title: An innovative iterative thresholding algorithm for tumour segmentation and volumetric quantification on SPECT images: Monte Carlo-based methodology and validation.
abstract: OBJECTIVE
  Positron emission tomography (PET) and single-photon emission computed tomography (SPECT) imaging play an important role in the segmentation of functioning parts of organs or tumours, but an accurate and reproducible delineation is still a challenging task. In this work, an innovative iterative thresholding method for tumour segmentation has been proposed and implemented for a SPECT system. This method, which is based on experimental threshold-volume calibrations, implements also the recovery coefficients (RC) of the imaging system, so it has been called recovering iterative thresholding method (RIThM). The possibility to employ Monte Carlo (MC) simulations for system calibration was also investigated.
  
  
  METHODS
  The RIThM is an iterative algorithm coded using MATLAB: after an initial rough estimate of the volume of interest, the following calculations are repeated: (i) the corresponding source-to-background ratio (SBR) is measured and corrected by means of the RC curve; (ii) the threshold corresponding to the amended SBR value and the volume estimate is then found using threshold-volume data; (iii) new volume estimate is obtained by image thresholding. The process goes on until convergence. The RIThM was implemented for an Infinia Hawkeye 4 (GE Healthcare) SPECT/CT system, using a Jaszczak phantom and several test objects. Two MC codes were tested to simulate the calibration images: SIMIND and SimSet. For validation, test images consisting of hot spheres and some anatomical structures of the Zubal head phantom were simulated with SIMIND code. Additional test objects (flasks and vials) were also imaged experimentally. Finally, the RIThM was applied to evaluate three cases of brain metastases and two cases of high grade gliomas.
  
  
  RESULTS
  Comparing experimental thresholds and those obtained by MC simulations, a maximum difference of about 4% was found, within the errors (+/- 2% and +/- 5%, for volumes > or = 5 ml or < 5 ml, respectively). Also for the RC data, the comparison showed differences (up to 8%) within the assigned error (+/- 6%). ANOVA test demonstrated that the calibration results (in terms of thresholds or RCs at various volumes) obtained by MC simulations were indistinguishable from those obtained experimentally. The accuracy in volume determination for the simulated hot spheres was between -9% and 15% in the range 4-270 ml, whereas for volumes less than 4 ml (in the range 1-3 ml) the difference increased abruptly reaching values greater than 100%. For the Zubal head phantom, errors ranged between 9% and 18%. For the experimental test images, the accuracy level was within +/- 10%, for volumes in the range 20-110 ml. The preliminary test of application on patients evidenced the suitability of the method in a clinical setting.
  
  
  CONCLUSIONS
  The MC-guided delineation of tumor volume may reduce the acquisition time required for the experimental calibration. Analysis of images of several simulated and experimental test objects, Zubal head phantom and clinical cases demonstrated the robustness, suitability, accuracy, and speed of the proposed method. Nevertheless, studies concerning tumors of irregular shape and/or nonuniform distribution of the background activity are still in progress.
authors.affiliation: Department of Medical Physics, Azienda Ospedaliera S. Camillo Forlanini, Rome 00151, Italy. mpacilio@scamilloforlanini.rm.it
authors.forename: M
authors.initials: M
authors.lastname: Pacilio
authors.forename: C
authors.initials: C
authors.lastname: Basile
authors.forename: S
authors.initials: S
authors.lastname: Shcherbinin
authors.forename: F
authors.initials: F
authors.lastname: Caselli
authors.forename: G
authors.initials: G
authors.lastname: Ventroni
authors.forename: D
authors.initials: D
authors.lastname: Aragno
authors.forename: L
authors.initials: L
authors.lastname: Mango
authors.forename: E
authors.initials: E
authors.lastname: Santini
mesh_terms: D000465:Algorithms; D001692:Biological Transport; D001932:Brain Neoplasms / Q000000981:diagnostic imaging / Q000556:secondary; D002138:Calibration; D005910:Glioma / Q000000981:diagnostic imaging / Q000473:pathology; D006801:Humans; D008175:Lung Neoplasms / Q000473:pathology; D009010:Monte Carlo Method; D009369:Neoplasms / Q000000981:diagnostic imaging* / Q000473:pathology; D019047:Phantoms, Imaging; D015899:Tomography, Emission-Computed, Single-Photon / Q000379:methods*; D047368:Tumor Burden