PET/MR Technology: Advancement and Challenges

When this article was written, it coincided with the 11th anniversary of the installation of our PET/MR device in Munich. In fact, this was the first fully integrated device to be in clinical use. During this time, we have observed many interesting behaviors, to put it kindly. However, it is more critical that in this process, our understanding of the system also improved - including the advantages and limitations from a technical, logistical, and medical perspective. The last decade of PET/MRI research has certainly been characterized by most sites looking for a "key application." There were many ideas in this context and before and after the devices became available, some of which were based on the earlier work with integrating data from single devices. These involved validating classical PET methods with MRI (eg, perfusion or oncology diagnostics). More important, however, were the scenarios where intermodal synergies could be expected. In this review, we look back on this decade-long journey, at the challenges overcome and those still to come

Is there more than meets the eye in PSMA imaging in prostate cancer with PET/MRI? Looking closer at uptake time, correlation with PSA and Gleason score

Abstract Background In patients with increasing PSA and suspicion for prostate cancer, but previous negative biopsies, PET/MRI is used to test for tumours and target potential following biopsy. We aimed to determine different PSMA PET timing effects on signal kinetics and test its correlation with the patients’ PSA and Gleason scores (GS). Methods A total of 100 patients were examined for 900 s using PET/MRI approximately 1–2 h p.i. depending on the tracer used (68Ga-PSMA-11, 18F-PSMA-1007 or 18F-rhPSMA7). The scans were reconstructed in static and dynamic mode (6 equal frames capturing “late” PSMA dynamics). TACs were computed for detected lesions as well as linear regression plots against time for static (SUV) and dynamic (SUV, SUL, and percent injected dose per gram) parameters. All computed trends were tested for correlation with PSA and GS. Results Static and dynamic scans allowed unchanged lesion detection despite the difference in statistics. For all tracers, the lesions in the pelvic lymph nodes and bones had a mostly negative activity concentration trend (78% and 68%, resp.), while a mostly positive, stronger trend was found for the lesions in the prostate and prostatic fossa following RPE (84% and 83%, resp.). In case of 68Ga-PSMA-11, a strong negative (R min = − 0.62, R max = − 0.73) correlation was found between the dynamic parameters and the PSA. 18F-PSMA-1007 dynamic data showed no correlation with PSA, while for 18F-rhPSMA7 dynamic data, it was consistently low positive (R min = 0.29, R max = 0.33). All tracers showed only moderate correlation against GS (R min = 0.41, R max = 0.48). The static parameters showed weak correlation with PSA (R min = 0.24, R max = 0.36) and no correlation with GS. Conclusion “Late” dynamic PSMA data provided additional insight into the PSMA kinetics. While a stable moderate correlation was found between the PSMA kinetics in pelvic lesions and GS, a significantly variable correlation with the PSA values was shown depending on the radiotracer used, the highest being consistently for 68Ga-PSMA-11. We reason that with such late dynamics, the PSMA kinetics are relatively stable and imaging could even take place at earlier time points as is now in the clinical routine

Multiparametric MR and PET Imaging of Intratumoral Biological Heterogeneity in Patients with Metastatic Lung Cancer Using Voxel-by-Voxel Analysis

<div><p>Objectives</p><p>Diffusion-weighted magnetic resonance imaging (DW-MRI) and imaging of glucose metabolism by positron emission tomography (FDG-PET) provide quantitative information on tissue characteristics. Combining the two methods might provide novel insights into tumor heterogeneity and biology. Here, we present a solution to analyze and visualize the relationship between the apparent diffusion coefficient (ADC) and glucose metabolism on a spatially resolved voxel-by-voxel basis using dedicated quantitative software.</p><p>Materials and Methods</p><p>In 12 patients with non small cell lung cancer (NSCLC), the primary tumor or metastases were examined with DW-MRI and PET using ¹⁸F-fluorodeoxyglucose (FDG). The ADC’s from DW-MRI were correlated with standardized-uptake-values on a voxel-by-voxel basis using custom made software (Anima M³P). For cluster analysis, we used prospectively defined thresholds for ¹⁸F-FDG and ADC to define tumor areas of different biological activity.</p><p>Results</p><p>Combined analysis and visualization of ADC maps and PET data was feasible in all patients. Spatial analysis showed relatively homogeneous ADC values over the entire tumor area, whereas FDG showed a decreasing uptake towards the tumor center. As expected, restricted water diffusivity was notable in areas with high glucose metabolism but was also found in areas with lower glucose metabolism. In detail, 72% of all voxels showed low ADC values (<1.5x10^-3 mm²/s) and high tracer uptake of ¹⁸F-FDG (SUV>3.6). However, 83% of the voxels with low FDG uptake also showed low ADC values, increasingly towards the tumor center.</p><p>Conclusions</p><p>Multiparametric analysis and visualization of DW-MRI and FDG-PET is feasible on a spatially resolved voxel-by-voxel respectively cluster basis using dedicated imaging software. Our preliminary data suggest that water diffusivity and glucose metabolism in metastatic NSCLC are not necessarily correlated in all tumor areas.</p></div

