The promise of artificial intelligence and deep learning in PET and SPECT imaging.

Abstract The purpose of the EANM Dosimetry Committee is to provide recommendations and guidance to scientists and clinicians on patient-specific dosimetry. Radiopharmaceuticals labelled with lutetium-177 (177Lu) are increasingly used for therapeutic applications, in particular for the treatment of metastatic neuroendocrine tumours using ligands for somatostatin receptors and prostate adenocarcinoma with small-molecule PSMA-targeting ligands. This paper provides an overview of reported dosimetry data for these therapies and summarises current knowledge about radiation-induced side effects on normal tissues and dose-effect relationships for tumours. Dosimetry methods and data are summarised for kidneys, bone marrow, salivary glands, lacrimal glands, pituitary glands, tumours, and the skin in case of radiopharmaceutical extravasation. Where applicable, taking into account the present status of the field and recent evidence in the literature, guidance is provided. The purpose of these recommendations is to encourage the practice of patient-specific dosimetry in therapy with 177Lu-labelled compounds. The proposed methods should be within the scope of centres offering therapy with 177Lu-labelled ligands for somatostatin receptors or small-molecule PSMA. 

/pdf/eanm-dosimetry-committee-recommendations-for-dosimetry-of-35t2f5yy.pdf

EANM dosimetry committee recommendations for dosimetry of 177Lu-labelled somatostatin-receptor- and PSMA-targeting ligands

Visual Abstract Patient-specific dosimetry in radiopharmaceutical therapy (RPT) is impeded by the lack of tools that are accurate and practical for the clinic. Our aims were to construct and test an integrated voxel-level pipeline that automates key components (organ segmentation, registration, dose-rate estimation, and curve fitting) of the RPT dosimetry process and then to use it to report patient-specific dosimetry in 177Lu-DOTATATE therapy. Methods: An integrated workflow that automates the entire dosimetry process, except tumor segmentation, was constructed. First, convolutional neural networks (CNNs) are used to automatically segment organs on the CT portion of one post-therapy SPECT/CT scan. Second, local contour intensity–based SPECT--SPECT alignment results in volume-of-interest propagation to other time points. Third, dose rate is estimated by explicit Monte Carlo (MC) radiation transport using the fast, Dose Planning Method code. Fourth, the optimal function for dose-rate fitting is automatically selected for each voxel. When reporting mean dose, we apply partial-volume correction, and uncertainty is estimated by an empiric approach of perturbing segmentations. Results: The workflow was used with 4-time-point 177Lu SPECT/CT imaging data from 20 patients with 77 neuroendocrine tumors, segmented by a radiologist. CNN-defined kidneys resulted in high Dice values (0.91–0.94) and only small differences (2%–5%) in mean dose when compared with manual segmentation. Contour intensity–based registration led to visually enhanced alignment, and the voxel-level fitting had high R2 values. Across patients, dosimetry results were highly variable; for example, the average of the mean absorbed dose (Gy/GBq) was 3.2 (range, 0.2–10.4) for lesions, 0.49 (range, 0.24–1.02) for left kidney, 0.54 (range, 0.31–1.07) for right kidney, and 0.51 (range, 0.27–1.04) for healthy liver. Patient results further demonstrated the high variability in the number of cycles needed to deliver hypothetical threshold absorbed doses of 23 Gy to kidney and 100 Gy to tumor. The uncertainty in mean dose, attributable to variability in segmentation, averaged 6% (range, 3%–17%) for organs and 10% (range, 3%–37%) for lesions. For a typical patient, the time for the entire process was about 25 min (∼2 min manual time) on a desktop computer, including time for CNN organ segmentation, coregistration, MC dosimetry, and voxel curve fitting. Conclusion: A pipeline integrating novel tools that are fast and automated provides the capacity for clinical translation of dosimetry-guided RPT.

