scholarly journals Prediction of Treatment Outcomes in Patients with Chest Wall Sarcoma: Evaluation with PET/CT

2012 ◽  
Vol 42 (10) ◽  
pp. 912-918 ◽  
Author(s):  
Y. Nishiyama ◽  
U. Tateishi ◽  
A. Kawai ◽  
H. Chuman ◽  
F. Nakatani ◽  
...  
Keyword(s):  
Treatment Outcomes ◽  
Chest Wall ◽  
Pet Ct

Enhancing 18F-FDG-PET/CT analysis in lung cancer patients

2015 ◽  
Vol 54 (06) ◽  
pp. 247-254 ◽  
Author(s):  
A. Kapfhammer ◽  
T. Winkens ◽  
T. Lesser ◽  
A. Reissig ◽  
M. Steinert ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Image Fusion ◽  
Chest Wall ◽  
Fdg Pet ◽  
Ct Image ◽  
Lung Cancers ◽  
Pet Ct ◽  
Peripheral Lung ◽  
Fdg Pet Ct ◽  
Tumour Infiltration

SummaryAim: To retrospectively evaluate the feasibility and value of CT-CT image fusion to assess the shift of peripheral lung cancers with/-out chest wall infiltration, comparing computed tomography acquisitions in shallow-breathing (SB-CT) and deep-inspiration breath-hold (DIBH-CT) in patients undergoing FDG-PET/ CT for lung cancer staging. Methods: Image fusion of SB-CT and DIBH-CT was performed with a multimodal workstation used for nuclear medicine fusion imaging. The distance of intrathoracic landmarks and the positional shift of tumours were measured using semitransparent overlay of both CT series. Statistical analyses were adjusted for confounders of tumour infiltration. Cutoff levels were calculated for prediction of no-/infiltration. Results: Lateral pleural recessus and diaphragm showed the largest respiratory excursions. Infiltrating lung cancers showed more limited respiratory shifts than non-infiltrating tumours. A large respiratory tumour-motility accurately predicted non-infiltration. However, the tumour shifts were limited and variable, limiting the accuracy of prediction. Conclusion: This pilot fusion study proved feasible and allowed a simple analysis of the respiratory shifts of peripheral lung tumours using CT-CT image fusion in a PET/CT setting. The calculated cutoffs were useful in predicting the exclusion of chest wall infiltration but did not accurately predict tumour infiltration. This method can provide additional qualitative information in patients with lung cancers with contact to the chest wall but unclear CT evidence of infiltration undergoing PET/CT without the need of additional investigations. Considering the small sample size investigated, further studies are necessary to verify the obtained results.

scholarly journals Evaluation of image quality at the detector’s edge of dedicated breast positron emission tomography

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Yoko Satoh ◽  
Utaroh Motosugi ◽  
Masamichi Imai ◽  
Yoshie Omiya ◽  
Hiroshi Onishi
Keyword(s):  
Image Quality ◽  
Chest Wall ◽  
Clinical Studies ◽  
Breast Imaging ◽  
Phantom Study ◽  
Breast Lesions ◽  
Clinical Images ◽  
Dedicated Breast Pet ◽  
Pet Ct ◽  
Significant Difference

Abstract Background Using phantoms and clinical studies in prone hanging breast imaging, we assessed the image quality of a commercially available dedicated breast PET (dbPET) at the detector’s edge, where mammary glands near the chest wall are located. These are compared to supine PET/CT breast images of the same clinical subjects. Methods A breast phantom with four spheres (16-, 10-, 7.5-, and 5-mm diameter) was filled with 18F-fluorodeoxyglucose solution (sphere-to-background activity concentration ratio, 8:1). The spheres occupied five different positions from the top edge to the centre of the detector and were scanned for 5 min in each position. Reconstructed images were visually evaluated, and the contrast-to-noise ratio (CNR), contrast recovery coefficient (CRC) for all spheres, and coefficient of variation of the background (CVB) were calculated. Subsequently, clinical images obtained with standard supine PET/CT and prone dbPET were retrospectively analysed. Tumour-to-background ratios (TBRs) between breast cancer near the chest wall (close to the detector’s edge; peripheral group) and at other locations (non-peripheral group) were compared. The TBR of each lesion was compared between dbPET and PET/CT. Results Closer to the detector’s edge, the CNR and CRC of all spheres decreased while the CVB increased in the phantom study. The disadvantages of this placement were visually confirmed. Regarding clinical images, TBR of dbPET was significantly higher than that of PET/CT in both the peripheral (12.38 ± 6.41 vs 6.73 ± 3.5, p = 0.0006) and non-peripheral (12.44 ± 5.94 vs 7.71 ± 7.1, p = 0.0183) groups. There was no significant difference in TBR of dbPET between the peripheral and non-peripheral groups. Conclusion The phantom study revealed poorer image quality at < 2-cm distance from the detector’s edge than at other more central parts. In clinical studies, however, the visibility of breast lesions with dbPET was the same regardless of the lesion position, and it was higher than that in PET/CT. dbPET has a great potential for detecting breast lesions near the chest wall if they are at least 2 cm from the edge of the FOV, even in young women with small breasts.

