Metal artifact reduction on cervical CT images by deep residual learning

Background

Cervical cancer is the fifth most common cancer among women, which is the third leading cause of cancer death in women worldwide. Brachytherapy is the most effective treatment for cervical cancer. For brachytherapy, computed tomography (CT) imaging is necessary since it conveys tissue density information which can be used for dose planning. However, the metal artifacts caused by brachytherapy applicators remain a challenge for the automatic processing of image data for image-guided procedures or accurate dose calculations. Therefore, developing an effective metal artifact reduction (MAR) algorithm in cervical CT images is of high demand.

Methods

A novel residual learning method based on convolutional neural network (RL-ARCNN) is proposed to reduce metal artifacts in cervical CT images. For MAR, a dataset is generated by simulating various metal artifacts in the first step, which will be applied to train the CNN. This dataset includes artifact-insert, artifact-free, and artifact-residual images. Numerous image patches are extracted from the dataset for training on deep residual learning artifact reduction based on CNN (RL-ARCNN). Afterwards, the trained model can be used for MAR on cervical CT images.

Results

The proposed method provides a good MAR result with a PSNR of 38.09 on the test set of simulated artifact images. The PSNR of residual learning (38.09) is higher than that of ordinary learning (37.79) which shows that CNN-based residual images achieve favorable artifact reduction. Moreover, for a 512?×?512 image, the average removal artifact time is less than 1 s.

Conclusions

The RL-ARCNN indicates that residual learning of CNN remarkably reduces metal artifacts and improves critical structure visualization and confidence of radiation oncologists in target delineation. Metal artifacts are eliminated efficiently free of sinogram data and complicated post-processing procedure.

     

Statistical learning methods as a preprocessing step for survival analysis: evaluation of concept using lung cancer data

Background

Despite its superb lateral resolution, flat-panel-detector (FPD) based tomosynthesis suffers from low contrast and inter-plane artifacts caused by incomplete cancellation of the projection components stemming from outside the focal plane. The incomplete cancellation of the projection components, mostly due to the limited scan angle in the conventional tomosynthesis scan geometry, often makes the image contrast too low to differentiate the malignant tissues from the background tissues with confidence.

Methods

In this paper, we propose a new method to suppress the inter-plane artifacts in FPD-based tomosynthesis. If 3D whole volume CT images are available before the tomosynthesis scan, the CT image data can be incorporated into the tomosynthesis image reconstruction to suppress the inter-plane artifacts, hence, improving the image contrast. In the proposed technique, the projection components stemming from outside the region-of-interest (ROI) are subtracted from the measured tomosynthesis projection data to suppress the inter-plane artifacts. The projection components stemming from outside the ROI are calculated from the 3D whole volume CT images which usually have lower lateral resolution than the tomosynthesis images. The tomosynthesis images are reconstructed from the subtracted projection data which account for the x-ray attenuation through the ROI. After verifying the proposed method by simulation, we have performed both CT scan and tomosynthesis scan on a phantom and a sacrificed rat using a FPD-based micro-CT.

Results

We have measured contrast-to-noise ratio (CNR) from the tomosynthesis images which is an indicator of the residual inter-plane artifacts on the focal-plane image. In both cases of the simulation and experimental imaging studies of the contrast evaluating phantom, CNRs have been significantly improved by the proposed method. In the rat imaging also, we have observed better visual contrast from the tomosynthesis images reconstructed by the proposed method.

Conclusions

The proposed tomosynthesis technique can improve image contrast with aids of 3D whole volume CT images. Even though local tomosynthesis needs extra 3D CT scanning, it may find clinical applications in special situations in which extra 3D CT scan is already available or allowed.     

