qPSMA: Semiautomatic Software for Whole-Body Tumor Burden Assessment in Prostate Cancer Using 68Ga-PSMA11 PET/CT.

Our aim was to introduce and validate qPSMA, a semiautomatic software package for whole-body tumor burden assessment in prostate cancer patients using 68Ga-prostate-specific membrane antigen (PSMA) 11 PET/CT. Methods: qPSMA reads hybrid PET/CT images in DICOM format. Its pipeline was written using Python and C++ languages. A bone mask based on CT and a normal-uptake mask including organs with physiologic 68Ga-PSMA11 uptake are automatically computed. An SUV threshold of 3 and a liver-based threshold are used to segment bone and soft-tissue lesions, respectively. Manual corrections can be applied using different tools. Multiple output parameters are computed, that is, PSMA ligand-positive tumor volume (PSMA-TV), PSMA ligand-positive total lesion (PSMA-TL), PSMA SUVmean, and PSMA SUVmax Twenty 68Ga-PSMA11 PET/CT data sets were used to validate and evaluate the performance characteristics of qPSMA. Four analyses were performed: validation of the semiautomatic algorithm for liver background activity determination, assessment of intra- and interobserver variability, validation of data from qPSMA by comparison with Syngo.via, and assessment of computational time and comparison of PSMA PET-derived parameters with serum prostate-specific antigen. Results: Automatic liver background calculation resulted in a mean relative difference of 0.74% (intraclass correlation coefficient [ICC], 0.996; 95%CI, 0.989;0.998) compared with METAVOL. Intra- and interobserver variability analyses showed high agreement (all ICCs > 0.990). Quantitative output parameters were compared for 68 lesions. Paired t testing showed no significant differences between the values obtained with the 2 software packages. The ICC estimates obtained for PSMA-TV, PSMA-TL, SUVmean, and SUVmax were 1.000 (95%CI, 1.000;1.000), 1.000 (95%CI, 1.000;1.000), 0.995 (95%CI, 0.992;0.997), and 0.999 (95%CI, 0.999;1.000), respectively. The first and second reads for intraobserver variability resulted in mean computational times of 13.63 min (range, 8.22-25.45 min) and 9.27 min (range, 8.10-12.15 min), respectively (P = 0.001). Highly significant correlations were found between serum prostate-specific antigen value and both PSMA-TV (r = 0.72, P < 0.001) and PSMA-TL (r = 0.66, P = 0.002). Conclusion: Semiautomatic analyses of whole-body tumor burden in 68Ga-PSMA11 PET/CT is feasible. qPSMA is a robust software package that can help physicians quantify tumor load in heavily metastasized prostate cancer patients.

Segmentation of Skeleton and Organs in Whole-Body CT Images via Iterative Trilateration.

Whole body oncological screening using CT images requires a good anatomical localisation of organs and the skeleton. While a number of algorithms for multi-organ localisation have been presented, developing algorithms for a dense anatomical annotation of the whole skeleton, however, has not been addressed until now. Only methods for specialised applications, e.g., in spine imaging, have been previously described. In this work, we propose an approach for localising and annotating different parts of the human skeleton in CT images. We introduce novel anatomical trilateration features and employ them within iterative scale-adaptive random forests in a hierarchical fashion to annotate the whole skeleton. The anatomical trilateration features provide high-level long-range context information that complements the classical local context-based features used in most image segmentation approaches. They rely on anatomical landmarks derived from the previous element of the cascade to express positions relative to reference points. Following a hierarchical approach, large anatomical structures are segmented first, before identifying substructures. We develop this method for bone annotation but also illustrate its performance, although not specifically optimised for it, for multi-organ annotation. Our method achieves average dice scores of 77.4 to 85.6 for bone annotation on three different data sets. It can also segment different organs with sufficient performance for oncological applications, e.g., for PET/CT analysis, and its computation time allows for its use in clinical practice.

Exploring New Multimodal Quantitative Imaging Indices for the Assessment of Osseous Tumor Burden in Prostate Cancer Using 68Ga-PSMA PET/CT.

PET combined with CT and prostate-specific membrane antigen (PSMA) ligands has gained significant interest for staging prostate cancer (PC). In this study, we propose 2 multimodal quantitative indices as imaging biomarkers for the assessment of osseous tumor burden using 68Ga-PSMA PET/CT and present preliminary clinical data. Methods: We defined 2 bone PET indices (BPIs) that incorporate anatomic information from CT and functional information from 68Ga-PSMA PET: BPIVOL is the percentage of bone volume affected by tumor and BPISUV additionally considers the level of PSMA expression. We describe a semiautomatic computation method based on segmentation of bones in CT and of lesions in PET. Data from 45 patients with castration-resistant PC and bone metastases during 223Ra-dichloride were retrospectively analyzed. We evaluated the computational stability and reproducibility of the proposed indices and explored their relation to the prostate-specific antigen blood value, the bone scan index (BSI), and disease classification using PERCIST. Results: On the technical side, BPIVOL and BPISUV showed an interobserver maximum difference of 3.5%, and their computation took only a few minutes. On the clinical side, BPIVOL and BPISUV showed significant correlations with BSI (r = 0.76 and 0.74, respectively, P < 0.001) and prostate-specific antigen values (r = 0.57 and 0.54, respectively, P < 0.01). When the proposed indices were compared against expert rating using PERCIST, BPIVOL and BPISUV showed better agreement than BSI, indicating their potential for objective response evaluation. Conclusion: We propose the evaluation of BPIVOL and BPISUV as imaging biomarkers for 68Ga-PSMA PET/CT in a prospective study exploring their potential for outcome prediction in patients with bone metastases from PC.

Atlas construction for dynamic (4D) PET using diffeomorphic transformations.

A novel dynamic (4D) PET to PET image registration procedure is proposed and applied to multiple PET scans acquired with the high resolution research tomograph (HRRT), the highest resolution human brain PET scanner available in the world. By extending the recent diffeomorphic log-demons (DLD) method and applying it to multiple dynamic [11C]raclopride scans from the HRRT, an important step towards construction of a PET atlas of unprecedented quality for [11C]raclopride imaging of the human brain has been achieved. Accounting for the temporal dimension in PET data improves registration accuracy when compared to registration of 3D to 3D time-averaged PET images. The DLD approach was chosen for its ease in providing both an intensity and shape template, through iterative sequential pair-wise registrations with fast convergence. The proposed method is applicable to any PET radiotracer, providing 4D atlases with useful applications in high accuracy PET data simulations and automated PET image analysis.