Quantitative and Visual Benefits of Data-Driven Motion Correction on Oncologic PET/CT: A Prospective Cross-sectional Study.

Rationale And Objective: Conventional positron emission tomography (PET) respiratory gating utilizes a fraction of acquired PET counts (i.e., optimal gate [OG]), whereas elastic motion correction with deblurring (EMCD) utilizes all PET counts to reconstruct motion-corrected images without increasing image noise.

Addressing lung truncation in Tc-MAA SPECT/CT for Y microsphere radioembolization treatment planning.

Background: Prior studies have established that macroaggregated albumin (MAA)-SPECT/CT offers more robust lung shunt fraction (LSF) and lung mean absorbed dose (LMD) estimates in Y radioembolization in comparison to planar imaging. However, incomplete SPECT/CT coverage of the lungs is common due to clinical workflows, complicating its potential use for LSF and LMD calculations. In this work, lung truncation in MAA-SPECT/CT was addressed via correction strategies to improve Y treatment planning.

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.

Accuracy and longitudinal consistency of PET/MR attenuation correction in amyloid PET imaging amid software and hardware upgrades.

Background And Purpose: Integrated PET/MR allows the simultaneous acquisition of PET biomarkers and structural and functional MRI to study Alzheimer disease (AD). Attenuation correction (AC), crucial for PET quantification, can be performed using a deep learning approach, DL-Dixon, based on standard Dixon images. Longitudinal amyloid PET imaging, which provides important information about disease progression or treatment responses in AD, is usually acquired over several years.

Assessment of lesion insertion tool in pelvis PET/MR data with applications to attenuation correction method development.

Background: In modern positron emission tomography (PET) with multi-modality imaging (e.g., PET/CT and PET/MR), the attenuation correction (AC) is the single largest correction factor for image reconstruction.

Whole-Body Multiparametric PET in Clinical Oncology: Current Status, Challenges, and Opportunities.

The interpretation of clinical oncologic PET studies has historically used static reconstructions based on SUVs. SUVs and SUV-based images have important limitations, including dependence on uptake times and reduced conspicuity of tracer-avid lesions in organs with high background uptake. The acquisition of dynamic PET images enables additional PET reconstructions via Patlak modeling, which assumes that a tracer is irreversibly trapped by tissues of interest.

Quantitative Assessments of Tumor Activity in a General Oncologic PET/CT Population: Which Metric Minimizes Tracer Uptake Time Dependence?

In oncologic PET, the SUV and standardized uptake ratio (SUR) of a viable tumor generally increase during the postinjection period. In contrast, the net influx rate ( ), which is derived from dynamic PET data, should remain relatively constant. Uptake-time-corrected SUV (cSUV) and SUR (cSUR) have been proposed as uptake-time-independent, static alternatives to Our primary aim was to quantify the intrascan repeatability of , SUV, cSUV, SUR, and cSUR among malignant lesions on PET/CT.

FAST (fast analytical simulator of tracer)-PET: an accurate and efficient PET analytical simulation tool.

Simulation of positron emission tomography (PET) images is an essential tool in the development and validation of quantitative imaging workflows and advanced image processing pipelines. Existing Monte Carlo or analytical PET simulators often compromise on either efficiency or accuracy. We aim to develop and validate fast analytical simulator of tracer (FAST)-PET, a novel analytical framework, to simulate PET images accurately and efficiently.

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.

Patlak Slope versus Standardized Uptake Value Image Quality in an Oncologic PET/CT Population: A Prospective Cross-Sectional Study.

Patlak slope (PS) images have the potential to improve lesion conspicuity compared with standardized uptake value (SUV) images but may be more artifact-prone. This study compared PS versus SUV image quality and hepatic tumor-to-background ratios (TBRs) at matched time points. Early and late SUV and PS images were reconstructed from dynamic positron emission tomography (PET) data.

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.

Test-Retest Repeatability of Patlak Slopes versus Standardized Uptake Values for Hypermetabolic Lesions and Normal Organs in an Oncologic PET/CT Population.

Purpose: We aimed to determine the test-retest repeatability of quantitative metrics based on the Patlak slope (PS) versus the standardized uptake value (SUV) among lesions and normal organs on oncologic [F]FDG-PET/CT.

