Multi-observation PET image analysis for patient follow-up quantitation and therapy assessment

Abstract

In positron emission tomography (PET) imaging, an early therapeutic response is usually characterized by variations of semi-quantitative parameters restricted to maximum SUV measured in PET scans during the treatment. Such measurements do not reflect overall tumor volume and radiotracer uptake variations. The proposed approach is based on multi-observation image analysis for merging several PET acquisitions to assess tumor metabolic volume and uptake variations. The fusion algorithm is based on iterative estimation using a stochastic expectation maximization (SEM) algorithm. The proposed method was applied to simulated and clinical follow-up PET images. We compared the multi-observation fusion performance to threshold-based methods, proposed for the assessment of the therapeutic response based on functional volumes. On simulated datasets the adaptive threshold applied independently on both images led to higher errors than the ASEM fusion and on clinical datasets it failed to provide coherent measurements for four patients out of seven due to aberrant delineations. The ASEM method demonstrated improved and more robust estimation of the evaluation leading to more pertinent measurements. Future work will consist in extending the methodology and applying it to clinical multi-tracer datasets in order to evaluate its potential impact on the biological tumor volume definition for radiotherapy applications.

Figure 1

Multi-observation framework of multi-tracer and patient follow-up data.

Figure 2

Illustration of a VOI definition in the pre-treatment scan (a) and automatically reported on the registered mid-treatment scan (b).

Figure 3

(b) and (d) Simulated follow-up tumours, (a) and (c) associated ground-truths, (e) ASEM fusion map, individual segmented map with the adaptive threshold (f) and (g), and the ASEM method (h) and (i) for the two simulated cases.

Figure 4

Mean VE (%) with standard deviation as error bars of the first and second follow-up scans for adaptive threshold and ASEM methods applied to the simulated cases.

Figure 5

(a) and (b) Clinical follow-up tumours (c) ASEM fusion maps, individual segmented map of the two clinicians (d), (e) and (f), (g) with the adaptive threshold, (h) and (i), the ASEM method, for three real clinical datasets.