A Process Model for Direct Correlation between Computed Tomography and Histopathology

Rationale and Objectives

Multimodal imaging techniques for capturing normal and diseased human anatomy and physiology are being developed to benefit patient clinical care, research, and education. In the past, the incorporation of histopathology into these multimodal datasets has been complicated by the large differences in image quality, content, and spatial association.

Materials and Methods

We have developed a novel system, the large-scale image microtome array (LIMA), to bridge the gap between nonstructurally destructive and destructive imaging such that reliable registration between radiological data and histopathology can be achieved. Registration algorithms have been designed to align the multimodal datasets, which include computed tomography, computed micro-tomography, LIMA, and histopathology data to a common coordinate system.

Results

The resulting volumetric dataset provides an abundance of valuable information relating to the tissue sample including density, anatomical structure, color, texture, and cellular information in three dimensions. An image processing pipeline has been established to register all the multimodal data to a common coordinate system.

Conclusion

In this study, we have chosen to use human lung cancer nodules as an example; however, the flexibility of the image acquisition and subsequent processing algorithms makes it applicable to any soft organ tissue. A novel process model has been established to generate cross registered multimodal datasets for the investigation of human lung cancer nodule content and associated image-based representation.

The development of multimodal image acquisition and analysis continues to progress rapidly, driven by the potential to improve diagnostic and therapeutic aspects of medical care. Multimodal imaging, such as the combination of computed tomography (CT) and positron emission tomography, have been shown to improve the ability to identify potential disease states, such as lung nodules, and facilitate the planning of effective treatment approaches ( . However, the diagnostic “gold standard” in solid tumor oncology remains histological examination of diseased tissue, which requires invasive tissue sampling.

To understand and exploit all the information captured in noninvasive imaging techniques, accurate registration between the histological truth and the corresponding radiological appearance is required. This is complicated to achieve, because of the large differences in image quality, content, and loss of spatial association. Datasets that incorporate the corresponding histological truth are required to effectively evaluate the diagnostic ability of these systems.

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Figure 1

Summary of the large image microscope array (LIMA) system. This system serves to section the tissue while recording the spatial correspondence between sections by imaging the tissue block before the removal of a section. Multiple subimages are acquired as the camera and microscope coupling rastor scan over the tissue surface. The surface is then sectioned and removed from the tissue block, via a vibrating knife, for subsequent histological processing. This process results in the generation of a color dataset which can be used as the ground truth for linking nondestructive image sets, such as computed tomography to destructive histopathology image sets.

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Materials and methods

Patients

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Table 1

Patient Demographic Information

Characteristic Mean Range Age (y) 66 51–79 Body mass index 26 21–33 Nodule size from radiology report Maximum diameter 27 14–50 Minimum diameter 19 10–15n% Gender Male 2 18 Female 9 82 Race Caucasian 10 91 African American 1 9 Site Right upper lobe 1 9 Right lower lobe 6 55 Left upper lobe 3 27% Left lower lobe 1 9 Diagnosis Adenocarcinoma 7 64 Squamous cell carcinoma 3 27 Neuroendocrine carcinoma 1 9 Stage Stage IA 5 46 Stage IB 3 27% Stage IIB 2 18% Stage IIIA 1 9 Smoking history Never 1 9 <1 pack per week 2 18 1 pack per week 6 55 2 pack per week 1 9 3 pack per week 1 9

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Data Acquisition Process

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Some Specific Challenges in Multimodal acquisition

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Registration

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2D Rigid Registration

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fS(x)=λAx+t f

(

)

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D(fS)=∑ni=1||fS(Lrim)−Lrif||2=∑ni=1||λALrim+t−Lrif||2 D

(

)

∑

(

)

−

∑

−

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FRE=D(fs)=∑ni=1||λALrim+t−Lrif||2 F

(

)

∑

−

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TRE=∑ni=1||λALeim+t−Leif||2 T

∑

−

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2D Nonrigid Registration

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fTPS(x)=Dx+∑ni−1wiU(∣∣x−Lrim∣∣) f

(

)

∑

−

(

−

)

where U(r)=r2logr2 U

(

)

log

2 , D is a matrix representing the affine transformation and w i is the warping coefficient matrix that represents the non-affine deformation. The bending energy function, E, is minimized to control the extent of warping to the smallest deformation possible:

E[f(x)]=∬R2(f2xx+2f2xy+f2yy)dxdy E

[

(

)

]

∬

ℜ

(

)

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FRE=∑ni=1∣∣∣∣fTPS(Lrim)−Lrif||2=0 F

∑

(

)

−

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TRE=∑ni=1||fTPS(Leim)−Leif||2 T

∑

(

)

−

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3D Rigid Registration

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fR(x)=Ax+t f

(

)

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NMI(A,B)=H(A)+H(B)H(A,B) N

(

)

(

)

(

)

(

)

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H(X)=−∑ni=1p(xi)logbp(xi) H

(

)

−

∑

(

)

log

(

)

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Results

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Discussion

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Acknowledgment

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References

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A Process Model for Direct Correlation between Computed Tomography and Histopathology

Rationale and Objectives

Materials and Methods

Results

Conclusion

Materials and methods

Patients

Data Acquisition Process

Some Specific Challenges in Multimodal acquisition

Registration

2D Rigid Registration

2D Nonrigid Registration

3D Rigid Registration

Results

Discussion

Acknowledgment

References

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