4D ML reconstruction as a tool for volumetric PET‐based treatment verification in ion beam radiotherapy

• Published in: Medical physics (Lancaster) Vol. 43; no. 2; pp. 710 - 726
• Main Authors: De Bernardi, E., Ricotti, R., Riboldi, M., Baroni, G., Parodi, K., Gianoli, C.
• Format: Journal Article
• Language: English
• Published: United States American Association of Physicists in Medicine 01-02-2016
• Subjects: 4D ML reconstruction; ALGORITHMS; ANNIHILATION; Biomedical modeling; Computed tomography; COMPUTERIZED SIMULATION; DATASETS; Digital computing or data processing equipment or methods, specially adapted for specific applications; Image data processing or generation, in general; image denoising; Image enhancement or restoration, e.g. from bit‐mapped to bit‐mapped creating a similar image; Image Processing, Computer-Assisted - methods; image reconstruction; ion beam therapy; ION BEAMS; ITERATIVE METHODS; Likelihood Functions; Measuring half‐life of a radioactive substance; Medical image noise; medical image processing; Medical image reconstruction; MONTE CARLO METHOD; Monte Carlo methods; Numerical approximation and analysis; PET‐based treatment verification; POSITRON COMPUTED TOMOGRAPHY; Positron emission tomography (PET); Positron-Emission Tomography; Probability theory, stochastic processes, and statistics; RADIATION PROTECTION AND DOSIMETRY; radiation therapy; RADIOLOGY AND NUCLEAR MEDICINE; RADIOTHERAPY; Radiotherapy, Image-Guided; Reconstruction; Scintigraphy; Therapeutic applications, including brachytherapy; VERIFICATION
• ISSN: 0094-2405; 2473-4209
• DOI: 10.1118/1.4939227

Purpose:
An innovative strategy to improve the sensitivity of positron emission tomography (PET)‐based treatment verification in ion beam radiotherapy is proposed.
Methods:
Low counting statistics PET images acquired during or shortly after the treatment (Measured PET) and a Monte Carlo estimate of the same PET images derived from the treatment plan (Expected PET) are considered as two frames of a 4D dataset. A 4D maximum likelihood reconstruction strategy was adapted to iteratively estimate the annihilation events distribution in a reference frame and the deformation motion fields that map it in the Expected PET and Measured PET frames. The outputs generated by the proposed strategy are as follows: (1) an estimate of the Measured PET with an image quality comparable to the Expected PET and (2) an estimate of the motion field mapping Expected PET to Measured PET. The details of the algorithm are presented and the strategy is preliminarily tested on analytically simulated datasets.
Results:
The algorithm demonstrates (1) robustness against noise, even in the worst conditions where 1.5 × 104 true coincidences and a random fraction of 73% are simulated; (2) a proper sensitivity to different kind and grade of mismatches ranging between 1 and 10 mm; (3) robustness against bias due to incorrect washout modeling in the Monte Carlo simulation up to 1/3 of the original signal amplitude; and (4) an ability to describe the mismatch even in presence of complex annihilation distributions such as those induced by two perpendicular superimposed ion fields.
Conclusions:
The promising results obtained in this work suggest the applicability of the method as a quantification tool for PET‐based treatment verification in ion beam radiotherapy. An extensive assessment of the proposed strategy on real treatment verification data is planned.