Ring Artifacts Reduction in CBCT: Pixels Detection and Patch Based Correction

The ring artifacts introduced by the defective pixels with non-linear responses in the high-resolution detector, have a great impact on subsequent processing and quantitative analysis of the reconstructed images. In this paper, a multistep method is proposed to suppress the ring artifacts of micro CT images, which firstly locates the positions of the defective pixels in the sinogram, and then corrects the corresponding value in the projections. Since the defective pixels always appear as vertical stripes in the sinogram, a horizontal curve is derived by summing the pixel values along vertical direction, thus the abrupt segments related to the defective stripes are enhanced notably, and a proportion coefficient based on the second derivative of the curve is taken as the indicator for the position and the severity of the defective pixels. Then, the detected defective pixels in the sinogram are transferred and relocated in the projections, an improved 3D block matching filtering (BM3D) algorithm is applied to restore the defective pixels in corresponding projection images. In the end, the tomographic images are reconstructed from the corrected projections. In the experiment, a small piece of the motherwort’s rhizome and a part of a mouse’s lung are imaged by micro-CT, and the result shows that, compared with the other four state-of-art methods, the proposed method has a great reduction on the ring artifacts of the reconstructed images, and makes less impact in spatial resolution and contrast in the same time.     

Bone-induced streak artifact suppression in sparse-view CT image reconstruction

ABSTRACT: BACKGROUND: In sparse-view CT imaging, strong streak artifacts may appear around bony structures and they often compromise the image readability. Compressed sensing (CS) or total variation (TV) minimization-based image reconstruction method has reduced the streak artifacts to a great extent, but, sparse-view CT imaging still suffers from residual streak artifacts. We introduce a new bone-induced streak artifact reduction method in the CS-based image reconstruction. METHODS: We firstly identify the high-intensity bony regions from the image reconstructed by the filtered backprojection (FBP) method, and we calculate the sinogram stemming from the bony regions only. Then, we subtract the calculated sinogram, which stands for the bony regions, from the measured sinogram before performing the CS-based image reconstruction. The image reconstructed from the subtracted sinogram will stand for the soft tissues with little streak artifacts on it. To restore the original image intensity in the bony regions, we add the bony region image, which has been identified from the FBP image, to the soft tissue image to form a combined image. Then, we perform the CS-based image reconstruction again on the measured sinogram using the combined image as the initial condition of the iteration. For experimental validation of the proposed method, we take images of a contrast phantom and a rat using a micro-CT and we evaluate the reconstructed images based on two figures of merit, relative mean square error and total variation caused by the streak artifacts. RESULTS: The images reconstructed by the proposed method have been found to have smaller streak artifacts than the ones reconstructed by the original CS-based method when visually inspected. The quantitative image evaluation studies have also shown that the proposed method outperforms the conventional CS-based method. CONCLUSIONS: The proposed method can effectively suppress streak artifacts stemming from bony structures in sparse-view CT imaging.     

Characterization of Scattered X-Ray Photons in Dental Cone-Beam Computed Tomography

Purpose

Scatter is a very important artifact causing factor in dental cone-beam CT (CBCT), which has a major influence on the detectability of details within images. This work aimed to improve the image quality of dental CBCT through scatter correction.

Methods

Scatter was estimated in the projection domain from the low frequency component of the difference between the raw CBCT projection and the projection obtained by extrapolating the model fitted to the raw projections acquired with 2 different sizes of axial field-of-view (FOV). The function for curve fitting was optimized by using Monte Carlo simulation. To validate the proposed method, an anthropomorphic phantom and a water-filled cylindrical phantom with rod inserts simulating different tissue materials were scanned using 120 kVp, 5 mA and 9-second scanning time covering an axial FOV of 4 cm and 13 cm. The detectability of the CT image was evaluated by calculating the contrast-to-noise ratio (CNR).

Results

Beam hardening and cupping artifacts were observed in CBCT images without scatter correction, especially in those acquired with 13 cm FOV. These artifacts were reduced in CBCT images corrected by the proposed method, demonstrating its efficacy on scatter correction. After scatter correction, the image quality of CBCT was improved in terms of target detectability which was quantified as the CNR for rod inserts in the cylindrical phantom.

Conclusions

Hopefully the calculations performed in this work can provide a route to reach a high level of diagnostic image quality for CBCT imaging used in oral and maxillofacial structures whilst ensuring patient dose as low as reasonably achievable, which may ultimately make CBCT scan a reliable and safe tool in clinical practice.     

