Pub Date : 2024-01-08DOI: 10.1186/s40658-023-00606-y
Yan Huang, Han Zhang, Xueping Hu, Shanshan Qin, Fan Hu, Yuchen Li, Haidong Cai, Kuangyu Shi, Fei Yu
Background: Due to spatial resolution limitations, conventional NaI-SPECT typically overestimates the left ventricular (LV) ejection fraction (EF) in patients with small LV volumes. The purpose of this study was to explore the clinical application value of the small heart (SH) reconstruction protocol embedded in the postprocessing procedure of D-SPECT.
Methods: We retrospectively analyzed patients who undergo both D-SPECT and echocardiography (Echo) within one week. Patients with small LV volume were defined as those with a rest end-systolic volume (rESV) ≤ 25 mL and underwent reconstruction using the standard (SD) reconstruction protocol. The SH protocol was deemed successful in correcting the LVEF value if it decreased by 5% or more compared to the SD protocol. The ROC curve was used to calculate the optimal cutoff value of the SH protocol. LVEF, ESV and EDV were computed with SD and SH, respectively. Echo was performed as a reference, and Echo-LVEF, ESV, and EDV were calculated using the Teichholz formula. One-way ANOVA was used to compare these parameters among the three groups.
Results: The final study included 209 patients (73.21% female, age 67.34 ± 7.85 years). Compared with the SD protocol, the SH protocol significantly decreased LVEF (67.43 ± 7.38% vs. 71.30 ± 7.61%, p < 0.001). The optimal cutoff value for using the SH protocol was rESV > 17 mL (AUC = 0.651, sensitivity = 78.43%, specificity = 45.57%, p = 0.001). In the subgroup of rESV > 17 mL, there was no significant difference in LVEF (61.84 ± 4.67% vs. 62.83 ± 2.85%, p = 0.481) between the SH protocol and Echo, and no significant difference was observed in rESV (26.92 ± 3.25 mL vs. 27.94 ± 7.96 mL, p = 0.60) between the SH protocol and Echo.
Conclusion: This pilot study demonstrated that the SH reconstruction protocol was able to effectively correct the overestimation of LVEF in patients with small LV volumes. Particularly, in the rESV > 17 mL subgroup, the time and computing power waste could be reduced while still ensuring the accuracy of the LVEF value and image quality.
{"title":"The D-SPECT SH reconstruction protocol: improved quantification of small left ventricle volumes.","authors":"Yan Huang, Han Zhang, Xueping Hu, Shanshan Qin, Fan Hu, Yuchen Li, Haidong Cai, Kuangyu Shi, Fei Yu","doi":"10.1186/s40658-023-00606-y","DOIUrl":"10.1186/s40658-023-00606-y","url":null,"abstract":"<p><strong>Background: </strong>Due to spatial resolution limitations, conventional NaI-SPECT typically overestimates the left ventricular (LV) ejection fraction (EF) in patients with small LV volumes. The purpose of this study was to explore the clinical application value of the small heart (SH) reconstruction protocol embedded in the postprocessing procedure of D-SPECT.</p><p><strong>Methods: </strong>We retrospectively analyzed patients who undergo both D-SPECT and echocardiography (Echo) within one week. Patients with small LV volume were defined as those with a rest end-systolic volume (rESV) ≤ 25 mL and underwent reconstruction using the standard (SD) reconstruction protocol. The SH protocol was deemed successful in correcting the LVEF value if it decreased by 5% or more compared to the SD protocol. The ROC curve was used to calculate the optimal cutoff value of the SH protocol. LVEF, ESV and EDV were computed with SD and SH, respectively. Echo was performed as a reference, and Echo-LVEF, ESV, and EDV were calculated using the Teichholz formula. One-way ANOVA was used to compare these parameters among the three groups.</p><p><strong>Results: </strong>The final study included 209 patients (73.21% female, age 67.34 ± 7.85 years). Compared with the SD protocol, the SH protocol significantly decreased LVEF (67.43 ± 7.38% vs. 71.30 ± 7.61%, p < 0.001). The optimal cutoff value for using the SH protocol was rESV > 17 mL (AUC = 0.651, sensitivity = 78.43%, specificity = 45.57%, p = 0.001). In the subgroup of rESV > 17 mL, there was no significant difference in LVEF (61.84 ± 4.67% vs. 62.83 ± 2.85%, p = 0.481) between the SH protocol and Echo, and no significant difference was observed in rESV (26.92 ± 3.25 mL vs. 27.94 ± 7.96 mL, p = 0.60) between the SH protocol and Echo.</p><p><strong>Conclusion: </strong>This pilot study demonstrated that the SH reconstruction protocol was able to effectively correct the overestimation of LVEF in patients with small LV volumes. Particularly, in the rESV > 17 mL subgroup, the time and computing power waste could be reduced while still ensuring the accuracy of the LVEF value and image quality.</p>","PeriodicalId":11559,"journal":{"name":"EJNMMI Physics","volume":"11 1","pages":"5"},"PeriodicalIF":4.0,"publicationDate":"2024-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10774323/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139377370","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-08DOI: 10.1186/s40658-023-00609-9
Jonas Högberg, Christoffer Andersén, Tobias Rydén, Jakob H Lagerlöf
Background: The Otsu method and the Chan-Vese model are two methods proven to perform well in determining volumes of different organs and specific tissue fractions. This study aimed to compare the performance of the two methods regarding segmentation of active thyroid gland volumes, reflecting different clinical settings by varying the parameters: gland size, gland activity concentration, background activity concentration and gland activity concentration heterogeneity.
Methods: A computed tomography was performed on three playdough thyroid phantoms with volumes 20, 35 and 50 ml. The image data were separated into playdough and water based on Hounsfield values. Sixty single photon emission computed tomography (SPECT) projections were simulated by Monte Carlo method with isotope Technetium-99 m ([Formula: see text]Tc). Linear combinations of SPECT images were made, generating 12 different combinations of volume and background: each with both homogeneous thyroid activity concentration and three hotspots of different relative activity concentrations (48 SPECT images in total). The relative background levels chosen were 5 %, 10 %, 15 % and 20 % of the phantom activity concentration and the hotspot activities were 100 % (homogeneous case) 150 %, 200 % and 250 %. Poisson noise, (coefficient of variation of 0.8 at a 20 % background level, scattering excluded), was added before reconstruction was done with the Monte Carlo-based SPECT reconstruction algorithm Sahlgrenska Academy reconstruction code (SARec). Two different segmentation algorithms were applied: Otsu's threshold selection method and an adaptation of the Chan-Vese model for active contours without edges; the results were evaluated concerning relative volume, mean absolute error and standard deviation per thyroid volume, as well as dice similarity coefficient.
