How accurately can quantitative imaging methods be ranked without ground truth: An upper bound on no-gold-standard evaluation.

Objective evaluation of quantitative imaging (QI) methods with patient data, while important, is typically hindered by the lack of gold standards. To address this challenge, no-gold-standard evaluation (NGSE) techniques have been proposed. These techniques have demonstrated efficacy in accurately ranking QI methods without access to gold standards.

Can patient-specific acquisition protocol improve performance on defect detection task in myocardial perfusion SPECT?

Myocardial perfusion imaging using single-photon emission computed tomography (SPECT), or myocardial perfusion SPECT (MPS) is a widely used clinical imaging modality for the diagnosis of coronary artery disease. Current clinical protocols for acquiring and reconstructing MPS images are similar for most patients. However, for patients with outlier anatomical characteristics, such as large breasts, images acquired using conventional protocols are often sub-optimal in quality, leading to degraded diagnostic accuracy.

CTLESS: A scatter-window projection and deep learning-based transmission-less attenuation compensation method for myocardial perfusion SPECT.

Attenuation compensation (AC), while being beneficial for visual-interpretation tasks in myocardial perfusion imaging (MPI) by SPECT, typically requires the availability of a separate X-ray CT component, leading to additional radiation dose, higher costs, and potentially inaccurate diagnosis due to SPECT/CT misalignment. To address these issues, we developed a method for cardiac SPECT AC using deep learning and emission scatter-window photons without a separate transmission scan (CTLESS). In this method, an estimated attenuation map reconstructed from scatter-energy window projections is segmented into different regions using a multi-channel input multi-decoder network trained on CT scans.

Theranostics in Neuroendocrine Tumors: Updates and Emerging Technologies.

Advancements in somatostatin receptor (SSTR) targeted imaging and treatment of well-differentiated neuroendocrine tumors (NETs) have revolutionized the management of these tumors. This comprehensive review delves into the current practice, discussing the use of the various FDA-approved SSTR-agonist PET tracers and the predictive imaging biomarkers, and elaborating on Lu177-DOTATATE peptide receptor radionuclide therapy (PRRT) including the evolving areas of post-therapy imaging practices, PRRT retreatment, and the potential role of dosimetry in optimizing patient treatments. The future directions sections highlight ongoing research on investigational PET imaging radiotracers, future prospects in alpha particle therapy, and combination therapy strategies.

Joint regional uptake quantification of thorium-227 and radium-223 using a multiple-energy-window projection-domain quantitative SPECT method.

Thorium-227 (Th)-based α-particle radiopharmaceutical therapies (α-RPTs) are currently being investigated in several clinical and pre-clinical studies. After administration, Th decays to Ra, another α-particle-emitting isotope, which redistributes within the patient. Reliable dose quantification of both Th and Ra is clinically important, and SPECT may perform this quantification as these isotopes also emit X- and γ-ray photons.

DEMIST: A Deep-Learning-Based Detection-Task-Specific Denoising Approach for Myocardial Perfusion SPECT.

There is an important need for methods to process myocardial perfusion imaging (MPI) single-photon emission computed tomography (SPECT) images acquired at lower-radiation dose and/or acquisition time such that the processed images improve observer performance on the clinical task of detecting perfusion defects compared to low-dose images. To address this need, we build upon concepts from model-observer theory and our understanding of the human visual system to propose a detection task-specific deep-learning-based approach for denoising MPI SPECT images (DEMIST). The approach, while performing denoising, is designed to preserve features that influence observer performance on detection tasks.

Deep learning generation of preclinical positron emission tomography (PET) images from low-count PET with task-based performance assessment.

Background: Preclinical low-count positron emission tomography (LC-PET) imaging offers numerous advantages such as facilitating imaging logistics, enabling longitudinal studies of long- and short-lived isotopes as well as increasing scanner throughput. However, LC-PET is characterized by reduced photon-count levels resulting in low signal-to-noise ratio (SNR), segmentation difficulties, and quantification uncertainties.

Purpose: We developed and evaluated a novel deep-learning (DL) architecture-Attention based Residual-Dilated Net (ARD-Net)-to generate standard-count PET (SC-PET) images from LC-PET images.

Can patient-specific acquisition protocol improve performance on defect detection task in myocardial perfusion SPECT?

