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

Abstract

Single-photon emission-computed tomography (SPECT) provides a mechanism to estimate regional isotope uptake in lesions and at-risk organs after administration of α-particle-emitting radiopharmaceutical therapies (α-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 α-RPT SPECT. To address these challenges, we developed a low-count quantitative SPECT (LC-QSPECT) method that directly estimates the regional activity uptake from the projection data (obviating the reconstruction step), compensates for stray-radiation-related noise, and accounts for the radioisotope and SPECT physics, including the isotope spectra, scatter, attenuation, and collimator-detector response, using a Monte Carlo-based approach. The method was validated in the context of 3-D SPECT with 223 Ra, a commonly used radionuclide for α-RPT. Validation was performed using both realistic simulation studies, including a virtual clinical trial, and synthetic and 3-D-printed anthropomorphic physical-phantom studies. Across all studies, the LC-QSPECT method yielded reliable regional-uptake estimates and outperformed the conventional ordered subset expectation-maximization (OSEM)-based

Nuclear Medicine and Artificial Intelligence: Best Practices for Evaluation (the RELAINCE Guidelines)

“…If using prior information on the parameters to be estimated, maximum-a-posteriori (34) and posterior-mean (35) estimators could be used. In several cases, measuring quantitative features directly from projection data may yield optimal quantification (36,37) and can be considered. Joint classification/quantification task: These tasks should again be performed optimally.…”

A tissue‐fraction estimation‐based segmentation method for quantitative dopamine transporter SPECT

“…By operating directly on the projection data, the PDQ technique accurately models the statistics of noise while performing quantification. Under the assumption that the uptake within each considered region is homogeneous, 66,67 the tracer distribution $$f(\mathbf {r})$$ can be written in terms of $$\phi _k(\mathbf {r})$$ (Equation ): $$\begin{align} f(\mathbf {r}) = \sum _{k=1}^K \lambda _k \phi _k(\mathbf {r}). \end{align}$$…”

“…When $$\phi _k(\mathbf {r})$$ is known, the output of the PDQ technique has been observed to be unbiased with a variance that approached the theoretical lower limit as defined by the Cramer–Rao lower bound 67 . Thus, the PDQ technique is optimal for the task of regional uptake quantification.…”

“…66 By operating directly on the projection data, the PDQ technique accurately models the statistics of noise while performing quantification. Under the assumption that the uptake within each considered region is homogeneous, 66,67 the tracer distribution f (r) can be written in terms of 𝜙 k (r) (Equation 2):…”

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

“…1,2 For example, the use of metabolic tumor volume (MTV) obtained from 18 F-fluoro-deoxyglucose positron emission tomography (PET) to predict treatment response, 3 apparent diffusion coefficient measured using diffusion magnetic resonance imaging (MRI) to monitor cancer therapy response, 4 and radiotracer uptake in organ and tumor measured using quantitative single-photon emission computed tomography (SPECT) for dosimetry of radiopharmaceutical therapies. 5 Multiple QI methods have been developed for each of these applications. For clinical translation of these methods, it is important to objectively evaluate these QI methods on the task of reliably measuring the underlying true quantitative value.…”