Whole-body uptake classification and prostate cancer staging in Ga-PSMA-11 PET/CT using dual-tracer learning.

Purpose: In PSMA-ligand PET/CT imaging, standardized evaluation frameworks and image-derived parameters are increasingly used to support prostate cancer staging. Clinical applicability remains challenging wherever manual measurements of numerous suspected lesions are required. Deep learning methods are promising for automated image analysis, typically requiring extensive expert-annotated image datasets to reach sufficient accuracy.

Single-site I-FP-CIT reference values from individuals with non-degenerative parkinsonism-comparison with values from healthy volunteers.

Purpose: Iodine 123-radiolabeled 2β-carbomethoxy-3β-(4-iodophenyl)-N-(3-fluoropropyl) nortropane (I-FP-CIT) SPECT can be performed to distinguish degenerative forms of movement disorders/parkinsonism/tremor from other entities such as idiopathic tremor or drug-induced parkinsonism. For equivocal cases, semi-quantification and comparison to reference values are a necessary addition to visual interpretation of I-FP-CIT scans. To overcome the challenges of multi-center recruitment and scanning of healthy volunteers, we generated I-FP-CIT reference values from individuals with various neurological conditions but without dopaminergic degeneration, scanned at a single center on the same SPECT-CT system following the same protocol, and compared them to references from a multi-center database built using healthy volunteers' data.

ROC study and SUV threshold using quantitative multi-modal SPECT for bone imaging.

Background: We investigated the clinical performance of a quantitative multi-modal SPECT/CT reconstruction platform for yielding radioactivity concentrations of bone imaging with Tc-methylene diphosphonate (MDP) or Tc-dicarboxypropane diphosphonate (DPD). The novel reconstruction incorporates CT-derived tissue information while preserving the delineation of tissue boundaries. We assessed image-based reader concordance and confidence, and determined lesion classification and SUV thresholds from ROC analysis.

Evaluation of MLACF based calculated attenuation brain PET imaging for FDG patient studies.

Calculating attenuation correction for brain PET imaging rather than using CT presents opportunities for low radiation dose applications such as pediatric imaging and serial scans to monitor disease progression. Our goal is to evaluate the iterative time-of-flight based maximum-likelihood activity and attenuation correction factors estimation (MLACF) method for clinical FDG brain PET imaging. FDG PET/CT brain studies were performed in 57 patients using the Biograph mCT (Siemens) four-ring scanner.

Classification of amyloid status using machine learning with histograms of oriented 3D gradients.

Brain amyloid burden may be quantitatively assessed from positron emission tomography imaging using standardised uptake value ratios. Using these ratios as an adjunct to visual image assessment has been shown to improve inter-reader reliability, however, the amyloid positivity threshold is dependent on the tracer and specific image regions used to calculate the uptake ratio. To address this problem, we propose a machine learning approach to amyloid status classification, which is independent of tracer and does not require a specific set of regions of interest.