Structure-Function-Based Quantitative Brain Image Analysis

DeepTOFSino: A deep learning model for synthesizing full-dose time-of-flight bin sinograms from their corresponding low-dose sinograms

PURPOSE

Reducing the injected activity and/or the scanning time is a desirable goal to minimize radiation exposure and maximize patients' comfort. To achieve this goal, we developed a deep neural network (DNN) model for synthesizing full-dose (FD) time-of-flight (TOF) bin sinograms from their corresponding fast/low-dose (LD) TOF bin sinograms.

METHODS

Clinical brain PET/CT raw data of 140 normal and abnormal patients were employed to create LD and FD TOF bin sinograms. The LD TOF sinograms were created through 5% undersampling of FD list-mode PET data. The TOF sinograms were split into seven time bins (0, ±1, ±2, ±3). Residual network (ResNet) algorithms were trained separately to generate FD bins from LD bins. An extra ResNet model was trained to synthesize FD images from LD images to compare the performance of DNN in sinogram space (SS) vs implementation in image space (IS). Comprehensive quantitative and statistical analysis was performed to assess the performance of the proposed model using established quantitative metrics, including the peak signal-to-noise ratio (PSNR), structural similarity index metric (SSIM) region-wise standardized uptake value (SUV) bias and statistical analysis for 83 brain regions.

RESULTS

SSIM and PSNR values of 0.97 ± 0.01, 0.98 ± 0.01 and 33.70 ± 0.32, 39.36 ± 0.21 were obtained for IS and SS, respectively, compared to 0.86 ± 0.02and 31.12 ± 0.22 for reference LD images. The absolute average SUV bias was 0.96 ± 0.95% and 1.40 ± 0.72% for SS and IS implementations, respectively. The joint histogram analysis revealed the lowest mean square error (MSE) and highest correlation (R² = 0.99, MSE = 0.019) was achieved by SS compared to IS (R² = 0.97, MSE= 0.028). The Bland & Altman analysis showed that the lowest SUV bias (-0.4%) and minimum variance (95% CI: -2.6%, +1.9%) were achieved by SS images. The voxel-wise t-test analysis revealed the presence of voxels with statistically significantly lower values in LD, IS, and SS images compared to FD images respectively.

CONCLUSION

The results demonstrated that images reconstructed from the predicted TOF FD sinograms using the SS approach led to higher image quality and lower bias compared to images predicted from LD images.

Feasibility evaluation of PET scan-time reduction for diagnosing amyloid-β levels in Alzheimer's disease patients using a deep-learning-based denoising algorithm

PURPOSE

To shorten positron emission tomography (PET) scanning time in diagnosing amyloid-β levels thus increasing the workflow in centers involving Alzheimer's Disease (AD) patients.

METHODS

PET datasets were collected for 25 patients injected with ¹⁸F-AV45 radiopharmaceutical. To generate necessary training data, PET images from both normal-scanning-time (20-min) as well as so-called "shortened-scanning-time" (1-min, 2-min, 5-min, and 10-min) were reconstructed for each patient. Building on our earlier work on MCDNet (Monte Carlo Denoising Net) and a new Wasserstein-GAN algorithm, we developed a new denoising model called MCDNet-2 to predict normal-scanning-time PET images from a series of shortened-scanning-time PET images. The quality of the predicted PET images was quantitatively evaluated using objective metrics including normalized-root-mean-square-error (NRMSE), structural similarity (SSIM), and peak signal-to-noise ratio (PSNR). Furthermore, two radiologists performed subjective evaluations including the qualitative evaluation and a five-point grading evaluation. The denoising performance of the proposed MCDNet-2 was finally compared with those of U-Net, MCDNet, and a traditional denoising method called Gaussian Filtering.

RESULTS

The proposed MCDNet-2 can yield good denoising performance in 5-min PET images. In the comparison of denoising methods, MCDNet-2 yielded the best performance in the subjective evaluation although it is comparable with MCDNet in objective comparison (NRMSE, PSNR, and SSIM). In the qualitative evaluation of amyloid-β positive or negative results, MCDNet-2 was found to achieve a classification accuracy of 100%.

CONCLUSIONS

The proposed denoising method has been found to reduce the PET scan time from the normal level of 20 min to 5 min but still maintaining acceptable image quality in correctly diagnosing amyloid-β levels. These results suggest strongly that deep learning-based methods such as ours can be an attractive solution to the clinical needs to improve PET imaging workflow.

Validation technique and improvements introduced in a new dedicated brain positron emission tomograph (CareMiBrain)

The goal of developing a PET dedicated to the brain (CareMiBrain) has evolved from its initial approach to diagnosis and monitoring of dementias, to the more ambitious of creating a revolutionary clinical pathway for the knowledge and personalized treatment of multiple neurological diseases. The main innovative feature of CareMiBrain is the use of detectors with continuous crystals, which allow a high resolution determination of the depth of annihilation photons interaction within the thickness of the scintillation crystal. The technical validation phase of the equipment consisted of a pilot, prospective and observational study whose objective was to obtain the first images (40 patients), analyze them and make adjustments in the acquisition, reconstruction and correction parameters, comparing the image quality of the CareMiBrain equipment with that of the whole-body PET-CT. Thanks to the team meetings and the joint analysis of the images, it was possible to detect its weak points and some of its causes. The calibration, acquisition and processing processes, as well as the reconstruction, were optimized, the number of iterations was set to achieve the best signal-to-noise ratio, the random correction was optimized and a post-processing algorithm was included in the reconstruction algorithm. The main technical improvements implemented in this phase of technical validation carried out through collaboration of the Services of Nuclear Medicine and Neurology of the Hospital Clínico San Carlos with the Spanish company Oncovision will be exposed in a project financed with funds from the European Union (Horizon 2020 innovation program, 713323).

