Sir Peter MacCallum Department of Oncology - Research Publications
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ItemVoxel-wise correlation of positron emission tomography/computed tomography with multiparametric magnetic resonance imaging and histology of the prostate using a sophisticated registration framework(WILEY, 2019-06)OBJECTIVES: To develop a registration framework for correlating positron emission tomography/computed tomography (PET/CT) images with multiparametric magnetic resonance imaging (mpMRI) and histology of the prostate, thereby enabling voxel-wise analysis of imaging parameters. PATIENTS AND METHODS: In this prospective proof-of-concept study, nine patients scheduled for radical prostatectomy underwent mpMRI and PET/CT imaging before surgery. One had PET imaging using 18 F-fluoromethylcholine, five using 68 Ga-labelled prostate-specific membrane antigen (PSMA)-HBED-CC (PMSA-11), and three using a trial 68 Ga-labelled THP-PSMA tracer. PET/CT data were co-registered with mpMRI via the CT scan and an in vivo three-dimensional T2-weighted (T2w) MRI, and then co-registered with ground truth histology data using ex vivo MRI of the prostate specimen. Maximum and mean standardised uptake values (SUVmax and SUVmean ) were extracted from PET data using tumour annotations from histology, and Kolmogorov-Smirnov tests were used to compare between tumour- and benign-voxel values. Correlation analysis was performed between mpMRI and PET SUV tumour voxel values using Pearson's correlation coefficient and R2 statistics. RESULTS: PET/CT data from all nine patients were successfully registered with mpMRI and histology data. SUVmax and SUVmean ranged from 2.21 to 12.11 and 1.08 to 4.21, respectively. All patients showed the PET SUV values in benign and tumour voxels were from statistically different distributions. Correlation analysis showed no consistent trend between the T2w or apparent diffusion coefficient values and PET SUV. However, parameters from dynamic contrast-enhanced (DCE) MRI including the maximum enhancement, volume transfer constant (Ktrans ), and the initial area under the contrast agent concentration curve for the first 60 s after injection (iAUGC60), showed consistent positive correlations with PET SUV. Furthermore, R2* values from blood oxygen level-dependent (BOLD) MRI showed consistent negative correlations with PET SUV-voxel values. CONCLUSION: We have developed a novel framework for registering and correlating PET/CT data at a voxel-level with mpMRI and histology. Despite registration uncertainties, perfusion and oxygenation parameters from DCE MRI and BOLD imaging showed correlations with PET SUV. Further analysis will be performed on a larger patient cohort to quantify these proof-of-concept findings. Improved understanding of the correlation between mpMRI and PET will provide supportive information for focal therapy planning of the prostate.
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ItemTechnical Note: Rapid multiexponential curve fitting algorithm for voxel-based targeted radionuclide dosimetry(WILEY, 2020-09)BACKGROUND: Dosimetry in nuclear medicine often relies on estimating pharmacokinetics based on sparse temporal data. As analysis methods move toward image-based three-dimensional computation, it becomes important to interpolate and extrapolate these data without requiring manual intervention; that is, in a manner that is highly efficient and reproducible. Iterative least-squares solvers are poorly suited to this task because of the computational overhead and potential to optimize to local minima without applying tight constraints at the outset. METHODOLOGY: This work describes a fully analytical method for solving three-phase exponential time-activity curves based on three measured time points in a manner that may be readily employed by image-based dosimetry tools. The methodology uses a series of conditional statements and a piecewise approach for solving exponential slope directly through measured values in most instances. The proposed algorithm is tested against a purpose-designed iterative fitting technique and linear piecewise method followed by single exponential in a cohort of ten patients receiving 177 Lu-DOTA-Octreotate therapy. RESULTS: Tri-exponential time-integrated values are shown to be comparable to previously published methods with an average difference between organs when computed at the voxel level of 9.8 ± 14.2% and -3.6 ± 10.4% compared to iterative and interpolated methods, respectively. Of the three methods, the proposed tri-exponential algorithm was most consistent when regional time-integrated activity was evaluated at both voxel- and whole-organ levels. For whole-body SPECT imaging, it is possible to compute 3D time-integrated activity maps in <5 min processing time. Furthermore, the technique is able to predictably and reproducibly handle artefactual measurements due to noise or spatial misalignment over multiple image times. CONCLUSIONS: An efficient, analytical algorithm for solving multiphase exponential pharmacokinetics is reported. The method may be readily incorporated into voxel-dose routines by combining with widely available image registration and radiation transport tools.
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ItemDeep Learning Renal Segmentation for Fully Automated Radiation Dose Estimation in Unsealed Source Therapy(FRONTIERS MEDIA SA, 2018-06-14)BACKGROUND: Convolutional neural networks (CNNs) have been shown to be powerful tools to assist with object detection and-like a human observer-may be trained based on a relatively small cohort of reference subjects. Rapid, accurate organ recognition in medical imaging permits a variety of new quantitative diagnostic techniques. In the case of therapy with targeted radionuclides, it may permit comprehensive radiation dose analysis in a manner that would often be prohibitively time-consuming using conventional methods. METHODS: An automated image segmentation tool was developed based on three-dimensional CNNs to detect right and left kidney contours on non-contrast CT images. Model was trained based on 89 manually contoured cases and tested on a cohort of patients receiving therapy with 177Lu-prostate-specific membrane antigen-617 for metastatic prostate cancer. Automatically generated contours were compared with those drawn by an expert and assessed for similarity based on dice score, mean distance-to-agreement, and total segmented volume. Further, the contours were applied to voxel dose maps computed from post-treatment quantitative SPECT imaging to estimate renal radiation dose from therapy. RESULTS: Neural network segmentation was able to identify right and left kidneys in all patients with a high degree of accuracy. The system was integrated into the hospital image database, returning contours for a selected study in approximately 90 s. Mean dice score was 0.91 and 0.86 for right and left kidneys, respectively. Poor performance was observed in three patients with cystic kidneys of which only few were included in the training data. No significant difference in mean radiation absorbed dose was observed between the manual and automated algorithms. CONCLUSION: Automated contouring using CNNs shows promise in providing quantitative assessment of functional SPECT and possibly PET images; in this case demonstrating comparable accuracy for radiation dose interpretation in unsealed source therapy relative to a human observer.