Artifacts in magnetic resonance imaging and computed tomography caused by dental materials

Background

Artifacts caused by dental restorations, such as dental crowns, dental fillings and orthodontic appliances, are a common problem in MRI and CT scans of the head and neck. The aim of this in-vitro study was to identify and evaluate the artifacts produced by different dental restoration materials in CT and MRI images.

Methods

Test samples of 44 materials (Metal and Non-Metal) commonly used in dental restorations were fabricated and embedded with reference specimens in gelatin moulds. MRI imaging of 1.5T and CT scan were performed on the samples and evaluated in two dimensions. Artifact size and distortions were measured using a digital image analysis software.

Results

In MRI, 13 out of 44 materials produced artifacts, while in CT 41 out of 44 materials showed artifacts. Artifacts produced in both MRI and CT images were categorized according to the size of the artifact.

Significance

Metal based restoration materials had strong influence on CT and less artifacts in MRI images. Rare earth elements such as Ytterbium trifluoride found in composites caused artifacts in both MRI and CT. Recognizing these findings would help dental materials manufacturers and developers to produce materials which can cause less artifacts in MRI and CT images.     

A Robust Post-Processing Workflow for Datasets with Motion Artifacts in Diffusion Kurtosis Imaging

Purpose

The aim of this study was to develop a robust post-processing workflow for motion-corrupted datasets in diffusion kurtosis imaging (DKI).

Materials and methods

The proposed workflow consisted of brain extraction, rigid registration, distortion correction, artifacts rejection, spatial smoothing and tensor estimation. Rigid registration was utilized to correct misalignments. Motion artifacts were rejected by using local Pearson correlation coefficient (LPCC). The performance of LPCC in characterizing relative differences between artifacts and artifact-free images was compared with that of the conventional correlation coefficient in 10 randomly selected DKI datasets. The influence of rejected artifacts with information of gradient directions and b values for the parameter estimation was investigated by using mean square error (MSE). The variance of noise was used as the criterion for MSEs. The clinical practicality of the proposed workflow was evaluated by the image quality and measurements in regions of interest on 36 DKI datasets, including 18 artifact-free (18 pediatric subjects) and 18 motion-corrupted datasets (15 pediatric subjects and 3 essential tremor patients).

Results

The relative difference between artifacts and artifact-free images calculated by LPCC was larger than that of the conventional correlation coefficient (p<0.05). It indicated that LPCC was more sensitive in detecting motion artifacts. MSEs of all derived parameters from the reserved data after the artifacts rejection were smaller than the variance of the noise. It suggested that influence of rejected artifacts was less than influence of noise on the precision of derived parameters. The proposed workflow improved the image quality and reduced the measurement biases significantly on motion-corrupted datasets (p<0.05).

Conclusion

The proposed post-processing workflow was reliable to improve the image quality and the measurement precision of the derived parameters on motion-corrupted DKI datasets. The workflow provided an effective post-processing method for clinical applications of DKI in subjects with involuntary movements.     

Quantifying the effect of soil moisture content on segmenting root system architecture in X-ray computed tomography images

Aims

A commonly accepted challenge when visualising plant roots in X-ray micro Computed Tomography (μCT) images is the similar X-ray attenuation of plant roots and soil phases. Soil moisture content remains a recognised, yet currently uncharacterised source of segmentation error. This work sought to quantify the effect of soil moisture content on the ability to segment roots from soil in μCT images.

Methods

Rice (Oryza sativa) plants grown in contrasting soils (loamy sand and clay loam) were μCT scanned daily for nine days whilst drying from saturation. Root volumes were segmented from μCT images and compared with volumes derived by root washing.

Results

At saturation the overlapping attenuation values of root material, water-filled soil pores and soil organic matter significantly hindered segmentation. However, in dry soil (ca. six days of drying post-saturation) the air-filled pores increased image noise adjacent to roots and impeded accurate visualisation of root material. The root volume was most accurately segmented at field capacity.

Conclusions

Root volumes can be accurately segmented from μCT images of undisturbed soil without compromising the growth requirements of the plant providing soil moisture content is kept at field capacity. We propose all future studies in this area should consider the error associated with scanning at different soil moisture contents.     