Validation technique and improvements introduced in a new dedicated brain positron emission tomograph (CareMiBrain)

The goal of developing a PET dedicated to the brain (CareMiBrain) has evolved from its initial approach to diagnosis and monitoring of dementias, to the more ambitious of creating a revolutionary clinical pathway for the knowledge and personalized treatment of multiple neurological diseases. The main innovative feature of CareMiBrain is the use of detectors with continuous crystals, which allow a high resolution determination of the depth of annihilation photons interaction within the thickness of the scintillation crystal. The technical validation phase of the equipment consisted of a pilot, prospective and observational study whose objective was to obtain the first images (40 patients), analyze them and make adjustments in the acquisition, reconstruction and correction parameters, comparing the image quality of the CareMiBrain equipment with that of the whole-body PET/CT. Thanks to the team meetings and the joint analysis of the images, it was possible to detect its weak points and some of its causes. The calibration, acquisition and processing processes, as well as the reconstruction, were optimized, the number of iterations was set to achieve the best signal-to-noise ratio, the random correction was optimized and a post-processing algorithm was included in the reconstruction algorithm. The main technical improvements implemented in this phase of technical validation carried out through collaboration of the Services of Nuclear Medicine and Neurology of the Hospital Clínico San Carlos with the Spanish company Oncovision will be exposed in a project financed with funds from the European Union (Horizon 2020 innovation program, 713323).

Projection Space Implementation of Deep Learning-Guided Low-Dose Brain PET Imaging Improves Performance over Implementation in Image Space

Our purpose was to assess the performance of full-dose (FD) PET image synthesis in both image and sinogram space from low-dose (LD) PET images and sinograms without sacrificing diagnostic quality using deep learning techniques. Methods: Clinical brain PET/CT studies of 140 patients were retrospectively used for LD-to-FD PET conversion. Five percent of the events were randomly selected from the FD list-mode PET data to simulate a realistic LD acquisition. A modified 3-dimensional U-Net model was implemented to predict FD sinograms in the projection space (PSS) and FD images in image space (PIS) from their corresponding LD sinograms and images, respectively. The quality of the predicted PET images was assessed by 2 nuclear medicine specialists using a 5-point grading scheme. Quantitative analysis using established metrics including the peak signal-to-noise ratio (PSNR), structural similarity index metric (SSIM), regionwise SUV bias, and first-, second- and high-order texture radiomic features in 83 brain regions for the test and evaluation datasets was also performed. Results: All PSS images were scored 4 or higher (good to excellent) by the nuclear medicine specialists. PSNR and SSIM values of 0.96 ± 0.03 and 0.97 ± 0.02, respectively, were obtained for PIS, and values of 31.70 ± 0.75 and 37.30 ± 0.71, respectively, were obtained for PSS. The average SUV bias calculated over all brain regions was 0.24% ± 0.96% and 1.05% ± 1.44% for PSS and PIS, respectively. The Bland-Altman plots reported the lowest SUV bias (0.02) and variance (95% confidence interval, -0.92 to +0.84) for PSS, compared with the reference FD images. The relative error of the homogeneity radiomic feature belonging to the gray-level cooccurrence matrix category was -1.07 ± 1.77 and 0.28 ± 1.4 for PIS and PSS, respectively. Conclusion: The qualitative assessment and quantitative analysis demonstrated that the FD PET PSS led to superior performance, resulting in higher image quality and lower SUV bias and variance than for FD PET PIS.

Novel Quantitative PET Techniques for Clinical Decision Support in Oncology

Quantitative image analysis has deep roots in the usage of positron emission tomography (PET) in clinical and research settings to address a wide variety of diseases. It has been extensively employed to assess molecular and physiological biomarkers in vivo in healthy and disease states, in oncology, cardiology, neurology, and psychiatry. Quantitative PET allows relating the time-varying activity concentration in tissues/organs of interest and the basic functional parameters governing the biological processes being studied. Yet, quantitative PET is challenged by a number of degrading physical factors related to the physics of PET imaging, the limitations of the instrumentation used, and the physiological status of the patient. Moreover, there is no consensus on the most reliable and robust image-derived PET metric(s) that can be used with confidence in clinical oncology owing to the discrepancies between the conclusions reported in the literature. There is also increasing interest in the use of artificial intelligence based techniques, particularly machine learning and deep learning techniques in a variety of applications to extract quantitative features (radiomics) from PET including image segmentation and outcome prediction in clinical oncology. These novel techniques are revolutionizing clinical practice and are now offering unique capabilities to the clinical molecular imaging community and biomedical researchers at large. In this report, we summarize recent developments and future tendencies in quantitative PET imaging and present example applications in clinical decision support to illustrate its potential in the context of clinical oncology.

Novel Quantitative Techniques in Hybrid (PET-MR) Imaging of Brain Tumors

Brain tumors are a collection of heterogeneous intracranial neoplasms. Molecular PET and anatomic MR imaging together can provide reliable quantitative information on tumor characterization, and help in treatment planning and monitoring therapeutic evaluation, noninvasively. Coregistration of MRI and PET images have been successfully used to improve diagnostic accuracy and in evaluating treatment response. Whole-body PET-MR scanners capable of assessing morphologic, metabolic, and functional information simultaneously are now commercially available. Early clinical studies speculate that PET-MR will be useful in several clinical specialties. In this report, we highlight the advances and applications of hybrid PET-MR in quantitative brain tumor imaging.