Using a threshold for low and high tracer uptake of SUV = 3.6 for ¹⁸F-FDG and ADC = 1.5x10^-3 mm²/s for restricted and less restricted water diffusivity, up to four tumor cluster of different biologically activity could be defined.

<p>Here, the relative distribution (%) of the different cluster from the tumor periphery to the center is shown. The relative number of voxels with high uptake of ¹⁸F-FDG and lower ADC values (yellow, vital tumor) decreases to the tumor center, oppositely for voxels with low uptake of ¹⁸F-FDG and lower ADC values (turquoise, high density of cells with low tumor activity or low density of tumor cells but dense tumor stroma caused by desmoplastic reactions). Voxels from the clusters with less restricted water diffusivity (SUV_high respectively SUV_low) can be found in all tumor parts, indicative either for hypoxia (SUV_high, red) or necrosis (SUV_low, white). However, the highest amount of voxels from the SUV_high/ADC_high cluster can be found within the transitional zone, whereas the voxel of the SUV_low/ADC_high cluster increase to central tumor parts.</p

Subgroup analysis for the lung tumors only, showing a scatter plot of the voxel-by-voxel correlation of ADC and ¹⁸F-FDG uptake.

<p>No correlation between the ¹⁸F-FDG uptake and ADC can be demonstrated. The distribution of the voxel data is comparable to <b><a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0132386#pone.0132386.g003" target="_blank">Fig 3</a></b> and 75% of the voxels are located in the SUVhigh/ADClow cluster.</p

A-B. Scatter plot of the correlations of ADC and ¹⁸F-FDG uptake (B).

<p>The colors from (A) denote the localization of the voxels (from red: periphery to blue: center). Voxels with high uptake of ¹⁸F-FDG and lower ADC values can be found in all tumor parts (B). Surprisingly, there is a substantial number of voxels with low uptake of ¹⁸F-FDG and lower ADC values, mainly located in the central areas, maybe due to high density of cells with low tumor activity or low density of tumor cells but dense tumor stroma caused by desmoplastic reactions (B). For higher ADC values >1.5 x10-3 mm²/s the ¹⁸F-FDG values are widely distributed (B).</p

A-C. The tumors were divided into voxel rings starting from the periphery to the tumor center (A).

<p>Depending on the tumor size, up to 12 voxel rings could be defined for the largest tumors. From the entire cohort of patients, the data for each voxel ring are shown for ADC (B) and ¹⁸F-FDG (C) (grey bars: median; white bars: 25th to 75th percentile). Note that due to partial volume effects, there is an increase of SUV’s in the first approximately 4 voxel rings (gray-white shaded, which were omitted for further analysis), followed by a plateau. Subsequently, the SUV’s gradually decrease in the more central tumor parts. This suggests that glucose metabolism is more intense in the peripheral tumor parts and less in the tumor center. However, ADC values are more evenly distributed among the tumor.</p

A-D. Patient with a primary NSCLC of the left upper lobe infiltrating the chest wall (A: ADC map from DW-MRI).

<p>The tumor shows relatively homogenously low ADC values and high ¹⁸F-FDG uptake (B image fusion of ¹⁸F-FDG PET and the ADC map) with some focal spots. A ROI is placed around the tumor (C) and the Anima M³P software displays a scatter plot of the voxels within the ROI (D). Thresholds for low and high tracer uptake (y-axis) and low and high ADC values (x-axis) were defined (SUV = 3.6 for ¹⁸F-FDG and ADC 1.5 x10^-3 mm²/s) and the localization of the voxels of each quadrant (yellow: SUV_high/ADC_low; red: SUV_high/ADC_high; blue: SUV_low/ADC_low; gray: SUV_low/ADC_high) can be displayed in the fusion image (D). Note, most tumor parts show intense glucose metabolism with restricted water diffusivity (yellow), however some spots of the tumor also show intense glucose metabolism despite less restricted water diffusivity (red).</p

Relationship between the degree of water diffusivity and glucose metabolism, and their potential biological correlate.

<p>Relationship between the degree of water diffusivity and glucose metabolism, and their potential biological correlate.</p