Myocardial perfusion imaging using single-photon emission computed tomography (SPECT), or myocardial perfusion SPECT (MPS) is a widely used clinical imaging modality for the diagnosis of coronary artery disease. Current clinical protocols for acquiring and reconstructing MPS images are similar for most patients. However, for patients with outlier anatomical characteristics, such as large breasts, images acquired using conventional protocols are often sub-optimal in quality, leading to degraded diagnostic accuracy.

WIN-PDQ: A Wiener-estimator-based projection-domain quantitative SPECT method that accounts for intra-regional uptake heterogeneity.

SPECT can enable the quantification of activity uptake in lesions and at-risk organs in {\alpha}-particle-emitting radiopharmaceutical therapies ({\alpha}-RPTs). But this quantification is challenged by the low photon counts, complicated isotope physics, and the image-degrading effects in {\alpha}-RPT SPECT. Thus, strategies to optimize the SPECT system and protocol designs for the task of regional uptake quantification are needed.

How accurately can quantitative imaging methods be ranked without ground truth: An upper bound on no-gold-standard evaluation.

Objective evaluation of quantitative imaging (QI) methods with patient data, while important, is typically hindered by the lack of gold standards. To address this challenge, no-gold-standard evaluation (NGSE) techniques have been proposed. These techniques have demonstrated efficacy in accurately ranking QI methods without access to gold standards.

ISIT-QA: In Silico Imaging Trial to Evaluate a Low-Count Quantitative SPECT Method Across Multiple Scanner-Collimator Configurations for Ra-Based Radiopharmaceutical Therapies.

Personalized dose-based treatment planning requires accurate and reproducible noninvasive measurements to ensure safety and effectiveness. Dose estimation using SPECT is possible but challenging for alpha (α)-particle-emitting radiopharmaceutical therapy (α-RPT) because of complex γ-emission spectra, extremely low counts, and various image-degrading artifacts across a plethora of scanner-collimator configurations. Through the incorporation of physics-based considerations and skipping of the potentially lossy voxel-based reconstruction step, a recently developed projection-domain low-count quantitative SPECT (LC-QSPECT) method has the potential to provide reproducible, accurate, and precise activity concentration and dose measures across multiple scanners, as is typically the case in multicenter settings.

Need for Objective Task-Based Evaluation of Image Segmentation Algorithms for Quantitative PET: A Study with ACRIN 6668/RTOG 0235 Multicenter Clinical Trial Data.

Reliable performance of PET segmentation algorithms on clinically relevant tasks is required for their clinical translation. However, these algorithms are typically evaluated using figures of merit (FoMs) that are not explicitly designed to correlate with clinical task performance. Such FoMs include the Dice similarity coefficient (DSC), the Jaccard similarity coefficient (JSC), and the Hausdorff distance (HD).

Beyond Average: α-Particle Distribution and Dose Heterogeneity in Bone Metastatic Prostate Cancer.

α-particle emitters are emerging as a potent modality for disseminated cancer therapy because of their high linear energy transfer and localized absorbed dose profile. Despite great interest and pharmaceutical development, there is scant information on the distribution of these agents at the scale of the α-particle pathlength. We sought to determine the distribution of clinically approved [Ra]RaCl in bone metastatic castration-resistant prostate cancer at this resolution, for the first time to our knowledge, to inform activity distribution and dose at the near-cell scale.

A quality assurance framework for routine monitoring of deep learning cardiac substructure computed tomography segmentation models in radiotherapy.

Background: For autosegmentation models, the data used to train the model (e.g., public datasets and/or vendor-collected data) and the data on which the model is deployed in the clinic are typically not the same, potentially impacting the performance of these models by a process called domain shift.

Need for objective task-based evaluation of AI-based segmentation methods for quantitative PET.

Artificial intelligence (AI)-based methods are showing substantial promise in segmenting oncologic positron emission tomography (PET) images. For clinical translation of these methods, assessing their performance on clinically relevant tasks is important. However, these methods are typically evaluated using metrics that may not correlate with the task performance.

A task-specific deep-learning-based denoising approach for myocardial perfusion SPECT.

Deep-learning (DL)-based methods have shown significant promise in denoising myocardial perfusion SPECT images acquired at low dose. For clinical application of these methods, evaluation on clinical tasks is crucial. Typically, these methods are designed to minimize some fidelity-based criterion between the predicted denoised image and some reference normal-dose image.

Ethical Considerations for Artificial Intelligence in Medical Imaging: Data Collection, Development, and Evaluation.

