For quantitative PET information, correction of tissue photon attenuation is mandatory. Generally in conventional PET, the attenuation map is obtained from a transmission scan, which uses a rotating radionuclide source, or from the CT scan in a combined PET/CT scanner. In the case of PET/MRI scanners currently under development, insufficient space for the rotating source exists; the attenuation map can be calculated from the MR image instead. This task is challenging because MR intensities correlate with proton densities and tissue-relaxation properties, rather than with attenuation-related mass density. Methods: We used a combination of local pattern recognition and atlas registration, which captures global variation of anatomy, to predict pseudo-CT images from a given MR image. These pseudo-CT images were then used for attenuation correction, as the process would be performed in a PET/CT scanner. Results: For human brain scans, we show on a database of 17 MR/CT image pairs that our method reliably enables estimation of a pseudo-CT image from the MR image alone. On additional datasets of MRI/PET/ CT triplets of human brain scans, we compare MRI-based attenuation correction with CT-based correction. Our approach enables PET quantification with a mean error of 3.2% for predefined regions of interest, which we found to be clinically not significant. However, our method is not specific to brain imaging, and we show promising initial results on 1 whole-body animal dataset. Conclusion: This method allows reliable MRI-based attenuation correction for human brain scans. Further work is necessary to validate the method for whole-body imaging.

PET/MRI is an emerging dual-modality imaging technology that requires new approaches to PET attenuation correction (AC). We assessed 2 algorithms for whole-body MRI-based AC (MRAC): a basic MR image segmentation algorithm and a method based on atlas registration and pattern recognition (AT&PR). Methods: Eleven patients each underwent a whole-body PET/ CT study and a separate multibed whole-body MRI study. The MR image segmentation algorithm uses a combination of image thresholds, Dixon fat-water segmentation, and component analysis to detect the lungs. MR images are segmented into 5 tissue classes (not including bone), and each class is assigned a default linear attenuation value. The AT&PR algorithm uses a database of previously aligned pairs of MRI/CT image volumes. For each patient, these pairs are registered to the patient MRI volume, and machine-learning techniques are used to predict attenuation values on a continuous scale. MRAC methods are compared via the quantitative analysis of AC PET images using volumes of interest in normal organs and on lesions. We assume the PET/CT values after CT-based AC to be the reference standard. Results: In regions of normal physiologic uptake, the average error of the mean standardized uptake value was 14.1% 6 10.2% and 7.7% 6 8.4% for the segmentation and the AT&PR methods, respectively. Lesion-based errors were 7.5% 6 7.9% for the segmentation method and 5.7% 6 4.7% for the AT&PR method. Conclusion: The MRAC method using AT&PR provided better overall PET quantification accuracy than the basic MR image segmentation approach. This better quantification was due to the significantly reduced volume of errors made regarding volumes of interest within or near bones and the slightly reduced volume of errors made regarding areas outside the lungs.

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Contrast-enhanced CT is superior compared with PET alone to predict the extent of PC. In our patient group, the combination of both modalities (contrast enhanced PET/CT) yielded the best results and proved to be a useful tool for selecting candidates for peritonectomy and HIPEC.

OBJECTIVES To evaluate the potential of 11C‐choline‐positron emission tomography (PET)/computed tomography (CT) for planning surgery in patients with prostate cancer and prostate‐specific antigen (PSA) relapse after treatment with curative intent. PATIENTS AND METHODS We retrospectively reviewed the charts of 10 patients with PSA recurrence after either external beam radiation (two) or radical retropubic prostatectomy (eight) for prostate cancer, and who had a laparoscopic lymphadenectomy for suspicious lymph nodes detected on 11C‐choline‐PET/CT. The histological results and PET/CT findings were compared. RESULTS In all, 22 suspicious lymph nodes were found on PET/CT, and 14 on conventional CT or magnetic resonance imaging. Comparing the conventional imaging showed concordance in 13 lymph nodes. Three of the 10 patients had no metastatic lymph node disease on definitive histology. The mean (sd) PSA level for these patients was 1.0 (0.4) ng/mL, whereas that in patients with lymph node metastases was 15.1 (9.2) ng/mL (statistically significant difference, P < 0.05). The positive predictive value was seven of 10. All of the patients initially regressed, with PSA increases after lymphadenectomy. Two of the patients are being managed by watchful waiting, two had radiotherapy of the prostate fossa and two had chemotherapy with docetaxel. Four patients were treated by hormone‐deprivation therapy. After a mean (sd) follow up of 11 (7) months, one patient died, one has PSA progression, but none of those with negative histology has clinical signs of local recurrence. CONCLUSIONS 11C‐choline‐PET is a valuable tool for detecting recurrent prostate cancer, but the limited positive predictive value should lead to a critical interpretation of the results.