Procedures: This prospective, single-center study enrolled adults undergoing standard-of-care oncologic [F]FDG-PET/CTs. Early (35-50 min post-injection) and late (75-90 min post-injection) SUV and PS images were reconstructed from dynamic whole-body PET data.

MIRD Pamphlet No. 29: MIRDy90-A Y Research Microsphere Dosimetry Tool.

Y-microsphere radioembolization has become a well-established treatment option for liver malignancies and is one of the first U.S. Food and Drug Administration-approved unsealed radionuclide brachytherapy devices to incorporate dosimetry-based treatment planning.

Very-low-density lipoprotein triglyceride and free fatty acid plasma kinetics in women with high or low brown adipose tissue volume and overweight/obesity.

Although a high amount of brown adipose tissue (BAT) is associated with low plasma triglyceride concentration, the mechanism responsible for this relationship in people is not clear. Here, we evaluate the interrelationships among BAT, very-low-density lipoprotein triglyceride (VLDL-TG), and free fatty acid (FFA) plasma kinetics during thermoneutrality in women with overweight/obesity who had a low (<20 mL) or high (≥20 mL) volume of cold-activated BAT (assessed by using positron emission tomography in conjunction with 2-deoxy-2-[F]-fluoro-glucose). We find that plasma TG and FFA concentrations are lower and VLDL-TG and FFA plasma clearance rates are faster in women with high BAT than low BAT volume, whereas VLDL-TG and FFA appearance rates in plasma are not different between the two groups.

Chemokine Receptor 2 Is A Theranostic Biomarker for Abdominal Aortic Aneurysms.

Abdominal aortic aneurysm (AAA) is a degenerative vascular disease impacting aging populations with a high mortality upon rupture. There are no effective medical therapies to prevent AAA expansion and rupture. We previously demonstrated the role of the monocyte chemoattractant protein-1 (MCP-1) / C-C chemokine receptor type 2 (CCR2) axis in rodent AAA pathogenesis via positron emission tomography/computed tomography (PET/CT) using CCR2 targeted radiotracer Cu-DOTA-ECL1i.

Metal-Mediated, Autolytic Amide Bond Cleavage: A Strategy for the Selective, Metal Complexation-Catalyzed, Controlled Release of Metallodrugs.

Activation of metalloprodrugs or prodrug activation using transition metal catalysts represents emerging strategies for drug development; however, they are frequently hampered by poor spatiotemporal control and limited catalytic turnover. Here, we demonstrate that metal complex-mediated, autolytic release of active metallodrugs can be successfully employed to prepare clinical grade (radio-)pharmaceuticals. Optimization of the Lewis-acidic metal ion, chelate, amino acid linker, and biological targeting vector provides means to release peptide-based (radio-)metallopharmaceuticals in solution and from the solid phase using metal-mediated, autolytic amide bond cleavage (MMAAC).

Synthetic PET via Domain Translation of 3-D MRI.

Historically, patient datasets have been used to develop and validate various reconstruction algorithms for PET/MRI and PET/CT. To enable such algorithm development, without the need for acquiring hundreds of patient exams, in this article we demonstrate a deep learning technique to generate synthetic but realistic whole-body PET sinograms from abundantly available whole-body MRI. Specifically, we use a dataset of 56 F-FDG-PET/MRI exams to train a 3-D residual UNet to predict physiologic PET uptake from whole-body T1-weighted MRI.

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.

A simplified method to correct saturation of arterial input function for cardiac magnetic resonance first-pass perfusion imaging: validation with simultaneously acquired PET.

Background: First-pass perfusion imaging in magnetic resonance imaging (MRI) is an established method to measure myocardial blood flow (MBF). An obstacle for accurate quantification of MBF is the saturation of blood pool signal intensity used for arterial input function (AIF). The objective of this project was to validate a new simplified method for AIF estimation obtained from single-bolus and single sequence perfusion measurements.

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.

An automatic pipeline for PET/MRI attenuation correction validation in the brain.

Purpose: PET/MRI quantitative accuracy for neurological applications is challenging due to accuracy of the PET attenuation correction. In this work, we proposed and evaluated an automatic pipeline for assessing the quantitative accuracy of four different MRI = based attenuation correction (PET MRAC) approaches.

Methods: The proposed pipeline consists of a synthetic lesion insertion tool and the FreeSurfer neuroimaging analysis framework.