PET/CT Scan as a Surrogate for Treatment Outcomes in Sarcoidosis

2019 ◽  
Author(s):  
N.S. Khakoo ◽  
S. Jabeen Isma ◽  
M.A. Campos ◽  
G. Holt ◽  
M. Mirsaeidi
Keyword(s):  
Ct Scan ◽  
Treatment Outcomes ◽  
Pet Ct ◽  
Pet Ct Scan

ML Segmentation Strategies for Object Interference Compensation in FDG-PET Lesion Quantification

2010 ◽  
Vol 49 (05) ◽  
pp. 537-541 ◽  
Author(s):  
E. Bernardi ◽  
F. Gallotta ◽  
C. Gianoli ◽  
F. Zito ◽  
P. Gerundini ◽  
...  
Keyword(s):  
Chest Wall ◽  
Lung Lesion ◽  
Basis Functions ◽  
Clinical Image ◽  
Partial Volume ◽  
Partial Volume Effects ◽  
Pet Ct ◽  
Initial Segmentation ◽  
Regional Basis ◽  
Volume Effects

Summary Background: Quantification of lesion activity by FDG uptake in oncological PET is severely limited by partial volume effects. A maximum likelihood (ML) expectation maximization (EM) algorithm considering regional basis functions (AWOSEM-region) had been previously developed. Regional basis functions are iteratively segmented and quantified, thus identifying the volume and the activity of the lesion. Objectives: Improvement of AWOSEM-region when analyzing proximal interfering hot objects is addressed by proper segmentation initialization steps and models of spill-out and partial volume effects. Conditions relevant to lung PET-CT studies are considered: 1) lesion close to hot organ (e.g. chest wall, heart and mediastinum), 2) two close lesions. Methods: CT image was considered for pre-segmenting hot anatomical structures, never for lesion identification, solely defined by iterations on PET data. Further resolution recovery beyond the smooth standard clinical image was necessary to start lesion segmentation. A watershed algorithm was used to separate two close lesions. A subtraction of the spill-out from a nearby hot organ was introduced to enhance a lesion for the initial segmentation and start the further quantification steps. Biograph scanner blurring was modeled from phantom data in order to implement the procedure for 3D clinical lung studies. Results: In simulations, the procedure was able to separate structures as close as one pixel-size (2.25 mm). Robustness against the input segmentation errors defining the addressed objects was tested showing that convergence was not sensitive to initial volume overestimates up to 130%. Poor robustness was found against underestimates. A clinical study of a small lung lesion close to chest wall displayed a good recovery of both lesion activity and volume. Conclusions: With proper initialization and models of spill-out from hot organs, AWOSEM-region can be successfully applied to lung oncological studies.

scholarly journals PET/CT appearance of desmoid tumour of the chest wall

2010 ◽  
Vol 83 (986) ◽  
pp. e39-e42 ◽  
Author(s):  
F F Souza ◽  
F M Fennessy ◽  
Q Yang ◽  
A D van den Abbeele
Keyword(s):  
Chest Wall ◽  
Desmoid Tumour ◽  
Pet Ct

Chest Wall Tuberculosis: Clinical Features and Treatment Outcomes

2009 ◽  
Vol 67 (4) ◽  
pp. 318 ◽  
Author(s):  
Young Joo Kim ◽  
Hee Jung Jeon ◽  
Chang Ho Kim ◽  
Jae Yong Park ◽  
Tae Hoon Jung ◽  
...  
Keyword(s):  
Treatment Outcomes ◽  
Chest Wall ◽  
Clinical Features
Sign in / Sign up

Export Citation Format

Share Document