Results: Both methods segment the images well and deviate similarly from the true volumes. They seem to slightly overestimate small volumes and underestimate large ones. Different background levels affect the two methods similarly as well. However, the Chan-Vese model deviates less and paired t-testing showed significant difference between distributions of dice similarity coefficients (p-value [Formula: see text]).
Conclusions: The investigations indicate that the Chan-Vese model performs better and is slightly more robust, while being more challenging to implement and use clinically. There is a trade-off between performance and user-friendliness.
{"title":"Comparison of Otsu and an adapted Chan-Vese method to determine thyroid active volume using Monte Carlo generated SPECT images.","authors":"Jonas Högberg, Christoffer Andersén, Tobias Rydén, Jakob H Lagerlöf","doi":"10.1186/s40658-023-00609-9","DOIUrl":"10.1186/s40658-023-00609-9","url":null,"abstract":"<p><strong>Background: </strong>The Otsu method and the Chan-Vese model are two methods proven to perform well in determining volumes of different organs and specific tissue fractions. This study aimed to compare the performance of the two methods regarding segmentation of active thyroid gland volumes, reflecting different clinical settings by varying the parameters: gland size, gland activity concentration, background activity concentration and gland activity concentration heterogeneity.</p><p><strong>Methods: </strong>A computed tomography was performed on three playdough thyroid phantoms with volumes 20, 35 and 50 ml. The image data were separated into playdough and water based on Hounsfield values. Sixty single photon emission computed tomography (SPECT) projections were simulated by Monte Carlo method with isotope Technetium-99 m ([Formula: see text]Tc). Linear combinations of SPECT images were made, generating 12 different combinations of volume and background: each with both homogeneous thyroid activity concentration and three hotspots of different relative activity concentrations (48 SPECT images in total). The relative background levels chosen were 5 %, 10 %, 15 % and 20 % of the phantom activity concentration and the hotspot activities were 100 % (homogeneous case) 150 %, 200 % and 250 %. Poisson noise, (coefficient of variation of 0.8 at a 20 % background level, scattering excluded), was added before reconstruction was done with the Monte Carlo-based SPECT reconstruction algorithm Sahlgrenska Academy reconstruction code (SARec). Two different segmentation algorithms were applied: Otsu's threshold selection method and an adaptation of the Chan-Vese model for active contours without edges; the results were evaluated concerning relative volume, mean absolute error and standard deviation per thyroid volume, as well as dice similarity coefficient.</p><p><strong>Results: </strong>Both methods segment the images well and deviate similarly from the true volumes. They seem to slightly overestimate small volumes and underestimate large ones. Different background levels affect the two methods similarly as well. However, the Chan-Vese model deviates less and paired t-testing showed significant difference between distributions of dice similarity coefficients (p-value [Formula: see text]).</p><p><strong>Conclusions: </strong>The investigations indicate that the Chan-Vese model performs better and is slightly more robust, while being more challenging to implement and use clinically. There is a trade-off between performance and user-friendliness.</p>","PeriodicalId":11559,"journal":{"name":"EJNMMI Physics","volume":"11 1","pages":"6"},"PeriodicalIF":4.0,"publicationDate":"2024-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10774246/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139377369","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-05DOI: 10.1186/s40658-023-00608-w
Chengpeng Gong, Yajing Zhang, Fei Feng, Mengmeng Hu, Kun Li, Rundong Pi, Hua Shu, Rongmei Tang, Xiaoli Wang, Shilin Tan, Fan Hu, Jia Hu
<p><strong>Purpose: </strong>To investigate the optimal threshold for measuring thyroid volume in patients with Grave's hyperthyroidism (GH) by SPECT/CT.</p><p><strong>Materials and methods: </strong>A 53 mL butterfly-shaped hollow container made of two 45-degree transparent elbows was put into a NEMA IEC phantom tank. The butterfly-shaped container and the tank were then filled with Na<sup>99m</sup>TcO4 of different radioactive concentrations, respectively, which could simulate thyroid gland with GH by different target-to-background ratios (T/B) (200:1, 600:1, 1000:1). The different T/B of planar imaging and SPECT/CT were acquired by a Discovery NM/CT 670 Pro SPECT/CT. With Thyroid software (Version 4.0) of GE-Xeleris workstation, the region of the thyroid gland in planar imaging was delineated. The thyroid area and average long diameter of both lobes were substituted into the Allen formula to calculate the thyroid volume. The calculation error was compared with the actual volume. Q-Metrix software was used to perform CT-based attenuation correction, scatter correction, resolution recovery. Ordered-subsets expectation maximization was used to reconstruct SPECT data. 20%, 25%, 30%, 40%, 50%, 60% thresholds were selected to automatically delineate the volume of interest and compared with the real volume, which determinated the optimal threshold. We measured the thyroid volume of 40 GH patients using the threshold and compared the volumes obtained by planar imaging and ultrasound three-dimensional. The differences of the volumes with different T/B and thresholds were compared by the ANOVA and least significant difference t test. The volumes delineated by SPECT/CT were evaluated using ANOVA, least significant difference t test, correlation analysis and, linear regression and Bland-Altman concordance test plot. The differences and consistency of thyroid volume were compared among the above three methods.</p><p><strong>Results: </strong>There was no significant difference in the results between different T/B models (P > 0.05). The thyroid volume calculated by the planar imaging formula method was higher than the real volume, with an average overestimation of 22.81%. The volumes delineated by SPECT/CT threshold automatically decreased while the threshold increased. There were significant differences between groups with different thresholds (P < 0.001). With an average error of 3.73%, the thyroid volume analyzed by the threshold of 25% was close to the results of ultrasound measurement (P > 0.05). Thyroid volume measured by planar imaging method was significantly higher than ultrasound and SPECT/CT threshold automatic delineation method (P < 0.05). The agreement between the SPECT/CT 25% threshold and ultrasound (r = 0.956, b = 0.961) was better than that between the planar imaging and ultrasound (r = 0.590, b = 0.574). The Bland-Altman plot also showed that the thyroid volume measured by the 25% threshold automatic delineation method was in good agreeme