/pdf/a-pipeline-for-automated-voxel-dosimetry-application-in-3ajfobol.pdf

A Pipeline for Automated Voxel Dosimetry: Application in Patients with Multi-SPECT/CT Imaging After 177Lu-Peptide Receptor Radionuclide Therapy

Abstract In prostate cancer (PCa), the use of new radiopharmaceuticals has improved the accuracy of diagnosis and staging, refined surveillance strategies, and introduced specific and personalized radioreceptor therapies. Nuclear medicine, therefore, holds great promise for improving the quality of life of PCa patients, through managing and processing a vast amount of molecular imaging data and beyond, using a multi-omics approach and improving patients’ risk-stratification for tailored medicine. Artificial intelligence (AI) and radiomics may allow clinicians to improve the overall efficiency and accuracy of using these “big data” in both the diagnostic and theragnostic field: from technical aspects (such as semi-automatization of tumor segmentation, image reconstruction, and interpretation) to clinical outcomes, improving a deeper understanding of the molecular environment of PCa, refining personalized treatment strategies, and increasing the ability to predict the outcome. This systematic review aims to describe the current literature on AI and radiomics applied to molecular imaging of prostate cancer. 

/pdf/radiomics-and-artificial-intelligence-in-prostate-cancer-new-2y58mkm5.pdf

Radiomics and artificial intelligence in prostate cancer: new tools for molecular hybrid imaging and theragnostics

We highlight emerging uses of artificial intelligence (AI) in the field of theranostics, focusing on its significant potential to enable routine and reliable personalization of radiopharmaceutical therapies (RPTs). Personalized RPTs require patient-specific dosimetry calculations accompanying therapy. Additionally we discuss the potential to exploit biological information from diagnostic and therapeutic molecular images to derive biomarkers for absorbed dose and outcome prediction; toward personalization of therapies. We try to motivate the nuclear medicine community to expand and align efforts into making routine and reliable personalization of RPTs a reality.

Role of Artificial Intelligence in Theranostics:: Toward Routine Personalized Radiopharmaceutical Therapies.

The aims were to decrease 177Lu-SPECT (single-photon emission computed tomography) acquisition time by reducing the number of projections and to circumvent image degradation by adding deep learning–generated synthesized projections.

Methods: We constructed a deep convolutional U-structured network for generating synthetic intermediate projections (CUSIP). The number of SPECT investigations was 352 for training, 37 for validation, and 15 for testing. The input was every fourth projection of 120 acquired SPECT projections, i.e., 30 projections. The output was 30 synthetic intermediate projections (SIPs) per CUSIP. SPECT images were reconstructed with 120 or 30 projections, or 120 projections where 90 SIPs were generated from the 30 projections (30-120SIP); using 3 CUSIPs. The reconstructions were performed with two ordered subset expectation maximization (OSEM) algorithms: attenuation-corrected (AC)-OSEM, and attenuation, scatter, and collimator response–corrected (ASCC)-OSEM. Image quality of SIPs and SPECT images were quantitatively evaluated with root mean square error, peak signal-to-noise-ratio (PSNR), and structural similarity (SSIM) index metrics. From a Jaszczak SPECT Phantom, the recovery and signal-to-noise ratio (SNR) were determined. In addition, an experienced observer qualitatively assessed the SPECT image quality of the test set. Kidney activity concentrations, as determined from the different SPECT images, were compared.

Results: The generated SIPs had a mean SSIM value of 0.926 (0.061). For AC-OSEM, the reconstruction with 30-120SIP had higher SSIM (0.993 vs. 0.989; p<0.001) and PSNR (49.5 vs. 47.2; p<0.001) values than the reconstruction with 30 projections. ASCC-OSEM had higher SSIM and PSNR values than AC-OSEM (p<0.001). There was a minor loss in recovery for the 30-120SIP set, but SNR was clearly improved compared to the 30-projection set. The observer assessed 27/30 of the images reconstructed with 30 projections as having unacceptable noise levels, whereas corresponding values were 2/60 for the 30-120SIP and 120 projection sets. Image quality did not differ significantly between the 30-120SIP and 120 projection reconstructions. The kidney activity concentration was similar between the different projection sets, excepting a minor reduction of 2.5% for the ASCC-OSEM 30-120SIP.

Conclusion: Adopting synthetic intermediate projections for sparsely acquired projections considerably recovers image quality and could allow reduced SPECT acquisition time in clinical dosimetry protocols.

https://jnm.snmjournals.org/content/jnumed/early/2020/08/27/jnumed.120.245548.full.pdf

Ida Marin

Papers

Deep-Learning Generation of Synthetic Intermediate Projections Improves 177Lu SPECT Images Reconstructed with Sparsely Acquired Projections.