Observer Study of Reconstruction Strategies for Detection of Solitary Pulmonary Nodules Using Hybrid...
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Observer Study of Reconstruction Strategies for Detection of Solitary Pulmonary Nodules Using Hybrid NeoTect SPECT Images. Xiaoming Zheng, PhD. 20 October, 2004. Outlines. Lung Cancer and SPECT/PET NeoTect in Lung SPECT Image Reconstructions: RBI vs FBP Hybrid Images: Clinical Reality

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Xiaoming zheng phd 20 october 2004

Observer Study of Reconstruction Strategies for Detection of Solitary Pulmonary Nodules Using Hybrid NeoTect SPECT Images

Xiaoming Zheng, PhD.

20 October, 2004


Outlines

Outlines

  • Lung Cancer and SPECT/PET

  • NeoTect in Lung SPECT

  • Image Reconstructions: RBI vs FBP

  • Hybrid Images: Clinical Reality

  • Observer Studies: Human vs Numerical

  • ROC: Receiver Operating Characteristics

  • Results and Conclusions


Leading causes of cancer deaths

Leading Causes of Cancer Deaths


Nsclc non small cell lung cancer

NSCLC: Non-Small-Cell-Lung-Cancer

  • Surgery is providing the best chance of cure if tumor can be re-sected completely.

  • If cancer has spread to contra-lateral lymph nodes or beyond the chest surgery alone is not useful. Chemotherapy and/or radiotherapy are usually applied. These measures are rarely curative


Spn solitary pulmonary nodule

SPN: Solitary Pulmonary Nodule

  • Approx. 30% of new cases of lung cancer are found as an SPN

  • An SPN is defined as:

    • single pulmonary lesion

    • well defined borders

    • mean diameter not more than 3 cm

  • Found in 1 : 500 chest X-rays


Spect and pet

SPECT and PET


Neotect spect vs fdg pet

NeoTect/SPECT vs FDG/PET


Neotect 99m tc depreotide

NeoTect (99mTc-Depreotide)

  • Binds to Somatostatin receptors, which are over-expressed in lung cancer (NSCLC and SCLC)

  • Has a negative predictive value of up to 98% in combination with CT or chest X-ray for SPN

  • Procedure is non-invasive

  • 99mTc-labelled - readily available

  • Procedure is easy

  • Can be used wherever SPECT is available


Xiaoming zheng phd 20 october 2004

Binding region for SSTR*

N

H

2

N

H

2

(N-Me)Phe-Tyr

O

D-Trp

O

O

H

N

N

N

O

2

Lys

N

H

T

c

Hcy-Val

O

N

H

S

H

N

2

O

NeoTect

  • - a small synthetic peptide

  • - 10 amino acids, mol. wt. 1358 Da

  • - binding region for the somatostatin receptor

  • - radio-labeled with 99mTc


How neotect works

How NeoTect Works

  • Malignant tumors over-express somatostatin receptors (SSTRs)

  • NeoTect binds to and detects SSTRs

  • Most benign lesions do not over-express SSTRs


Xiaoming zheng phd 20 october 2004

Normal Transaxial SPECT Images


Xiaoming zheng phd 20 october 2004

CT

Coronal SPECT

72 yr female smoker, complaining of weight loss; chest x-ray: 2.5 cm LUL lesion; CT: LUL 2.0 cm spiculated mass; Histopathology (CT guided FNA biopsy): poorly-differentiated adenocarcinoma

Transaxial SPECT


Aims of this work

Aims of This Work

  • Use hybrid images of lung tumor imaging agent Tc-99m NeoTect in Localization Receiver Operating Characteristic (LROC) studies to determine reconstruction parameters and whether iterative reconstruction with attenuation, scatter, and distant resolution compensation should replace FBP clinically.


Why hybrid images

Why Hybrid Images

  • The Optimization of reconstruction parameters, and determination of whether iterative reconstruction should replace FBP clinically should be based on tasks which closely approximate the clinical application of the images

  • The use of hybrid images or studies represents a practical alternative to the use of purely clinical acquisitions for observer studies.


How hybrid images were created

How Hybrid Images Were Created

  • Simulated lesions are added to know normal clinical acquisitions

  • Monte Carlo simulation package SIMIND was used to simulate lesions.

  • Nine normal patient’s projection data were used to create 162 tumors randomly distributed within the lung regions.

  • Tumors were 1 cm in diameter which is the smallest tumor could be detected by CT.


Neotect projections from clinical 9 patients

NeoTect ProjectionsFrom Clinical 9 Patients


Tumor source projections from monte carlo simulation

Tumor Source ProjectionsFrom Monte Carlo Simulation


Images reconstructions

Images Reconstructions

  • Iterative Reconstruction: Rescaled Block Iterative Algorithm including attenuation, scatter, and distance resolution compensation. Parameters tested: iteration 1,3,5,7,10 and post Gaussian filter FWHM 0,1,2,3,4 pixels

  • Filtered Back-Projection: Parameters tested: Butterworth filter cut-off frequencies: 0.10, 0.15, 0.20, 0.25 and 0.30 pixel-1


Filtered back projection

Filtered Back-Projection


Butterworth filter and cutoff frequency

Butterworth Filter and Cutoff Frequency


Fbp reconstructed images

FBP Reconstructed Images


Iterative reconstruction

Iterative Reconstruction


Rescaled block iterative reconstruction algorithm

Rescaled Block Iterative Reconstruction Algorithm


Attenuation compensation

Attenuation Compensation


Scatter compensation

Scatter Compensation


Resolution compensation

Resolution Compensation


Rbi reconstructed images

RBI Reconstructed Images


Receiver operating characteristics

Receiver Operating Characteristics


Images for observers

Images for Observers


Numerical observers

Numerical Observers


Types of channels

Types of Channels


Xiaoming zheng phd 20 october 2004

Human

Observer

Interface

RBI


Xiaoming zheng phd 20 october 2004

Human

Observer

Interface

FBP


Numerical observer results rbi

Numerical Observer Results: RBI


Numerical observer results fbp

Numerical Observer Results: FBP


Human observer results

Human Observer Results


Conclusions

Conclusions

  • Iterative RBI-EM including all corrections performs better than that of FBP.

  • The best performance reconstruction strategy is RBI-EM with 5 iteration and 1 pixel FWHM in Gaussian post-filtering.

  • Numerical observer with and without mean background subtraction set the upper and lower bounds achievable by human observer.


Acknowlegements

Acknowlegements

  • This work was supported by a Charles Sturt University Special Study grant and a NIH research grant.

  • The co-authors of this work are Prof Mike King, Dr Howard Gifford and Dr Hennie Pretorius at the University of Massachusetts Medical School.


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