目的：研究通过 SPECT/CT 测量格拉夫氏甲状腺功能亢进症（GH）患者甲状腺容积的最佳阈值：将一个由两个 45 度透明弯头组成的 53 mL 蝶形空心容器放入一个 NEMA IEC 幻影罐中。然后在蝶形容器和水箱中分别注入不同放射性浓度的 Na99mTcO4，通过不同的靶-本底比（T/B）（200:1、600:1、1000:1）模拟甲状腺与 GH。不同T/B的平面成像和SPECT/CT均由Discovery NM/CT 670 Pro SPECT/CT采集。利用GE-Xeleris工作站的甲状腺软件（4.0版），在平面成像中划定甲状腺区域。将甲状腺面积和两叶平均长径代入艾伦公式计算甲状腺体积。计算误差与实际体积进行比较。Q-Metrix软件用于进行基于CT的衰减校正、散射校正和分辨率恢复。有序子集期望最大化用于重建SPECT数据。选择20%、25%、30%、40%、50%、60%的阈值来自动划定感兴趣的体积，并与实际体积进行比较，从而确定最佳阈值。我们使用阈值测量了 40 名 GH 患者的甲状腺体积，并比较了平面成像和超声三维成像获得的体积。通过方差分析和最小显著性差异t检验比较了不同T/B和阈值下体积的差异。采用方差分析、最小显著性差异 t 检验、相关性分析、线性回归和 Bland-Altman 一致性检验图评估 SPECT/CT 划分的体积。比较了上述三种方法在甲状腺体积上的差异和一致性：不同T/B模型之间的结果无明显差异（P>0.05）。平面成像公式法计算的甲状腺体积高于实际体积，平均高估22.81%。当阈值升高时，SPECT/CT阈值划定的体积自动减小。不同阈值组间差异明显（P 0.05）。平面成像法测得的甲状腺体积明显高于超声和SPECT/CT阈值自动划定法（P 结论：平面成像法测得的甲状腺体积明显高于超声和SPECT/CT阈值自动划定法：T/B对GH患者甲状腺容积的测量没有影响；平面成像法会明显高估GH患者的甲状腺容积，而25%阈值自动划线法可以获得更准确的GH患者甲状腺容积。
{"title":"The determination of the optimal threshold on measurement of thyroid volume using quantitative SPECT/CT for Graves' hyperthyroidism.","authors":"Chengpeng Gong, Yajing Zhang, Fei Feng, Mengmeng Hu, Kun Li, Rundong Pi, Hua Shu, Rongmei Tang, Xiaoli Wang, Shilin Tan, Fan Hu, Jia Hu","doi":"10.1186/s40658-023-00608-w","DOIUrl":"10.1186/s40658-023-00608-w","url":null,"abstract":"<p><strong>Purpose: </strong>To investigate the optimal threshold for measuring thyroid volume in patients with Grave's hyperthyroidism (GH) by SPECT/CT.</p><p><strong>Materials and methods: </strong>A 53 mL butterfly-shaped hollow container made of two 45-degree transparent elbows was put into a NEMA IEC phantom tank. The butterfly-shaped container and the tank were then filled with Na<sup>99m</sup>TcO4 of different radioactive concentrations, respectively, which could simulate thyroid gland with GH by different target-to-background ratios (T/B) (200:1, 600:1, 1000:1). The different T/B of planar imaging and SPECT/CT were acquired by a Discovery NM/CT 670 Pro SPECT/CT. With Thyroid software (Version 4.0) of GE-Xeleris workstation, the region of the thyroid gland in planar imaging was delineated. The thyroid area and average long diameter of both lobes were substituted into the Allen formula to calculate the thyroid volume. The calculation error was compared with the actual volume. Q-Metrix software was used to perform CT-based attenuation correction, scatter correction, resolution recovery. Ordered-subsets expectation maximization was used to reconstruct SPECT data. 20%, 25%, 30%, 40%, 50%, 60% thresholds were selected to automatically delineate the volume of interest and compared with the real volume, which determinated the optimal threshold. We measured the thyroid volume of 40 GH patients using the threshold and compared the volumes obtained by planar imaging and ultrasound three-dimensional. The differences of the volumes with different T/B and thresholds were compared by the ANOVA and least significant difference t test. The volumes delineated by SPECT/CT were evaluated using ANOVA, least significant difference t test, correlation analysis and, linear regression and Bland-Altman concordance test plot. The differences and consistency of thyroid volume were compared among the above three methods.</p><p><strong>Results: </strong>There was no significant difference in the results between different T/B models (P > 0.05). The thyroid volume calculated by the planar imaging formula method was higher than the real volume, with an average overestimation of 22.81%. The volumes delineated by SPECT/CT threshold automatically decreased while the threshold increased. There were significant differences between groups with different thresholds (P < 0.001). With an average error of 3.73%, the thyroid volume analyzed by the threshold of 25% was close to the results of ultrasound measurement (P > 0.05). Thyroid volume measured by planar imaging method was significantly higher than ultrasound and SPECT/CT threshold automatic delineation method (P < 0.05). The agreement between the SPECT/CT 25% threshold and ultrasound (r = 0.956, b = 0.961) was better than that between the planar imaging and ultrasound (r = 0.590, b = 0.574). The Bland-Altman plot also showed that the thyroid volume measured by the 25% threshold automatic delineation method was in good agreeme","PeriodicalId":11559,"journal":{"name":"EJNMMI Physics","volume":"11 1","pages":"4"},"PeriodicalIF":4.0,"publicationDate":"2024-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10766934/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139097594","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-03DOI: 10.1186/s40658-023-00602-2
Daniel Roth, Erik Larsson, Joanna Strand, Michael Ljungberg, Katarina Sjögreen Gleisner
In image processing for activity quantification, the end goal is to produce a metric that is independent of the measurement geometry. Photon attenuation needs to be accounted for and can be accomplished utilizing spectral information, avoiding the need of additional image acquisitions. The aim of this work is to investigate the feasibility of 177Lu activity quantification with a small CZT-based hand-held gamma-camera, using such an attenuation correction method. A previously presented dual photopeak method, based on the differential attenuation for two photon energies, is adapted for the three photopeaks at 55 keV, 113 keV, and 208 keV for 177Lu. The measurement model describes the count rates in each energy window as a function of source depth and activity, accounting for distance-dependent system sensitivity, attenuation, and build-up. Parameter values are estimated from characterizing measurements, and the source depth and activity are obtained by minimizing the difference between measured and modelled count rates. The method is applied and evaluated in phantom measurements, in a clinical setting for superficial lesions in two patients, and in a pre-clinical setting for one human tumour xenograft. Evaluation is made for a LEHR and an MEGP collimator. For phantom measurements at clinically relevant depths, the average (and standard deviation) in activity errors are 17% ± 9.6% (LEHR) and 2.9% ± 3.6% (MEGP). For patient measurements, deviations from activity estimates from planar images from a full-sized gamma-camera are 0% ± 21% (LEHR) and 16% ± 18% (MEGP). For mouse measurements, average deviations of − 16% (LEHR) and − 6% (MEGP) are obtained when compared to a small-animal SPECT/CT system. The MEGP collimator appears to be better suited for activity quantification, yielding a smaller variability in activity estimates, whereas the LEHR results are more severely affected by septal penetration. Activity quantification for 177Lu using the hand-held camera is found to be feasible. The readily available nature of the hand-held camera may enable more frequent activity quantification in e.g., superficial structures in patients or in the pre-clinical setting.
