Dr. Frank Hofheinz

Research Associate
Positron Emission Tomography
Phone: +49 351 260 2756
+49 351 4583966

Prof. Dr. Jörg van den Hoff

Positron Emission Tomography
Phone: +49 351 260 2621
+49 351 4585068

Improved Quantification in PET

Automatic detection of tumor boundaries in PET data


Accurate tumor delineation in PET is essential for integration of PET into radiation treatment planning and therapy response assessment. Quite often the tumors are delineated by manually drawing of the tumor boundaries. However, manual delineation is prone to substantial interobserver variability, since the finite resolution and limited statistical accuracy of PET data prevents unambiguous identification of the actual object boundary by the observer. The large interobserver variability of manual delineation might be especially problematic for the evaluation of follow up studies by different observers. Furthermore, purely manual delineation is inherently tedious and time consuming and thus ill suited for routine use.

We, therefore, developed an algorithm for tumor delineation which takes as user input only a sub-volume of the PET image containing one or more tumors. The algorithm then iteratively detects the tracer concentration in the background of the tumor and finally applies a background adjusted threshold in percent of the maximum concentration of tumor. This leads to robust results as long as the tracer distribution inside the tumor is not strongly heterogeneous. For accurate delineation of such tumors we extended the algorithm. In this extension for each tentative tumor voxel a background adjusted threshold is computed, where background and maximum value are computed only in the close vicinity of the voxel. This local method is slower than the global method (delineation time: 10s to 1min, depending on tumor and voxel size), but is able to delineate also very heterogeneous tumors accurately.

Both algorithms are essential parts of the PET software ROVER (see below). They are used for tumor delineation by researchers in Dresden and by many ROVER users in preclinical and clinical research.


Accurate quantification of tumor metabolism


High metabolism of cancer cells is an indication of fast tumor growth and, therefore, can provide important information for therapy planning and therapy outcome. PET allows to quantify tumor metabolism, but absolute quantification is very time consuming and limited to a small part of the patient. Therefore, in clinical routine the standardized uptake value (SUV) is typically used as surrogate parameter for tumor metabolism. However, SUV quantification has well known limitations and correlates rather weak with the actual metabolism.

We recently introduced the parameter standardized uptake ratio (SUR), which is the ratio of tumor SUV and blood SUV corrected for variations in uptake time (the time between injection of the tracer and measurement). SUR correlates much better with tumor metabolism than SUV and we were able to show that this improved correlation translates into better therapy outcome prediction in patients with esophageal cancer and lung cancer. In ongoing studies we currently investigate if SUR predicts therapy outcome also for patients with other tumors.


Tumor shape as a predictor of therapy outcome


Intratumoral spatial variation of cellularity, angiogenesis, necrosis, etc. is typical for aggressive tumors and can have influence on therapy planning if known. Since such variations lead to a heterogeneous distribution of tumor metabolism, they can be quantified, in principle, with PET by analyzing the distribution of tracer uptake inside the tumor. However, the limited spatial resolution of PET leads to strong partial volume effects which artificially increase the measured tumor heterogeneity.

On the other hand it can be hypothesized that variation of cellularity etc. also results in an irregular shape of the region of interest delineating the elevated tracer uptake. We quantify irregular shape as deviation from sphere shape by computing the tumor asphericity (ASP)
ASP equation
where S is the surface area of the tumor and V the volume. For spherical geometries ASP is zero. For all other 3D geometries ASP is larger than zero and it quantifies the relative increase of the surface area of a 3D structure compared to the surface area of a sphere with same volume.

We were able to show that ASP predicts therapy outcome in various tumor diseases (head and neck cancer, esophageal cancer, neuroendocrine tumors and neuroblastoma) and, therefore has the potential to provide important information for therapy planning. We currently investigate if these results can be confirmed in large patient groups.


Translation into clinical application


Image evaluation algorithms and new PET parameters are integrated into the software ROVER as soon as possible. In this way a fast translation into preclinical and clinical research and finally into clinical application is achieved. For example all above mentioned research results are based on evaluations of the corresponding PET data performed with ROVER.

ROVER is an evaluation program designed for the fast interactive visualization, analysis and easy data and image export of tomographic image data. It combines several functionalities related to the efficient evaluation of these data with the emphasis on (human and small animal) PET. Nevertheless many features are also useful with other imaging modalities (e.g. CT, MRI).

ROVER is developed by our department starting in 2002. In 2005 ROVER became commercially available and is distributed by our cooperation partner ABX GmbH, Radeberg, Germany. Currently about 50 institutions world wide are using ROVER for preclinical and clinical research.