patrick Dupont,et al.  Maximum-likelihood expectation-maximization reconstruction of sinograms with arbitrary noise distribution using NEC-transformations , 2001, IEEE Transactions on Medical Imaging.

[18]  Giuseppe Baselli,et al.  Lesion quantification in oncological positron emission tomography: a maximum likelihood partial volume correction strategy. , 2009, Medical physics.

[19]  Katia Parodi,et al.  PET monitoring of hadrontherapy , 2012 .

[20]  Wolfgang Enghardt,et al.  Attenuation and scatter correction for in-beam positron emission tomography monitoring of tumour irradiations with heavy ions. , 2003, Physics in medicine and biology.

[21]  J. Fessler,et al.  Joint estimation of image and deformation parameters in motion-corrected PET , 2003, 2003 IEEE Nuclear Science Symposium. Conference Record (IEEE Cat. No.03CH37515).

[22]  E De Bernardi,et al.  ML segmentation strategies for object interference compensation in FDG-PET lesion quantification. , 2010, Methods of information in medicine.

[23]  R. Lewitt.  Alternatives to voxels for image representation in iterative reconstruction algorithms , 1992, Physics in medicine and biology.

[24]  H. Zaidi,et al.  Advances in PET Image Reconstruction. , 2007, PET clinics.

[25]  G. Baselli,et al.  Evaluation of Frequency-Distance Relation Validity for FORE Optimization in 3-D PET , 2007, IEEE Transactions on Nuclear Science.

[26]  Hideaki Tashima,et al.  Total variation minimization for in-beam PET image reconstruction , 2012, 2012 IEEE Nuclear Science Symposium and Medical Imaging Conference Record (NSS/MIC).

[27]  Giuseppe Baselli,et al.  Region-based maximum likelihood reconstruction for lesion quantification in positron emission tomography , 2008 .

[28]  D. Townsend,et al.  Method for transforming CT images for attenuation correction in PET/CT imaging. , 2006, Medical physics.

[29]  Katia Parodi,et al.  Charged hadron tumour therapy monitoring by means of PET , 2004 .

[30]  Michel Defrise,et al.  Exact and approximate rebinning algorithms for 3-D PET data , 1997, IEEE Transactions on Medical Imaging.

[31]  D. Townsend,et al.  Physical and clinical performance of the mCT time-of-flight PET/CT scanner , 2011, Physics in medicine and biology.

[32]  H Paganetti,et al.  Quantitative assessment of the physical potential of proton beam range verification with PET/CT , 2008, Physics in medicine and biology.

[33]  Samuel Matej,et al.  Efficient 3D grids for image reconstruction using spherically-symmetric volume elements , 1995 .

[34]  I. Buvat,et al.  A review of partial volume correction techniques for emission tomography and their applications in neurology, cardiology and oncology , 2012, Physics in medicine and biology.

[35]  Herfried Wieczorek,et al.  The image quality of FBP and MLEM reconstruction , 2010, Physics in medicine and biology.

[36]  Oliver Jäkel,et al.  Positron emission tomography for quality assurance of cancer therapy with light ion beams , 1999 .

[37]  Katia Parodi,et al.  PET/CT imaging for treatment verification after proton therapy: a study with plastic phantoms and metallic implants. , 2007, Medical physics.