{"title":"Feasibility of 177Lu activity quantification using a small portable CZT-based gamma-camera","authors":"Daniel Roth, Erik Larsson, Joanna Strand, Michael Ljungberg, Katarina Sjögreen Gleisner","doi":"10.1186/s40658-023-00602-2","DOIUrl":"https://doi.org/10.1186/s40658-023-00602-2","url":null,"abstract":"In image processing for activity quantification, the end goal is to produce a metric that is independent of the measurement geometry. Photon attenuation needs to be accounted for and can be accomplished utilizing spectral information, avoiding the need of additional image acquisitions. The aim of this work is to investigate the feasibility of 177Lu activity quantification with a small CZT-based hand-held gamma-camera, using such an attenuation correction method. A previously presented dual photopeak method, based on the differential attenuation for two photon energies, is adapted for the three photopeaks at 55 keV, 113 keV, and 208 keV for 177Lu. The measurement model describes the count rates in each energy window as a function of source depth and activity, accounting for distance-dependent system sensitivity, attenuation, and build-up. Parameter values are estimated from characterizing measurements, and the source depth and activity are obtained by minimizing the difference between measured and modelled count rates. The method is applied and evaluated in phantom measurements, in a clinical setting for superficial lesions in two patients, and in a pre-clinical setting for one human tumour xenograft. Evaluation is made for a LEHR and an MEGP collimator. For phantom measurements at clinically relevant depths, the average (and standard deviation) in activity errors are 17% ± 9.6% (LEHR) and 2.9% ± 3.6% (MEGP). For patient measurements, deviations from activity estimates from planar images from a full-sized gamma-camera are 0% ± 21% (LEHR) and 16% ± 18% (MEGP). For mouse measurements, average deviations of − 16% (LEHR) and − 6% (MEGP) are obtained when compared to a small-animal SPECT/CT system. The MEGP collimator appears to be better suited for activity quantification, yielding a smaller variability in activity estimates, whereas the LEHR results are more severely affected by septal penetration. Activity quantification for 177Lu using the hand-held camera is found to be feasible. The readily available nature of the hand-held camera may enable more frequent activity quantification in e.g., superficial structures in patients or in the pre-clinical setting.","PeriodicalId":11559,"journal":{"name":"EJNMMI Physics","volume":"149 1","pages":""},"PeriodicalIF":4.0,"publicationDate":"2024-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139084449","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-03DOI: 10.1186/s40658-023-00604-0
Alexandros Moraitis, Walter Jentzen, Gloria Reiter, Jochen Schmitz, Thorsten Dirk Pöppel, Manuel Weber, Ken Herrmann, Wolfgang Peter Fendler, Pedro Fragoso Costa, Andreas Bockisch, David Kersting
Positron emission tomography (PET) using 124I-mIBG has been established for imaging and pretherapeutic dosimetry. Here, we report the first systematic analysis of the biodistribution and radiation dosimetry of 124I-mIBG in patients with neural crest tumours and project the results to paediatric patient models. Adult patients with neural crest tumours who underwent sequential 124I-mIBG PET were included in this retrospective single-center analysis. PET data were acquired 4, 24, 48, and/or 120 h after administration of a mean of 43 MBq 124I-mIBG. Whole-body counting and blood sampling were performed at 2, 4, 24, 48 and 120 h after administration. Absorbed organ dose and effective dose coefficients were estimated in OLINDA/EXM 2.2 according to the MIRD formalism. Extrapolation to paediatric models was performed based on mass-fraction scaling of the organ-specific residence times. Biodistribution data for adults were also projected to 123I-mIBG and 131I-mIBG. Twenty-one patients (11 females, 10 males) were evaluated. For adults, the organs exposed to the highest dose per unit administered activity were urinary bladder (1.54 ± 0.40 mGy/MBq), salivary glands (0.77 ± 0.28 mGy/MBq) and liver (0.65 ± 0.22 mGy/MBq). Mean effective dose coefficient for adults was 0.25 ± 0.04 mSv/MBq (male: 0.24 ± 0.03 mSv/MBq, female: 0.26 ± 0.06 mSv/MBq), and increased gradually to 0.29, 0.44, 0.69, 1.21, and 2.94 mSv/MBq for the 15-, 10-, 5-, 1-years-old, and newborn paediatric reference patients. Projected mean effective dose coefficients for 123I-mIBG and 131I-mIBG for adults were 0.014 ± 0.002 mSv/MBq and 0.18 ± 0.04 mSv/MBq, respectively. PET-based derived radiation dosimetry data for 124I-mIBG from this study agreed well with historical projected data from ICRP 53. The effective dose coefficients presented here may aid in guidance for establishing weight-based activity administration protocols.
{"title":"Biodistribution and radiation dosimetry of 124I-mIBG in adult patients with neural crest tumours and extrapolation to paediatric models","authors":"Alexandros Moraitis, Walter Jentzen, Gloria Reiter, Jochen Schmitz, Thorsten Dirk Pöppel, Manuel Weber, Ken Herrmann, Wolfgang Peter Fendler, Pedro Fragoso Costa, Andreas Bockisch, David Kersting","doi":"10.1186/s40658-023-00604-0","DOIUrl":"https://doi.org/10.1186/s40658-023-00604-0","url":null,"abstract":"Positron emission tomography (PET) using 124I-mIBG has been established for imaging and pretherapeutic dosimetry. Here, we report the first systematic analysis of the biodistribution and radiation dosimetry of 124I-mIBG in patients with neural crest tumours and project the results to paediatric patient models. Adult patients with neural crest tumours who underwent sequential 124I-mIBG PET were included in this retrospective single-center analysis. PET data were acquired 4, 24, 48, and/or 120 h after administration of a mean of 43 MBq 124I-mIBG. Whole-body counting and blood sampling were performed at 2, 4, 24, 48 and 120 h after administration. Absorbed organ dose and effective dose coefficients were estimated in OLINDA/EXM 2.2 according to the MIRD formalism. Extrapolation to paediatric models was performed based on mass-fraction scaling of the organ-specific residence times. Biodistribution data for adults were also projected to 123I-mIBG and 131I-mIBG. Twenty-one patients (11 females, 10 males) were evaluated. For adults, the organs exposed to the highest dose per unit administered activity were urinary bladder (1.54 ± 0.40 mGy/MBq), salivary glands (0.77 ± 0.28 mGy/MBq) and liver (0.65 ± 0.22 mGy/MBq). Mean effective dose coefficient for adults was 0.25 ± 0.04 mSv/MBq (male: 0.24 ± 0.03 mSv/MBq, female: 0.26 ± 0.06 mSv/MBq), and increased gradually to 0.29, 0.44, 0.69, 1.21, and 2.94 mSv/MBq for the 15-, 10-, 5-, 1-years-old, and newborn paediatric reference patients. Projected mean effective dose coefficients for 123I-mIBG and 131I-mIBG for adults were 0.014 ± 0.002 mSv/MBq and 0.18 ± 0.04 mSv/MBq, respectively. PET-based derived radiation dosimetry data for 124I-mIBG from this study agreed well with historical projected data from ICRP 53. The effective dose coefficients presented here may aid in guidance for establishing weight-based activity administration protocols.","PeriodicalId":11559,"journal":{"name":"EJNMMI Physics","volume":"29 1","pages":""},"PeriodicalIF":4.0,"publicationDate":"2024-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139084038","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This study aims to decrease the scan time and enhance image quality in pediatric total-body PET imaging by utilizing multimodal artificial intelligence techniques. A total of 270 pediatric patients who underwent total-body PET/CT scans with a uEXPLORER at the Sun Yat-sen University Cancer Center were retrospectively enrolled. 