Speeding up the Consensus Clustering methodology for microarray data analysis

Background

Position-specific priors (PSP) have been used with success to boost EM and Gibbs sampler-based motif discovery algorithms. PSP information has been computed from different sources, including orthologous conservation, DNA duplex stability, and nucleosome positioning. The use of prior information has not yet been used in the context of combinatorial algorithms. Moreover, priors have been used only independently, and the gain of combining priors from different sources has not yet been studied.

Results

We extend RISOTTO, a combinatorial algorithm for motif discovery, by post-processing its output with a greedy procedure that uses prior information. PSP's from different sources are combined into a scoring criterion that guides the greedy search procedure. The resulting method, called GRISOTTO, was evaluated over 156 yeast TF ChIP-chip sequence-sets commonly used to benchmark prior-based motif discovery algorithms. Results show that GRISOTTO is at least as accurate as other twelve state-of-the-art approaches for the same task, even without combining priors. Furthermore, by considering combined priors, GRISOTTO is considerably more accurate than the state-of-the-art approaches for the same task. We also show that PSP's improve GRISOTTO ability to retrieve motifs from mouse ChiP-seq data, indicating that the proposed algorithm can be applied to data from a different technology and for a higher eukaryote.

Conclusions

The conclusions of this work are twofold. First, post-processing the output of combinatorial algorithms by incorporating prior information leads to a very efficient and effective motif discovery method. Second, combining priors from different sources is even more beneficial than considering them separately.     

Reduction of Metal Artifact in Single Photon-Counting Computed Tomography by Spectral-Driven Iterative Reconstruction Technique

PurposeThe exciting prospect of Spectral CT (SCT) using photon-counting detectors (PCD) will lead to new techniques in computed tomography (CT) that take advantage of the additional spectral information provided. We introduce a method to reduce metal artifact in X-ray tomography by incorporating knowledge obtained from SCT into a statistical iterative reconstruction scheme. We call our method Spectral-driven Iterative Reconstruction (SPIR).MethodThe proposed algorithm consists of two main components: material decomposition and penalized maximum likelihood iterative reconstruction. In this study, the spectral data acquisitions with an energy-resolving PCD were simulated using a Monte-Carlo simulator based on EGSnrc C++ class library. A jaw phantom with a dental implant made of gold was used as an object in this study. A total of three dental implant shapes were simulated separately to test the influence of prior knowledge on the overall performance of the algorithm. The generated projection data was first decomposed into three basis functions: photoelectric absorption, Compton scattering and attenuation of gold. A pseudo-monochromatic sinogram was calculated and used as input in the reconstruction, while the spatial information of the gold implant was used as a prior. The results from the algorithm were assessed and benchmarked with state-of-the-art reconstruction methods.ResultsDecomposition results illustrate that gold implant of any shape can be distinguished from other components of the phantom. Additionally, the result from the penalized maximum likelihood iterative reconstruction shows that artifacts are significantly reduced in SPIR reconstructed slices in comparison to other known techniques, while at the same time details around the implant are preserved. Quantitatively, the SPIR algorithm best reflects the true attenuation value in comparison to other algorithms.ConclusionIt is demonstrated that the combination of the additional information from Spectral CT and statistical reconstruction can significantly improve image quality, especially streaking artifacts caused by the presence of materials with high atomic numbers.     

Reducing the worst case running times of a family of RNA and CFG problems, using Valiant's approach

Background

RNA secondary structure prediction is a mainstream bioinformatic domain, and is key to computational analysis of functional RNA. In more than 30 years, much research has been devoted to defining different variants of RNA structure prediction problems, and to developing techniques for improving prediction quality. Nevertheless, most of the algorithms in this field follow a similar dynamic programming approach as that presented by Nussinov and Jacobson in the late 70's, which typically yields cubic worst case running time algorithms. Recently, some algorithmic approaches were applied to improve the complexity of these algorithms, motivated by new discoveries in the RNA domain and by the need to efficiently analyze the increasing amount of accumulated genome-wide data.

