MR-guided attenuation map for prostate PET-MRI: an intensity and morphologic-based segmentation approach for generating a five-class attenuation map in pelvic region

Construction of a Phantom for Image Quality Evaluation in PET/MRI System

Background: There is no phantom for image quality test in magnetic resonance imaging combined with positron emission tomography systems (PET/MRI systems). In MRI, radioactive water phantom containing 2-deoxy-2-[F-18] fluoro-D-glucose (¹⁸F-FDG) cannot be used due to the dielectric effect. Even for phantoms filled with MR-available solutions, the source current of the RF coil is strongly disturbed as the diameter of the phantom increases. Stable MR images require proper phantom size and solution selection. Previous reports have not provided these details. Other than that, few existing phantoms evaluate negative signals such as N-13 ammonia (¹³N-NH₃). We created a phantom for PET/MRI system for image quality test. Methods: The phantom for the PET/MRI system was assembled in two portions. One portion is a signal part containing ¹⁸F-FDG radioactive water. The other portion is filled with polyvinyl alcohol glue to construct MRI image to generate µ-map. The glue part is allowed to rewrite the table position overlaps with the first layer, and attenuation correction is performed. Signals are set as positive (4 times and twice higher than background radioactivity) and negative (no radioactivity) columns with different sizes (15 mm φ and 7 mm φ). The PET images with X-ray computed tomography-based attenuation correction (CT-AC) and MRI-AC were evaluated by %-contrasts, variation and uniformity. Results: The %-contrasts of the positive shallow signals with PET/magnetic resonance (MR) and PET/CT were 41.8% and 45.4%, respectively. And it of the positive deep signals with PET/MR and PET/CT were 40.7% and 44.9%. On the other hand, the %-contrasts of the negative shallow signals with PET/MR and PET/CT were 62.3% and 65.6%, respectively. And it of the negative deep signals with PET/MR and PET/CT were 60.7% and 63.7%. Moreover, the % Nj index of uniformity was 2.0% on PET/MRI images and 0.34% on PET/CT images. For negative signals that assume a decrease in myocardial blood flow, The image quality of MR-AC was almost the same as that of CT-AC. Consistency between the images after CT-AC and MR-AC correction were confirmed, and in particular, a stable MR-AC µ-map was obtained in the phantom study. Conclusion: The suggested prototype phantom for generating µ-map is reasonable and useful for evaluating PET/MRI image quality, based on the present standard.

Attenuation correction for human PET/MRI studies

Attenuation correction has been one of the main methodological challenges in the integrated positron emission tomography and magnetic resonance imaging (PET/MRI) field. As standard transmission or computed tomography approaches are not available in integrated PET/MRI scanners, MR-based attenuation correction approaches had to be developed. Aspects that have to be considered for implementing accurate methods include the need to account for attenuation in bone tissue, normal and pathological lung and the MR hardware present in the PET field-of-view, to reduce the impact of subject motion, to minimize truncation and susceptibility artifacts, and to address issues related to the data acquisition and processing both on the PET and MRI sides. The standard MR-based attenuation correction techniques implemented by the PET/MRI equipment manufacturers and their impact on clinical and research PET data interpretation and quantification are first discussed. Next, the more advanced methods, including the latest generation deep learning-based approaches that have been proposed for further minimizing the attenuation correction related bias are described. Finally, a future perspective focused on the needed developments in the field is given.

Direct attenuation correction of brain PET images using only emission data via a deep convolutional encoder-decoder (Deep-DAC)

OBJECTIVE

To obtain attenuation-corrected PET images directly from non-attenuation-corrected images using a convolutional encoder-decoder network.

METHODS

Brain PET images from 129 patients were evaluated. The network was designed to map non-attenuation-corrected (NAC) images to pixel-wise continuously valued measured attenuation-corrected (MAC) PET images via an encoder-decoder architecture. Image quality was evaluated using various evaluation metrics. Image quantification was assessed for 19 radiomic features in 83 brain regions as delineated using the Hammersmith atlas (n30r83). Reliability of measurements was determined using pixel-wise relative errors (RE; %) for radiomic feature values in reference MAC PET images.

RESULTS

Peak signal-to-noise ratio (PSNR) and structural similarity index metric (SSIM) values were 39.2 ± 3.65 and 0.989 ± 0.006 for the external validation set, respectively. RE (%) of SUV_mean was - 0.10 ± 2.14 for all regions, and only 3 of 83 regions depicted significant differences. However, the mean RE (%) of this region was 0.02 (range, - 0.83 to 1.18). SUV_max had mean RE (%) of - 3.87 ± 2.84 for all brain regions, and 17 regions in the brain depicted significant differences with respect to MAC images with a mean RE of - 3.99 ± 2.11 (range, - 8.46 to 0.76). Homogeneity amongst Haralick-based radiomic features had the highest number (20) of regions with significant differences with a mean RE (%) of 7.22 ± 2.99.

CONCLUSIONS

Direct AC of PET images using deep convolutional encoder-decoder networks is a promising technique for brain PET images. The proposed deep learning method shows significant potential for emission-based AC in PET images with applications in PET/MRI and dedicated brain PET scanners.