18F-fluorodeoxyglucose (18F-FDG) was administered at a dose of 3.7 MBq/kg with an acquisition time of 600 s. Short-term scan PET images (acquired within 6, 15, 30, 60 and 150 s) were obtained by truncating the list-mode data. A three-dimensional (3D) neural network was developed with a residual network as the basic structure, fusing low-dose CT images as prior information, which were fed to the network at different scales. The short-term PET images and low-dose CT images were processed by the multimodal 3D network to generate full-length, high-dose PET images. The nonlocal means method and the same 3D network without the fused CT information were used as reference methods. The performance of the network model was evaluated by quantitative and qualitative analyses. Multimodal artificial intelligence techniques can significantly improve PET image quality. When fused with prior CT information, the anatomical information of the images was enhanced, and 60 s of scan data produced images of quality comparable to that of the full-time data. Multimodal artificial intelligence techniques can effectively improve the quality of pediatric total-body PET/CT images acquired using ultrashort scan times. This has the potential to decrease the use of sedation, enhance guardian confidence, and reduce the probability of motion artifacts.
本研究旨在利用多模态人工智能技术缩短小儿全身正电子发射计算机断层成像的扫描时间并提高图像质量。研究回顾性地纳入了270名在中山大学肿瘤防治中心使用uEXPLORER进行全身PET/CT扫描的儿科患者。18F-氟脱氧葡萄糖（18F-FDG）的剂量为3.7 MBq/kg，采集时间为600秒，通过截断列表模式数据获得短期扫描PET图像（6、15、30、60和150秒内采集）。以残差网络为基本结构开发了一个三维（3D）神经网络，将低剂量 CT 图像作为先验信息，按不同比例输入网络。通过多模态三维网络处理短期 PET 图像和低剂量 CT 图像，生成全长高剂量 PET 图像。非局部均值方法和没有融合 CT 信息的相同三维网络被用作参考方法。通过定量和定性分析评估了网络模型的性能。多模态人工智能技术能显著提高 PET 图像质量。当与先前的 CT 信息融合时，图像的解剖信息得到了增强，60 秒的扫描数据产生的图像质量可与全时数据相媲美。多模态人工智能技术能有效提高使用超短扫描时间获取的小儿全身 PET/CT 图像的质量。这有可能减少镇静剂的使用，增强监护人的信心，并降低运动伪影的概率。
{"title":"Reducing pediatric total-body PET/CT imaging scan time with multimodal artificial intelligence technology","authors":"Qiyang Zhang, Yingying Hu, Chao Zhou, Yumo Zhao, Na Zhang, Yun Zhou, Yongfeng Yang, Hairong Zheng, Wei Fan, Dong Liang, Zhanli Hu","doi":"10.1186/s40658-023-00605-z","DOIUrl":"https://doi.org/10.1186/s40658-023-00605-z","url":null,"abstract":"This study aims to decrease the scan time and enhance image quality in pediatric total-body PET imaging by utilizing multimodal artificial intelligence techniques. A total of 270 pediatric patients who underwent total-body PET/CT scans with a uEXPLORER at the Sun Yat-sen University Cancer Center were retrospectively enrolled. 18F-fluorodeoxyglucose (18F-FDG) was administered at a dose of 3.7 MBq/kg with an acquisition time of 600 s. Short-term scan PET images (acquired within 6, 15, 30, 60 and 150 s) were obtained by truncating the list-mode data. A three-dimensional (3D) neural network was developed with a residual network as the basic structure, fusing low-dose CT images as prior information, which were fed to the network at different scales. The short-term PET images and low-dose CT images were processed by the multimodal 3D network to generate full-length, high-dose PET images. The nonlocal means method and the same 3D network without the fused CT information were used as reference methods. The performance of the network model was evaluated by quantitative and qualitative analyses. Multimodal artificial intelligence techniques can significantly improve PET image quality. When fused with prior CT information, the anatomical information of the images was enhanced, and 60 s of scan data produced images of quality comparable to that of the full-time data. Multimodal artificial intelligence techniques can effectively improve the quality of pediatric total-body PET/CT images acquired using ultrashort scan times. This has the potential to decrease the use of sedation, enhance guardian confidence, and reduce the probability of motion artifacts.","PeriodicalId":11559,"journal":{"name":"EJNMMI Physics","volume":"34 1","pages":""},"PeriodicalIF":4.0,"publicationDate":"2024-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139080294","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-13DOI: 10.1186/s40658-023-00598-9
Yixuan Jia, Zongyu Li, Azadeh Akhavanallaf, Jeffrey A. Fessler, Yuni K. Dewaraja
90Y SPECT-based dosimetry following radioembolization (RE) in liver malignancies is challenging due to the inherent scatter and the poor spatial resolution of bremsstrahlung SPECT. This study explores a deep-learning-based absorbed dose-rate estimation method for 90Y that mitigates the impact of poor SPECT image quality on dosimetry and the accuracy–efficiency trade-off of Monte Carlo (MC)-based scatter estimation and voxel dosimetry methods. Our unified framework consists of three stages: convolutional neural network (CNN)-based bremsstrahlung scatter estimation, SPECT reconstruction with scatter correction (SC) and absorbed dose-rate map generation with a residual learning network (DblurDoseNet). The input to the framework is the measured SPECT projections and CT, and the output is the absorbed dose-rate map. For training and testing under realistic conditions, we generated a series of virtual patient phantom activity/density maps from post-therapy images of patients treated with 90Y-RE at our clinic. To train the scatter estimation network, we use the scatter projections for phantoms generated from MC simulation as the ground truth (GT). To train the dosimetry network, we use MC dose-rate maps generated directly from the activity/density maps of phantoms as the GT (Phantom + MC Dose). We compared performance of our framework (SPECT w/CNN SC + DblurDoseNet) and MC dosimetry (SPECT w/CNN SC + MC Dose) using normalized root mean square error (NRMSE) and normalized mean absolute error (NMAE) relative to GT. When testing on virtual patient phantoms, our CNN predicted scatter projections had NRMSE of 4.0% ± 0.7% on average. For the SPECT reconstruction with CNN SC, we observed a significant improvement on NRMSE (9.2% ± 1.7%), compared to reconstructions with no SC (149.5% ± 31.2%). In terms of virtual patient dose-rate estimation, SPECT w/CNN SC + DblurDoseNet had a NMAE of 8.6% ± 5.7% and 5.4% ± 4.8% in lesions and healthy livers, respectively; compared to 24.0% ± 6.1% and 17.7% ± 2.1% for SPECT w/CNN SC + MC Dose. In patient dose-rate maps, though no GT was available, we observed sharper lesion boundaries and increased lesion-to-background ratios with our framework. For a typical patient data set, the trained networks took ~ 1 s to generate the scatter estimate and ~ 20 s to generate the dose-rate map (matrix size: 512 × 512 × 194) on a single GPU (NVIDIA V100). Our deep learning framework, trained using true activity/density maps, has the potential to outperform non-learning voxel dosimetry methods such as MC that are dependent on SPECT image quality. Across comprehensive testing and evaluations on multiple targeted lesions and healthy livers in virtual patients, our proposed deep learning framework demonstrated higher (66% on average in terms of NMAE) estimation accuracy than the current “gold-standard” MC method. The enhanced computing speed with our framework without sacrificing accuracy is highly relevant for clinical dosimetry following 90Y-
