18F-fluorodeoxyglucose positron emission tomography (FDG-PET) can provide estimates of parameters related to the delivery, retention, and metabolism of glucose, and therefore has been proposed as a method of characterizing the response of tumors to treatment
[1–4]. To assess treatment response, semi-quantitative parameters, such as the standard uptake value (SUV) which is derived from static images
[5, 6], or quantitative parameters which are derived from dynamic images,
[7–9], are measured at different time points during the course of treatment. Changes in these values on a region of interest (ROI) basis are then used to assess or predict the response of tumors to therapy
[10, 11]. In recent years, there has been increasing interest in assessing imaging data at the voxel level, rather than at the ROI level, with the hypothesis that important information on tumor heterogeneity is discarded when an ROI average is performed
[12, 13]. In order to optimally perform such an analysis during therapy, the data sets acquired at different time points must be accurately co-registered so that similar sections of tissues can be compared. Towards this end, we have introduced a technique that allows for co-registering breast MRI data acquired at different imaging sessions during therapy
[14, 15]. In this effort, we seek to amend this method to perform longitudinal registration of PET/CT data of the breast in order to enable voxel level analysis of changes observed in FDG-PET images acquired during therapy.
The motivation for developing a method for characterizing changes in FDG-PET scans at the voxel level comes from the fact that probing tumor heterogeneity, as well as changes in that heterogeneity over time, is gaining prominence as a research topic
. As MRI studies have shown changes in heterogeneity are predictive of response
, it is natural to apply a similar approach in studying FDG-PET data. Indeed, as FDG-PET is becoming more accepted in assessing changes in tumor metabolism over time (see, e.g., the most recent version of the Response Evaluation Criteria in Solid Tumors, RECIST
[17, 18]), the ability to compare the same section of tissue will become increasingly important. While the limited spatial resolution of PET (compared to, e.g., MRI and CT) is a potential barrier for performing such a comparison, it certainly must be addressed to determine if such a promising approach is viable. As has been noted recently
, volumes of interest established just prior to the initiation of therapy (i.e., baseline images) are not easily transformed to images acquired at later time points, thereby making attempts to study changes in heterogeneity challenging. In this effort, we present one approach to potentially mitigate such difficulties.
There are three main challenges in performing longitudinal registration of breast FDG-PET images: (1) differences in patient positioning between consecutive imaging sessions, (2) changes in tumor shape and volume between imaging sessions, and (3) the relatively low spatial resolution of PET images. To minimize the error introduced by differences in patient positioning, we have designed a support device that allows for PET imaging in the prone position. This allows for the breasts to lay pendant during the scanning session, rather than flat against the chest as in the supine position. It is important to note that we are not the first to introduce this technique; Moy et al.
 have used prone PET imaging of the breast to facilitate registration of PET data to MRI data, where it has been applied to assist in diagnosis
[21, 22]. The second problem, addressing the changes in tumor shape and volume that occur due to therapy, requires a registration technique that can maximally align the breast volumes while minimally distorting the tumor, the volume of which must be kept true to what is measured at each time point
[14, 15]. That is, if V(t
1) is the tumor volume at time t
1 (e.g., pre-treatment), and V
1) is the tumor volume after registration to the post-treatment image, then the registration scheme should minimize the differences between V(t
1) and V
1). Similar comments apply for time t
2. To achieve this, we developed and applied a spatially constrained, non-rigid registration method previously used for longitudinal registration of MR images, which incorporates a tumor volume-preserving constraint. The third problem is addressed by applying the algorithm on the computed tomography (CT) images that are acquired during the PET/CT acquisition and applying the resulting transformation to the PET data. We tested our approach on ten patients receiving neoadjuvant chemotherapy for breast cancer, who were scanned at three different time points: pre-therapy, after one cycle of therapy, and at the conclusion of therapy. The PET/CT data at the first two time points were co-registered to the data at the third time point by applying constrained and unconstrained registration algorithms. Both qualitative visual comparisons, as well as a quantitative analysis of the change in the median tumor volume determined from the SUV maps and bending energy obtained from the deformation fields (DF), were employed to evaluate the performance of the registration algorithms.