Results

We study Valiant's classical algorithm for Context Free Grammar recognition in sub-cubic time, and extract features that are common to problems on which Valiant's approach can be applied. Based on this, we describe several problem templates, and formulate generic algorithms that use Valiant's technique and can be applied to all problems which abide by these templates, including many problems within the world of RNA Secondary Structures and Context Free Grammars.

Conclusions

The algorithms presented in this paper improve the theoretical asymptotic worst case running time bounds for a large family of important problems. It is also possible that the suggested techniques could be applied to yield a practical speedup for these problems. For some of the problems (such as computing the RNA partition function and base-pair binding probabilities), the presented techniques are the only ones which are currently known for reducing the asymptotic running time bounds of the standard algorithms.     

Extrapontine myelinolysis presenting as acute parkinsonism

Background

Extrapontine myelinolysis presenting with extra pyramidal features suggestive of parkinsonism may be a challenging clinical syndrome. Clinicians should maintain their vigilance while correcting electrolyte imbalances, especially with associated co-morbidity.

Case presentation

A 41-year-old woman presented with acute parkinsonism like features while on a holiday. This followed slow correction of hyponatraemia after repeated vomiting. MRI changes were suggestive of Extrapontine myelinolysis(EPM). This case is at variance with four previous cases reported in the medical literature in that the patient made a full clinical recovery and the MR changes resolved with symptomatic support alone.

Conclusion

Extrapontine myelinolysis could make a complete recovery with symptomatic support alone. During hyponatraemia correction, rapid osmotic shifts of fluid that cause hypernatremia, causes myelinolysis rather than absolute serum sodium level. Even gradual correction of hyponatraemia can produce myelinolysis, especially with pre-existing malnourishment, alcoholism, drug misuse, Addison's disease and immuno-suppression. Pallidial sparing is typical of EPM in MRI scans.     

Quantitative Comparison of Commercial and Non-Commercial Metal Artifact Reduction Techniques in Computed Tomography

Objectives

Typical streak artifacts known as metal artifacts occur in the presence of strongly attenuating materials in computed tomography (CT). Recently, vendors have started offering metal artifact reduction (MAR) techniques. In addition, a MAR technique called the metal deletion technique (MDT) is freely available and able to reduce metal artifacts using reconstructed images. Although a comparison of the MDT to other MAR techniques exists, a comparison of commercially available MAR techniques is lacking. The aim of this study was therefore to quantify the difference in effectiveness of the currently available MAR techniques of different scanners and the MDT technique.

Materials and Methods

Three vendors were asked to use their preferential CT scanner for applying their MAR techniques. The scans were performed on a Philips Brilliance ICT 256 (S1), a GE Discovery CT 750 HD (S2) and a Siemens Somatom Definition AS Open (S3). The scans were made using an anthropomorphic head and neck phantom (Kyoto Kagaku, Japan). Three amalgam dental implants were constructed and inserted between the phantom’s teeth. The average absolute error (AAE) was calculated for all reconstructions in the proximity of the amalgam implants.

Results

The commercial techniques reduced the AAE by 22.0±1.6%, 16.2±2.6% and 3.3±0.7% for S1 to S3 respectively. After applying the MDT to uncorrected scans of each scanner the AAE was reduced by 26.1±2.3%, 27.9±1.0% and 28.8±0.5% respectively. The difference in efficiency between the commercial techniques and the MDT was statistically significant for S2 (p=0.004) and S3 (p<0.001), but not for S1 (p=0.63).

Conclusions

The effectiveness of MAR differs between vendors. S1 performed slightly better than S2 and both performed better than S3. Furthermore, for our phantom and outcome measure the MDT was more effective than the commercial MAR technique on all scanners.     

Computed tomography of the shoulders in patients with obstetric brachial plexus injuries: a retrospective study

Background

Scapular hypoplasia, elevation, and rotation (SHEAR) deformity and posterior subluxation of the humeral head are common tertiary sequelae of obstetric brachial plexus injuries (OBPI). Interpretations of images from bilateral computed tomography (CT) scans of the upper extremities are critical to the diagnosis and treatment plan for patients with these bony deformities resulting from OBPI.