{"title":"90Y SPECT scatter estimation and voxel dosimetry in radioembolization using a unified deep learning framework","authors":"Yixuan Jia, Zongyu Li, Azadeh Akhavanallaf, Jeffrey A. Fessler, Yuni K. Dewaraja","doi":"10.1186/s40658-023-00598-9","DOIUrl":"https://doi.org/10.1186/s40658-023-00598-9","url":null,"abstract":"90Y SPECT-based dosimetry following radioembolization (RE) in liver malignancies is challenging due to the inherent scatter and the poor spatial resolution of bremsstrahlung SPECT. This study explores a deep-learning-based absorbed dose-rate estimation method for 90Y that mitigates the impact of poor SPECT image quality on dosimetry and the accuracy–efficiency trade-off of Monte Carlo (MC)-based scatter estimation and voxel dosimetry methods. Our unified framework consists of three stages: convolutional neural network (CNN)-based bremsstrahlung scatter estimation, SPECT reconstruction with scatter correction (SC) and absorbed dose-rate map generation with a residual learning network (DblurDoseNet). The input to the framework is the measured SPECT projections and CT, and the output is the absorbed dose-rate map. For training and testing under realistic conditions, we generated a series of virtual patient phantom activity/density maps from post-therapy images of patients treated with 90Y-RE at our clinic. To train the scatter estimation network, we use the scatter projections for phantoms generated from MC simulation as the ground truth (GT). To train the dosimetry network, we use MC dose-rate maps generated directly from the activity/density maps of phantoms as the GT (Phantom + MC Dose). We compared performance of our framework (SPECT w/CNN SC + DblurDoseNet) and MC dosimetry (SPECT w/CNN SC + MC Dose) using normalized root mean square error (NRMSE) and normalized mean absolute error (NMAE) relative to GT. When testing on virtual patient phantoms, our CNN predicted scatter projections had NRMSE of 4.0% ± 0.7% on average. For the SPECT reconstruction with CNN SC, we observed a significant improvement on NRMSE (9.2% ± 1.7%), compared to reconstructions with no SC (149.5% ± 31.2%). In terms of virtual patient dose-rate estimation, SPECT w/CNN SC + DblurDoseNet had a NMAE of 8.6% ± 5.7% and 5.4% ± 4.8% in lesions and healthy livers, respectively; compared to 24.0% ± 6.1% and 17.7% ± 2.1% for SPECT w/CNN SC + MC Dose. In patient dose-rate maps, though no GT was available, we observed sharper lesion boundaries and increased lesion-to-background ratios with our framework. For a typical patient data set, the trained networks took ~ 1 s to generate the scatter estimate and ~ 20 s to generate the dose-rate map (matrix size: 512 × 512 × 194) on a single GPU (NVIDIA V100). Our deep learning framework, trained using true activity/density maps, has the potential to outperform non-learning voxel dosimetry methods such as MC that are dependent on SPECT image quality. Across comprehensive testing and evaluations on multiple targeted lesions and healthy livers in virtual patients, our proposed deep learning framework demonstrated higher (66% on average in terms of NMAE) estimation accuracy than the current “gold-standard” MC method. The enhanced computing speed with our framework without sacrificing accuracy is highly relevant for clinical dosimetry following 90Y-","PeriodicalId":11559,"journal":{"name":"EJNMMI Physics","volume":"103 1","pages":""},"PeriodicalIF":4.0,"publicationDate":"2023-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138579314","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-12DOI: 10.1186/s40658-023-00603-1
Pascal Bebié, Werner Lustermann, Jan Debus, Christian Ritzer, Günther Dissertori, Bruno Weber
Small Animal Fast Insert for MRI detector I (SAFIR-I) is a novel Positron Emission Tomography insert for a $$7,hbox {T}$$ Bruker BioSpec 70/30 Ultra Shield Refrigerated Magnetic Resonance Imaging (MRI) system. It facilitates truly simultaneous quantitative imaging in mice and rats at injected activities as high as $$500,hbox {MBq}$$ . Exploitation of the resulting high count rates enables quick image formation at few seconds per frame. In this investigation, key performance parameters of SAFIR-I have been determined according to the evaluations outlined in the National Electrical Manufacturers Association (NEMA) Standards Publication NU 4-2008 (NEMA-NU4) protocol. Using an energy window of 391 to $$601,hbox {keV}$$ and a Coincidence Timing Window of $$500,hbox {ps}$$ , the following performance was observed: The average spatial resolution at $$5,hbox {mm}$$ radial offset (Full Width at Half Maximum) is $$2.54,hbox {mm}$$ when using Filtered Backprojection, 3D Reprojection reconstruction. For the mouse- and rat-like phantoms, the maximal Noise-Equivalent Count Rates (NECRs) are $$1368,hbox {kcps}$$ at the highest tested average effective concentration of $$14.7,hbox {MBq},hbox {cc}^{-1}$$ , and $$713,hbox {kcps}$$ at the highest tested average effective concentration of $$1.72,hbox {MBq},hbox {cc}^{-1}$$ , respectively. The NECR peak is not yet reached for either of these cases. The peak sensitivity is $$1.46,%$$ . The Image Quality phantom uniformity standard deviation is $$4.8,%$$ . The Recovery Coefficient for the $$5,hbox {mm}$$ rod is $$(1.08 pm 0.10)$$ . The Spill-Over Ratios are $$(0.22 pm 0.03)$$ and $$(0.22 pm 0.02)$$ , for the water- and air-filled cylinder, respectively. An accuracy of $$4.3,%$$ was achieved for the quantitative calibration of reconstructed voxel values. The measured performance parameters indicate that the various design goals have been achieved. SAFIR-I offers excellent performance, especially at the high activities it was designed for. This facilitates planned experiments with fast tracer kinetics in small animals. Ways to potentially improve performance can still be explored. Simultaneously, further performance gains can be expected for a forthcoming insert featuring 2.7 times longer axial coverage named Small Animal Fast Insert for MRI detector II (SAFIR-II).
