A multicentre and multi-national evaluation of the accuracy of quantitative Lu-177 SPECT/CT imaging performed within the MRTDosimetry project

Tran-Gia, Johannes; Denis-Bacelar, Ana M.; Ferreira, Kelley; Robinson, Andrew; Calvert, Nicholas; Fenwick, Andrew; Finocchiaro, Domenico; Fioroni, Federica; Grassi, Elisa; Heetun, Warda; Jewitt, S. J.; Kotzassarlidou, Maria; Ljungberg, Michael; McGowan, Daniel R.; Scott, Nathaniel; Scuffham, James; Gleisner, Katarina Sjögreen; Tipping, Jill; Wevrett, Jill; Laßmann, Michael

doi:10.1186/s40658-021-00397-0

Cited by 45 publications

(30 citation statements)

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“…The applications of some of the discussed phantoms have already changed our view on quantitative SPECT/CT. The kidney phantom by Tran-Gia and Lassmann has been used to evaluate quantification [ 65 ], kidney dosimetry [ 14 ] using

Lu, eventually extending it to a multicentre setting [ 79 ]. The main finding of their work was that the typical volume-based approach for partial volume correction based on spherical inserts like in the IEC NEMA Body Phantom was insufficient for accurate quantification.…”

Section: Discussionmentioning

confidence: 99%

Absolute Quantification in Diagnostic SPECT/CT: The Phantom Premise

et al. 2021

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The application of absolute quantification in SPECT/CT has seen increased interest in the context of radionuclide therapies where patient-specific dosimetry is a requirement within the European Union (EU) legislation. However, the translation of this technique to diagnostic nuclear medicine outside this setting is rather slow. Clinical research has, in some examples, already shown an association between imaging metrics and clinical diagnosis, but the applications, in general, lack proper validation because of the absence of a ground truth measurement. Meanwhile, additive manufacturing or 3D printing has seen rapid improvements, increasing its uptake in medical imaging. Three-dimensional printed phantoms have already made a significant impact on quantitative imaging, a trend that is likely to increase in the future. In this review, we summarize the data of recent literature to underpin our premise that the validation of diagnostic applications in nuclear medicine using application-specific phantoms is within reach given the current state-of-the-art in additive manufacturing or 3D printing.

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Section: Discussionmentioning

confidence: 99%

Absolute Quantification in Diagnostic SPECT/CT: The Phantom Premise

et al. 2021

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“…All SPECT/CT reconstruction in this work was performed based on 120 projections using OS-EM with 6 iterations and 8 subsets, employing compensation for attenuation and scatter using the ESSE method [19]. To convert the reconstructed counts into activity concentration, an image calibration factor (unit: counts-per-second-per-Megabecquerel) was determined as described in [20] based on the physical SPECT/CT measurement of the Jaszczak phantom described above. For these reconstructions, additional metrics were used to quantify the image quality.…”

Section: Physical Phantom-based Verification Of the Simulation-based ...mentioning

confidence: 99%

Analysis of a deep learning-based method for generation of SPECT projections based on a large Monte Carlo simulated dataset

et al. 2022

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Background In recent years, a lot of effort has been put in the enhancement of medical imaging using artificial intelligence. However, limited patient data in combination with the unavailability of a ground truth often pose a challenge to a systematic validation of such methodologies. The goal of this work was to investigate a recently proposed method for an artificial intelligence-based generation of synthetic SPECT projections, for acceleration of the image acquisition process based on a large dataset of realistic SPECT simulations. Methods A database of 10,000 SPECT projection datasets of heterogeneous activity distributions of randomly placed random shapes was simulated for a clinical SPECT/CT system using the SIMIND Monte Carlo program. Synthetic projections at fixed angular increments from a set of input projections at evenly distributed angles were generated by different u-shaped convolutional neural networks (u-nets). These u-nets differed in noise realization used for the training data, number of input projections, projection angle increment, and number of training/validation datasets. Synthetic projections were generated for 500 test projection datasets for each u-net, and a quantitative analysis was performed using statistical hypothesis tests based on structural similarity index measure and normalized root-mean-squared error. Additional simulations with varying detector orbits were performed on a subset of the dataset to study the effect of the detector orbit on the performance of the methodology. For verification of the results, the u-nets were applied to Jaszczak and NEMA physical phantom data obtained on a clinical SPECT/CT system. Results No statistically significant differences were observed between u-nets trained with different noise realizations. In contrast, a statistically significant deterioration was found for training with a small subset (400 datasets) of the 10,000 simulated projection datasets in comparison with using a large subset (9500 datasets) for training. A good agreement between synthetic (i.e., u-net generated) and simulated projections before adding noise demonstrates a denoising effect. Finally, the physical phantom measurements show that our findings also apply for projections measured on a clinical SPECT/CT system. Conclusion Our study shows the large potential of u-nets for accelerating SPECT/CT imaging. In addition, our analysis numerically reveals a denoising effect when generating synthetic projections with a u-net. Clinically interesting, the methodology has proven robust against camera orbit deviations in a clinically realistic range. Lastly, we found that a small number of training samples (e.g., ~ 400 datasets) may not be sufficient for reliable generalization of the u-net.

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“…Ideally, pixel-level deadtime correction should be performed in projection space prior to reconstruction (Cohalan et al , 2020; Koral et al , 1998), but if this is not feasible with commercial software, dead time correction factors can be applied post reconstruction. With recent interest in high count rate gamma camera imaging for therapy applications (Stella et al , 2021), real-time deadtime correction algorithms for SPECT systems have become available (Tran-Gia et al , 2021).…”

Section: Quantification Of Activity In Source Regions For Dosimetrymentioning

confidence: 99%

ICRU REPORT 96, Dosimetry-Guided Radiopharmaceutical Therapy

et al. 2021

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The Commission wishes to express its appreciation to the individuals involved in the preparation of this Report for the time and efforts that they devoted to this task and to express its appreciation to the organizations with which they are affiliated.All rights reserved. No part of this book may be reproduced, stored in retrieval systems or transmitted in any form by any means, electronic, electrostatic, magnetic, mechanical photocopying, recording or otherwise, without the permission in writing from the publishers.

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A multicentre and multi-national evaluation of the accuracy of quantitative Lu-177 SPECT/CT imaging performed within the MRTDosimetry project

Cited by 45 publications

References 30 publications

Absolute Quantification in Diagnostic SPECT/CT: The Phantom Premise

Absolute Quantification in Diagnostic SPECT/CT: The Phantom Premise

Analysis of a deep learning-based method for generation of SPECT projections based on a large Monte Carlo simulated dataset

ICRU REPORT 96, Dosimetry-Guided Radiopharmaceutical Therapy

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