Methods

We conducted a retrospective study to investigate the accuracy of radiologic reports in the diagnosis of SHEAR or posterior subluxation of the humeral head in OBPI patients. CT studies from 43 consecutive patients over a 33-month period were used in the study. For each patient, we compared the results from the radiologic report to those from a clinical examination given by the attending surgeon and to measurements taken from the CT studies by biomedical researchers.

Results

A comparison of SHEAR measured from the 3-D CT images to the diagnoses from the radiologists, revealed that only 40% of the radiological reports were accurate. However, there was a direct correlation between the use of the 3-D CT images and an accurate SHEAR diagnosis by the radiologists (p < 0.0001). When posterior subluxation was measured in the affected and contralateral shoulders, 93% of the patients that had greater than a 10% difference between the two shoulders did not have their deformity diagnosed. The radiological reports diagnosed 17% of these patients with a 'normal' shoulder. Only 5% of the reports were complete, accurately diagnosing SHEAR in addition to posterior subluxation.

Conclusion

Due to the low incidence rate of OBPI, many radiologists may be unfamiliar with the sequelae of these injuries. It is therefore critical that radiologists are made aware of the importance of an accurate measurement and diagnosis of the SHEAR deformity. Due to their lack of completeness, the radiological reports in this study did not significantly contribute to the clinical care of the patients. In order for OBPI patients to receive the highest standard of care, the final diagnosis from their radiological imaging should be deferred to a brachial plexus specialist who is experienced with these types of injuries.     

Simulation of scanner- and patient-specific low-dose CT imaging from existing CT images

PurposeSimulating low-dose Computed Tomography (CT) facilitates in-silico studies into the required dose for a diagnostic task. Conventionally, low-dose CT images are created by adding noise to the projection data. However, in practice the raw data is often simply not available. This paper presents a new method for simulating patient-specific, low-dose CT images without the need of the original projection data.MethodsThe low-dose CT simulation method included the following: (1) computation of a virtual sinogram from a high dose CT image through a radon transform; (2) simulation of a ‘reduced’-dose sinogram with appropriate amounts of noise; (3) subtraction of the high-dose virtual sinogram from the reduced-dose sinogram; (4) reconstruction of a noise volume via filtered back-projection; (5) addition of the noise image to the original high-dose image. The required scanner-specific parameters, such as the apodization window, bowtie filter, the X-ray tube output parameter (reflecting the photon flux) and the detector read-out noise, were retrieved from calibration images of a water cylinder. The low-dose simulation method was evaluated by comparing the noise characteristics in simulated images with experimentally acquired data.ResultsThe models used to recover the scanner-specific parameters fitted accurately to the calibration data, and the values of the parameters were comparable to values reported in literature. Finally, the simulated low-dose images accurately reproduced the noise characteristics in experimentally acquired low-dose-volumes.ConclusionThe developed methods truthfully simulate low-dose CT imaging for a specific scanner and reconstruction using filtered backprojection. The scanner-specific parameters can be estimated from calibration data.     

A novel method for high accuracy sumoylation site prediction from protein sequences

Background

The use of novel algorithmic techniques is pivotal to many important problems in life science. For example the sequencing of the human genome [1] would not have been possible without advanced assembly algorithms. However, owing to the high speed of technological progress and the urgent need for bioinformatics tools, there is a widening gap between state-of-the-art algorithmic techniques and the actual algorithmic components of tools that are in widespread use.

Results

To remedy this trend we propose the use of SeqAn, a library of efficient data types and algorithms for sequence analysis in computational biology. SeqAn comprises implementations of existing, practical state-of-the-art algorithmic components to provide a sound basis for algorithm testing and development. In this paper we describe the design and content of SeqAn and demonstrate its use by giving two examples. In the first example we show an application of SeqAn as an experimental platform by comparing different exact string matching algorithms. The second example is a simple version of the well-known MUMmer tool rewritten in SeqAn. Results indicate that our implementation is very efficient and versatile to use.