{"title":"SAFIR-I: first NEMA NU 4-2008-based performance characterization","authors":"Pascal Bebié, Werner Lustermann, Jan Debus, Christian Ritzer, Günther Dissertori, Bruno Weber","doi":"10.1186/s40658-023-00603-1","DOIUrl":"https://doi.org/10.1186/s40658-023-00603-1","url":null,"abstract":"Small Animal Fast Insert for MRI detector I (SAFIR-I) is a novel Positron Emission Tomography insert for a $$7,hbox {T}$$ Bruker BioSpec 70/30 Ultra Shield Refrigerated Magnetic Resonance Imaging (MRI) system. It facilitates truly simultaneous quantitative imaging in mice and rats at injected activities as high as $$500,hbox {MBq}$$ . Exploitation of the resulting high count rates enables quick image formation at few seconds per frame. In this investigation, key performance parameters of SAFIR-I have been determined according to the evaluations outlined in the National Electrical Manufacturers Association (NEMA) Standards Publication NU 4-2008 (NEMA-NU4) protocol. Using an energy window of 391 to $$601,hbox {keV}$$ and a Coincidence Timing Window of $$500,hbox {ps}$$ , the following performance was observed: The average spatial resolution at $$5,hbox {mm}$$ radial offset (Full Width at Half Maximum) is $$2.54,hbox {mm}$$ when using Filtered Backprojection, 3D Reprojection reconstruction. For the mouse- and rat-like phantoms, the maximal Noise-Equivalent Count Rates (NECRs) are $$1368,hbox {kcps}$$ at the highest tested average effective concentration of $$14.7,hbox {MBq},hbox {cc}^{-1}$$ , and $$713,hbox {kcps}$$ at the highest tested average effective concentration of $$1.72,hbox {MBq},hbox {cc}^{-1}$$ , respectively. The NECR peak is not yet reached for either of these cases. The peak sensitivity is $$1.46,%$$ . The Image Quality phantom uniformity standard deviation is $$4.8,%$$ . The Recovery Coefficient for the $$5,hbox {mm}$$ rod is $$(1.08 pm 0.10)$$ . The Spill-Over Ratios are $$(0.22 pm 0.03)$$ and $$(0.22 pm 0.02)$$ , for the water- and air-filled cylinder, respectively. An accuracy of $$4.3,%$$ was achieved for the quantitative calibration of reconstructed voxel values. The measured performance parameters indicate that the various design goals have been achieved. SAFIR-I offers excellent performance, especially at the high activities it was designed for. This facilitates planned experiments with fast tracer kinetics in small animals. Ways to potentially improve performance can still be explored. Simultaneously, further performance gains can be expected for a forthcoming insert featuring 2.7 times longer axial coverage named Small Animal Fast Insert for MRI detector II (SAFIR-II).","PeriodicalId":11559,"journal":{"name":"EJNMMI Physics","volume":"28 1","pages":""},"PeriodicalIF":4.0,"publicationDate":"2023-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138576721","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-11DOI: 10.1186/s40658-023-00600-4
Alaaddin Ibrahimy, Jocelyn Hoye, Hao Wu, Bart de Laat, Su Jin Kim, David L. Wilson, Evan D. Morris
Drug occupancy studies with positron emission tomography imaging are used routinely in early phase drug development trials. Recently, our group introduced the Lassen Plot Filter, an extended version of the standard Lassen plot to estimate voxel-level occupancy images. Occupancy images can be used to create an EC50 image by applying an Emax model at each voxel. Our goal was to apply functional clustering of occupancy images via a clustering algorithm and produce a more precise EC50 image while maintaining accuracy. A digital brain phantom was used to create 10 occupancy images (corresponding to 10 different plasma concentrations of drug) that correspond to a ground truth EC50 image containing two bilateral local “hot spots” of high EC50 (region-1: 25; region-2: 50; background: 6–10 ng/mL). Maximum occupancy was specified as 0.85. An established noise model was applied to the simulated occupancy images and the images were smoothed. Simple Linear Iterative Clustering, an existing k-means clustering algorithm, was modified to segment a series of occupancy images into K clusters (which we call “SLIC-Occ”). EC50 images were estimated by nonlinear estimation at each cluster (post SLIC-Occ) and voxel (no clustering). Coefficient of variation images were estimated at each cluster and voxel, respectively. The same process was also applied to human occupancy data produced for a previously published study. Variability in EC50 estimates was reduced by more than 80% in the phantom data after application of SLIC-Occ to occupancy images with only minimal loss of accuracy. A similar, but more modest improvement was achieved in variability when SLIC-Occ was applied to human occupancy images. Our results suggest that functional segmentation of occupancy images via SLIC-Occ could produce more precise EC50 images and improve our ability to identify local “hot spots” of high effective affinity of a drug for its target(s).