The development of artificial intelligence (AI) within nuclear imaging involves several ethically fraught components at different stages of the machine learning pipeline, including during data collection, model training and validation, and clinical use. Drawing on the traditional principles of medical and research ethics, and highlighting the need to ensure health justice, the AI task force of the Society of Nuclear Medicine and Molecular Imaging has identified 4 major ethical risks: privacy of data subjects, data quality and model efficacy, fairness toward marginalized populations, and transparency of clinical performance. We provide preliminary recommendations to developers of AI-driven medical devices for mitigating the impact of these risks on patients and populations.

A list-mode multi-energy window low-count SPECT reconstruction method for isotopes with multiple emission peaks.

Background: Single-photon emission computed tomography (SPECT) provides a mechanism to perform absorbed-dose quantification tasks for [Formula: see text]-particle radiopharmaceutical therapies ([Formula: see text]-RPTs). However, quantitative SPECT for [Formula: see text]-RPT is challenging due to the low number of detected counts, the complex emission spectrum, and other image-degrading artifacts. Towards addressing these challenges, we propose a low-count quantitative SPECT reconstruction method for isotopes with multiple emission peaks.

DEMIST: A deep-learning-based task-specific denoising approach for myocardial perfusion SPECT.

There is an important need for methods to process myocardial perfusion imaging (MPI) SPECT images acquired at lower radiation dose and/or acquisition time such that the processed images improve observer performance on the clinical task of detecting perfusion defects. To address this need, we build upon concepts from model-observer theory and our understanding of the human visual system to propose a Detection task-specific deep-learning-based approach for denoising MPI SPECT images (DEMIST). The approach, while performing denoising, is designed to preserve features that influence observer performance on detection tasks.

Carotid atherosclerotic plaque segmentation in multi-weighted MRI using a two-stage neural network: advantages of training with high-resolution imaging and histology.

Introduction: A reliable and automated method to segment and classify carotid artery atherosclerotic plaque components is needed to efficiently analyze multi-weighted magnetic resonance (MR) images to allow their integration into patient risk assessment for ischemic stroke. Certain plaque components such as lipid-rich necrotic core (LRNC) with hemorrhage suggest a greater likelihood of plaque rupture and stroke event. Assessment for presence and extent of LRNC could assist in directing treatment with impact upon patient outcomes.

A list-mode multi-energy window low-count SPECT reconstruction method for isotopes with multiple emission peaks.

SPECT provides a mechanism to perform absorbed-dose quantification tasks for $\alpha$-particle radiopharmaceutical therapies ($\alpha$-RPTs). However, quantitative SPECT for $\alpha$-RPT is challenging due to the low number of detected counts, the complex emission spectrum, and other image-degrading artifacts. Towards addressing these challenges, we propose a low-count quantitative SPECT reconstruction method for isotopes with multiple emission peaks.

Joint regional uptake quantification of Thorium-227 and Radium-223 using a multiple-energy-window projection-domain quantitative SPECT method.

Thorium-227-based alpha-particle radiopharmaceutical therapies ({\alpha}-RPTs) are being investigated in several clinical and pre-clinical studies. After administration, Thorium-227 decays to Radium-223, another alpha-particle-emitting isotope, which redistributes within the patient. Reliable dose quantification of both Thorium-227 and Radium-223 is clinically important, and SPECT may perform this quantification as these isotopes also emit X- and gamma-ray photons.

Development and task-based evaluation of a scatter-window projection and deep learning-based transmission-less attenuation compensation method for myocardial perfusion SPECT.

Attenuation compensation (AC) is beneficial for visual interpretation tasks in single-photon emission computed tomography (SPECT) myocardial perfusion imaging (MPI). However, traditional AC methods require the availability of a transmission scan, most often a CT scan. This approach has the disadvantage of increased radiation dose, increased scanner costs, and the possibility of inaccurate diagnosis in cases of misregistration between the SPECT and CT images.

A Projection-Domain Low-Count Quantitative SPECT Method for -Particle-Emitting Radiopharmaceutical Therapy.

Single-photon emission-computed tomography (SPECT) provides a mechanism to estimate regional isotope uptake in lesions and at-risk organs after administration of -particle-emitting radiopharmaceutical therapies (-RPTs). However, this estimation task is challenging due to the complex emission spectra, the very low number of detected counts (~20 times lower than in conventional SPECT), the impact of stray-radiation-related noise at these low counts, and the multiple image-degrading processes in SPECT. The conventional reconstruction-based quantification methods are observed to be erroneous for -RPT SPECT.