KEY POINTS

• We demonstrate direct emission-based attenuation correction of PET images without using anatomical information. • We performed radiomics analysis of 83 brain regions to show robustness of direct attenuation correction of PET images. • Deep learning methods have significant promise for emission-based attenuation correction in PET images with potential applications in PET/MRI and dedicated brain PET scanners.

The effect of occupational noise exposure on systolic blood pressure, diastolic blood pressure and salivary cortisol level among automotive assembly workers

Introduction. Both auditory and non-auditory health can be affected by exposure to occupational noise. The aim of this study was to evaluate the association of high occupational noise (at three levels) with systolic blood pressure (SBP), diastolic blood pressure (DBP) and salivary cortisol concentration in an automotive factory. Methods. This study was carried out on 78 male workers from a factory who worked in the warehouse unit (control group: 60-70 dB(A)) and assembly units (two test groups: 75-85 and 85-95 dB(A)). Data were collected through questionnaires and measurement of SBP, DBP, saliva cortisol concentration and body mass index (BMI), prior to and post occupational noise exposure. The measurements were performed twice/day and repeated after 10 days. Results. For each occupational noise level group, there was no significant association between cortisol level with age and BMI. The SBP and DBP in all groups increased significantly. Also, the average cortisol levels were similar in different groups before noise exposure, but were statistically different after occupational noise exposure. Conclusions. High occupational noise exposure increases the cortisol level and blood pressure, which are the major risk factors of cardiovascular disease.

Capturing Bone Signal in MRI of Pelvis, as a Large FOV Region, Using TWIST Sequence and Generating a 5-Class Attenuation Map for Prostate PET/MRI Imaging

Purpose:

Prostate imaging is a major application of hybrid positron emission tomography/magnetic resonance imaging (PET/MRI). Currently, MRI-based attenuation correction (MRAC) for whole-body PET/MRI in which the bony structures are ignored is the main obstacle to successful implementation of the hybrid modality in the clinical work flow. Ultrashort echo time sequence captures bone signal but needs specific hardware–software and is challenging in large field of view (FOV) regions, such as pelvis. The main aims of the work are (1) to capture a part of the bone signal in pelvis using short echo time (STE) imaging based on time-resolved angiography with interleaved stochastic trajectories (TWIST) sequence and (2) to consider the bone in pelvis attenuation map (µ-map) to MRAC for PET/MRI systems.

Procedures:

Time-resolved angiography with interleaved stochastic trajectories, which is routinely used for MR angiography with high temporal and spatial resolution, was employed for fast/STE MR imaging. Data acquisition was performed in a TE of 0.88 milliseconds (STE) and 4.86 milliseconds (long echo time [LTE]) in pelvis region. Region of interest (ROI)-based analysis was used for comparing the signal-to-noise ratio (SNR) of cortical bone in STE and LTE images. A hybrid segmentation protocol, which is comprised of image subtraction, a Fuzzy-based segmentation, and a dedicated morphologic operation, was used for generating a 5-class µ-map consisting of cortical bone, air cavity, fat, soft tissue, and background (µ-map_MR-5c). A MR-based 4-class µ-map (µ-map_MR-4c) that considered soft tissue rather than bone was generated. As such, a bilinear (µ-map_CT-ref), 5 (µ-map_CT-5c), and 4 class µ-map (µ-map_CT-4c) based on computed tomography (CT) images were generated. Finally, simulated PET data were corrected using µ-map_MR-5c (PET-MRAC5c), µ-map_MR-4c (PET-MRAC4c), µ-map_CT-5c (PET-CTAC5c), and µ-map_CT-ref (PET-CTAC).

Results:

The ratio of SNR_bone to SNR_{air cavity} in LTE images was 0.8, this factor was increased to 4.4 in STE images. The Dice, Sensitivity, and Accuracy metrics for bone segmentation in proposed method were 72.4% ± 5.5%, 69.6% ± 7.5%, and 96.5% ± 3.5%, respectively, where the segmented CT served as reference. The mean relative error in bone regions in the simulated PET images were −13.98% ± 15%, −35.59% ± 15.41%, and 1.81% ± 12.2%, respectively, in PET-MRAC5c, PET-MRAC4c, and PET-CTAC5c where PET-CTAC served as the reference. Despite poor correlation in the joint histogram of µ-map_MR-4c versus µ-map_CT-5c (R² > 0.78) and PET-MRAC4c versus PET-CTAC5c (R² = 0.83), high correlations were observed in µ-map_MR-5c versus µ-map_CT-5c (R² > 0.94) and PET-MRAC5c versus PET-CTAC5c (R² > 0.96).

Conclusions:

According to the SNR_{STE, pelvic bone}, the cortical bone can be separate from air cavity in STE imaging based on TWIST sequence. The proposed method generated an MRI-based µ-map containing bone and air cavity that led to more accurate tracer uptake estimation than MRAC4c. Uptake estimation in hybrid PET/MRI can be improved by employing the proposed method.