{"title":"SLIC-Occ: functional segmentation of occupancy images improves precision of EC50 images","authors":"Alaaddin Ibrahimy, Jocelyn Hoye, Hao Wu, Bart de Laat, Su Jin Kim, David L. Wilson, Evan D. Morris","doi":"10.1186/s40658-023-00600-4","DOIUrl":"https://doi.org/10.1186/s40658-023-00600-4","url":null,"abstract":"Drug occupancy studies with positron emission tomography imaging are used routinely in early phase drug development trials. Recently, our group introduced the Lassen Plot Filter, an extended version of the standard Lassen plot to estimate voxel-level occupancy images. Occupancy images can be used to create an EC50 image by applying an Emax model at each voxel. Our goal was to apply functional clustering of occupancy images via a clustering algorithm and produce a more precise EC50 image while maintaining accuracy. A digital brain phantom was used to create 10 occupancy images (corresponding to 10 different plasma concentrations of drug) that correspond to a ground truth EC50 image containing two bilateral local “hot spots” of high EC50 (region-1: 25; region-2: 50; background: 6–10 ng/mL). Maximum occupancy was specified as 0.85. An established noise model was applied to the simulated occupancy images and the images were smoothed. Simple Linear Iterative Clustering, an existing k-means clustering algorithm, was modified to segment a series of occupancy images into K clusters (which we call “SLIC-Occ”). EC50 images were estimated by nonlinear estimation at each cluster (post SLIC-Occ) and voxel (no clustering). Coefficient of variation images were estimated at each cluster and voxel, respectively. The same process was also applied to human occupancy data produced for a previously published study. Variability in EC50 estimates was reduced by more than 80% in the phantom data after application of SLIC-Occ to occupancy images with only minimal loss of accuracy. A similar, but more modest improvement was achieved in variability when SLIC-Occ was applied to human occupancy images. Our results suggest that functional segmentation of occupancy images via SLIC-Occ could produce more precise EC50 images and improve our ability to identify local “hot spots” of high effective affinity of a drug for its target(s).","PeriodicalId":11559,"journal":{"name":"EJNMMI Physics","volume":"15 1","pages":""},"PeriodicalIF":4.0,"publicationDate":"2023-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138568172","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-08DOI: 10.1186/s40658-023-00601-3
Mathieu Pavoine, Philippe Thuillier, Nicolas Karakatsanis, Delphine Legoupil, Karim Amrane, Romain Floch, Romain Le Pennec, Pierre-Yves Salaün, Ronan Abgral, David Bourhis
The aim was to investigate the feasibility of a shortened dynamic whole-body (dWB) FDG-PET/CT protocol and Patlak imaging using a population-based input function (PBIF), instead of an image-derived input function (IDIF) across the 60-min post-injection period, and study its effect on the FDG influx rate (Ki) quantification in patients with metastatic melanoma (MM) undergoing immunotherapy. Thirty-seven patients were enrolled, including a PBIF modeling group (n = 17) and an independent validation cohort (n = 20) of MM from the ongoing prospective IMMUNOPET2 trial. All dWB-PET data were acquired on Vision 600 PET/CT systems. The PBIF was fitted using a Feng’s 4-compartments model and scaled to the individual IDIF tail’s section within the shortened acquisition time. The area under the curve (AUC) of PBIFs was compared to respective IDIFs AUC within 9 shortened time windows (TW) in terms of linear correlation (R2) and Bland–Altman tests. Ki metrics calculated with PBIF vs IDIF on 8 organs with physiological tracer uptake, 44 tumoral lesions of MM and 11 immune-induced inflammatory sites of pseudo-progression disease were also compared (Mann–Whitney test). The mean ± SD relative AUC bias was calculated at 0.5 ± 3.8% (R2 = 0.961, AUCPBIF = 1.007 × AUCIDIF). In terms of optimal use in routine practice and statistical results, the 5th–7th pass (R2 = 0.999 for both Ki mean and Ki max) and 5th–8th pass (mean ± SD bias = − 4.9 ± 6.5% for Ki mean and − 4.8% ± 5.6% for Ki max) windows were selected. There was no significant difference in Ki values from PBIF5_7 vs IDIF5_7 for physiological uptakes (p > 0.05) as well as for tumor lesions (mean ± SD Ki IDIF5_7 3.07 ± 3.27 vs Ki PBIF5_7 2.86 ± 2.96 100ml/ml/min, p = 0.586) and for inflammatory sites (mean ± SD Ki IDIF5_7 1.13 ± 0.59 vs Ki PBIF5_7 1.13 ± 0.55 100ml/ml/min, p = 0.98). Our study showed the feasibility of a shortened dWB-PET imaging protocol with a PBIF approach, allowing to reduce acquisition duration from 70 to 20 min with reasonable bias. These findings open perspectives for its clinical use in routine practice such as treatment response assessment in oncology.
{"title":"Clinical application of a population-based input function (PBIF) for a shortened dynamic whole-body FDG-PET/CT protocol in patients with metastatic melanoma treated by immunotherapy","authors":"Mathieu Pavoine, Philippe Thuillier, Nicolas Karakatsanis, Delphine Legoupil, Karim Amrane, Romain Floch, Romain Le Pennec, Pierre-Yves Salaün, Ronan Abgral, David Bourhis","doi":"10.1186/s40658-023-00601-3","DOIUrl":"https://doi.org/10.1186/s40658-023-00601-3","url":null,"abstract":"The aim was to investigate the feasibility of a shortened dynamic whole-body (dWB) FDG-PET/CT protocol and Patlak imaging using a population-based input function (PBIF), instead of an image-derived input function (IDIF) across the 60-min post-injection period, and study its effect on the FDG influx rate (Ki) quantification in patients with metastatic melanoma (MM) undergoing immunotherapy. Thirty-seven patients were enrolled, including a PBIF modeling group (n = 17) and an independent validation cohort (n = 20) of MM from the ongoing prospective IMMUNOPET2 trial. All dWB-PET data were acquired on Vision 600 PET/CT systems. The PBIF was fitted using a Feng’s 4-compartments model and scaled to the individual IDIF tail’s section within the shortened acquisition time. The area under the curve (AUC) of PBIFs was compared to respective IDIFs AUC within 9 shortened time windows (TW) in terms of linear correlation (R2) and Bland–Altman tests. Ki metrics calculated with PBIF vs IDIF on 8 organs with physiological tracer uptake, 44 tumoral lesions of MM and 11 immune-induced inflammatory sites of pseudo-progression disease were also compared (Mann–Whitney test). The mean ± SD relative AUC bias was calculated at 0.5 ± 3.8% (R2 = 0.961, AUCPBIF = 1.007 × AUCIDIF). In terms of optimal use in routine practice and statistical results, the 5th–7th pass (R2 = 0.999 for both Ki mean and Ki max) and 5th–8th pass (mean ± SD bias = − 4.9 ± 6.5% for Ki mean and − 4.8% ± 5.6% for Ki max) windows were selected. There was no significant difference in Ki values from PBIF5_7 vs IDIF5_7 for physiological uptakes (p > 0.05) as well as for tumor lesions (mean ± SD Ki IDIF5_7 3.07 ± 3.27 vs Ki PBIF5_7 2.86 ± 2.96 100ml/ml/min, p = 0.586) and for inflammatory sites (mean ± SD Ki IDIF5_7 1.13 ± 0.59 vs Ki PBIF5_7 1.13 ± 0.55 100ml/ml/min, p = 0.98). Our study showed the feasibility of a shortened dWB-PET imaging protocol with a PBIF approach, allowing to reduce acquisition duration from 70 to 20 min with reasonable bias. These findings open perspectives for its clinical use in routine practice such as treatment response assessment in oncology.","PeriodicalId":11559,"journal":{"name":"EJNMMI Physics","volume":"36 1","pages":""},"PeriodicalIF":4.0,"publicationDate":"2023-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138552845","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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