EJNMMI Physics
2197-7364
2197-7364
Thụy Sĩ
Cơ quản chủ quản: SPRINGER , Springer International Publishing AG
Lĩnh vực:
Biomedical EngineeringRadiology, Nuclear Medicine and ImagingRadiationInstrumentation
Các bài báo tiêu biểu
Comparing voxel-based absorbed dosimetry methods in tumors, liver, lung, and at the liver-lung interface for 90Y microsphere selective internal radiation therapy Abstract
Background
To assess differences between four different voxel-based dosimetry methods (VBDM) for tumor, liver, and lung absorbed doses following 90 Y microsphere selective internal radiation therapy (SIRT) based on 90 Y bremsstrahlung SPECT/CT, a secondary objective was to estimate the sensitivity of liver and lung absorbed doses due to differences in organ segmentation near the liver-lung interface.
Methods
Investigated VBDM were Monte Carlo (MC), soft-tissue kernel with density correction (SKD), soft-tissue kernel (SK), and local deposition (LD). Seventeen SIRT cases were analyzed. Mean absorbed doses (
$$ \overline{AD} $$
AD
¯
) were calculated for tumor, non-tumoral liver (NL), and right lung (RL). Simulations with various SPECT spatial resolutions (FHWMs) and multiple lung shunt fractions (LSs) estimated the accuracy of VBDM at the liver-lung interface. Sensitivity of patient RL and NL
$$ \overline{AD} $$
AD
¯
on segmentation near the interface was assessed by excluding portions near the interface.
Results
SKD, SK, and LD were within 5 % of MC for tumor and NL
$$ \overline{AD} $$
AD
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. LD and SKD overestimated RL
$$ \overline{AD} $$
AD
¯
compared to MC on average by 17 and 20 %, respectively; SK underestimated RL
$$ \overline{AD} $$
AD
¯
on average by −60 %. Simulations (20 mm FWHM, 20 % LS) showed that SKD, LD, and MC were within 10 % of the truth deep (>39 mm) in the lung; SK significantly underestimated the absorbed dose deep in the lung by approximately −70 %. All VBDM were within 10 % of truth deep (>12 mm) in the liver. Excluding 1, 2, and 3 cm of RL near the interface changed the resulting RL
$$ \overline{AD} $$
AD
¯
by −22, −38, and −48 %, respectively, for all VBDM. An average change of −7 % in the NL
$$ \overline{AD} $$
AD
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was realized when excluding 3 cm of NL from the interface.
$$ \overline{AD} $$
AD
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was realized when excluding 3 cm of NL from the interface.
Conclusions
SKD, SK, and LD are equivalent to MC for tumor and NL
$$ \overline{AD} $$
AD
¯
. SK underestimates RL
$$ \overline{AD} $$
AD
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relative to MC whereas LD and SKD overestimate. RL
$$ \overline{AD} $$
AD
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is strongly influenced by the liver-lung interface.
- 2015
Assessment of population-based input functions for Patlak imaging of whole body dynamic 18F-FDG PET Abstract Background Arterial blood sampling is the gold standard method to obtain the arterial input function (AIF) for quantification of whole body (WB) dynamic18 F-FDG PET imaging. However, this procedure is invasive and not typically available in clinical environments. As an alternative, we compared AIFs to population-based input functions (PBIFs) using two normalization methods: area under the curve (AUC) and extrapolated initial plasma concentration (C P *(0)). To scale the PBIFs, we tested two methods: (1) the AUC of the image-derived input function (IDIF) and (2) the estimatedC P *(0). The aim of this study was to validate IDIF and PBIF for FDG oncological WB PET studies by comparing to the gold standard arterial blood sampling. Methods The Feng18 F-FDG plasma concentration model was applied to estimate AIF parameters (n = 23). AIF normalization used either AUC(0–60 min) orC P *(0), estimated from an exponential fit.C P *(0) is also described as the ratio of the injected dose (ID ) to initial distribution volume (iDV ).iDV was modeled using the subject height and weight, with coefficients that were estimated in 23 subjects. In 12 oncological patients, we computed IDIF (from the aorta) and PBIFs with scaling by the AUC of the IDIF from 4 time windows (15–45, 30–60, 45–75, 60–90 min) (PBIFAUC ) and estimatedC P *(0) (PBIFiDV ). The IDIF and PBIFs were compared with the gold standard AIF, using AUC values and PatlakK i values. Results The IDIF underestimated the AIF at early times and overestimated it at later times. Thus, based on the AUC andK i comparison, 30–60 min was the most accurate time window for PBIFAUC ; later time windows for scaling underestimatedK i (− 6 ± 8 to − 13 ± 9%). Correlations of AUC between AIF and IDIF, PBIFAUC(30–60) , and PBIFiDV were 0.91, 0.94, and 0.90, respectively. The bias ofK i was − 9 ± 10%, − 1 ± 8%, and 3 ± 9%, respectively. Conclusions Both PBIF scaling methods provided good mean performance with moderate variation. Improved performance can be obtained by refining IDIF methods and by evaluating PBIFs with test-retest data.
Tập 7 Số 1 - 2020
Initial experience with a SiPM-based PET/CT scanner: influence of acquisition time on image quality
Tập 5 Số 1 - 2018
Quantitative implications of the updated EARL 2019 PET–CT performance standards Abstract
Purpose
Recently, updated EARL specifications (EARL2) have been developed and announced. This study aims at investigating the impact of the EARL2 specifications on the quantitative reads of clinical PET–CT studies and testing a method to enable the use of the EARL2 standards whilst still generating quantitative reads compliant with current EARL standards (EARL1).
Methods
Thirteen non-small cell lung cancer ( NSCLC) and seventeen lymphoma PET–CT studies were used to derive four image datasets—the first dataset complying with EARL1 specifications and the second reconstructed using parameters as described in EARL2. For the third (EARL2F6) and fourth (EARL2F7) dataset in EARL2, respectively, 6 mm and 7 mm Gaussian post-filtering was applied. We compared the results of quantitative metrics (MATV, SUVmax, SUVpeak, SUVmean, TLG, and tumor-to-liver and tumor-to-blood pool ratios) obtained with these 4 datasets in 55 suspected malignant lesions using three commonly used segmentation/volume of interest (VOI) methods (MAX41, A50P, SUV4).
Results
We found that with EARL2 MAX41 VOI method, MATV decreases by 22%, TLG remains unchanged and SUV values increase by 23–30% depending on the specific metric used. The EARL2F7 dataset produced quantitative metrics best aligning with EARL1, with no significant differences between most of the datasets (p>0.05). Different VOI methods performed similarly with regard to SUV metrics but differences in MATV as well as TLG were observed. No significant difference between NSCLC and lymphoma cancer types was observed.
Conclusions
Application of EARL2 standards can result in higher SUVs, reduced MATV and slightly changed TLG values relative to EARL1. Applying a Gaussian filter to PET images reconstructed using EARL2 parameters successfully yielded EARL1 compliant data.
Tập 6 Số 1 - 2019