Conclusion

We anticipate that SeqAn greatly simplifies the rapid development of new bioinformatics tools by providing a collection of readily usable, well-designed algorithmic components which are fundamental for the field of sequence analysis. This leverages not only the implementation of new algorithms, but also enables a sound analysis and comparison of existing algorithms.     

The use of PET/CT scanning technique for 3D visualization and quantification of real-time soil/plant interactions

Aims

Conventional methodology using destructive sampling, which is laborious and has poor spatial and temporal resolution, has limited our understanding of soil-plant interactions. New non-invasive tomographic techniques have the potential to significantly improve our knowledge. In this study we demonstrated the simultaneous use of PET (positron emission tomography) and CT (X-ray computed tomography) to (a) non-destructively image a whole plant growing in sand, and (b) to link the observed morphology with recently assimilated C. The PET scanner was used to detect and visualize the location of the short-lived radioisotope ¹¹C (with a half-life of 20.4?min) taken up by the plant through ¹¹C-labelled CO₂. This provided information on carbon translocation and the metabolism of photo-assimilates in the plant as well as root structure. The CT scanners yielded data on soil and root structure.

Methods

A medical PET/CT scanner was used to scan a fodder radish plant growing in a pot with test soil composed of homogenous sand. We constructed an air-plant-soil controller system (APS) to control the environmental conditions, such as CO₂, temperature and light during the experiment. The plant was allowed to assimilate ¹¹CO₂ for 90?min before PET scanning was initiated. We carried out PET scanning for 60?min. Subsequently, the aerial parts of the plant was cut off and the pot was rescanned using a micro-CT scanner to obtain more detailed information on structure of the root system and the growth medium structure.

Results

The acquired PET and CT images gave images clearly visualizing the architecture and morphology of root and soil. Using a CT scanner, we were able to detect the main taproot located at 0 to 30?mm depth. With the PET scanner, we were able to measure a signal down to 82?mm below the surface of the sand. We found the highest concentration of ¹¹C at the position of the main root. The PET images, at different time intervals, showed the translocation and metabolisation of photo-assimilates from top to root. Using the micro-CT scanner (voxel size of 90?μm), we were able to detect roots down to 100?mm depth. These findings correlated the PET signals measured down to 82?mm depth.

Conclusions

We conclude that the simultaneous use of PET and CT technologies was successfully applied for soil-plant studies. The combined PET/CT technology has potential to provide new fundamental insight into soil-plant interactions and especially into the effect of abiotic stresses in spite of the limitation due to spatial resolution.     

Respiratory Motion Reduction in PET/CT Using Abdominal Compression for Lung Cancer Patients

Purpose

Respiratory motion causes substantial artifacts in reconstructed PET images when using helical CT as the attenuation map in PET/CT imaging. In this study, we aimed to reduce the respiratory artifacts in PET/CT images of patients with lung tumors using an abdominal compression device.

Methods

Twelve patients with lung cancer located in the middle or lower lobe of the lung were recruited. The patients were injected with 370 MBq of ¹⁸F-FDG. During PET, the patients assumed two bed positions for 1.5 min/bed. After conducting free-breathing imaging, we obtained images of the patients with abdominal compression by applying the same setup used in the free-breathing scan. The differences in the standardized uptake value (SUV)_max, SUV_mean, tumor volume, and the centroid of the tumors between PET and various CT schemes were measured.

Results

The SUV_max and SUV_mean derived from PET/CT imaging using an abdominal compression device increased for all the lesions, compared with those obtained using the conventional approach. The percentage increases were 18.1% ±14% and 17% ±16.8% for SUV_max and SUV_mean, respectively. PET/CT imaging combined with abdominal compression generally reduced the tumor mismatch between CT and the corresponding attenuation corrected PET images, with an average decrease of 1.9±1.7 mm over all the cases.

Conclusions

PET/CT imaging combined with abdominal compression reduces respiratory artifacts and PET/CT misregistration, and enhances quantitative SUV in tumor. Abdominal compression is easy to set up and is an effective method used in PET/CT imaging for clinical oncology, especially in the thoracic region.