Evaluation of tumor localization in respiration motion-corrected cone-beam CT: prospective study in lung.

PURPOSE: Target localization accuracy of cone-beam CT (CBCT) images used in radiation treatment of respiratory disease sites is affected by motion artifacts (blurring and streaking). The authors have previously reported on a method of respiratory motion correction in thoracic CBCT at end expiration (EE). The previous retrospective study was limited to examination of reducing motion artifacts in a small number of patient cases. They report here on a prospective study in a larger group of lung cancer patients to evaluate respiratory motion-corrected (RMC)-CBCT ability to improve lung tumor localization accuracy and reduce motion artifacts in Linac-mounted CBCT images. A second study goal examines whether the motion correction derived from a respiration-correlated CT (RCCT) at simulation yields similar tumor localization accuracy at treatment. METHODS: In an IRB-approved study, 19 lung cancer patients (22 tumors) received a RCCT at simulation, and on one treatment day received a RCCT, a respiratory-gated CBCT at end expiration, and a 1-min CBCT. A respiration monitor of abdominal displacement was used during all scans. In addition to a CBCT reconstruction without motion correction, the motion correction method was applied to the same 1-min scan. Projection images were sorted into ten bins based on abdominal displacement, and each bin was reconstructed to produce ten intermediate CBCT images. Each intermediate CBCT was deformed to the end expiration state using a motion model derived from RCCT. The deformed intermediate CBCT images were then added to produce a final RMC-CBCT. In order to evaluate the second study goal, the CBCT was corrected in two ways, one using a model derived from the RCCT at simulation [RMC-CBCT(sim)], the other from the RCCT at treatment [RMC-CBCT(tx)]. Image evaluation compared uncorrected CBCT, RMC-CBCT(sim), and RMC-CBCT(tx). The gated CBCT at end expiration served as the criterion standard for comparison. Using automatic rigid image registration, each CBCT was registered twice to the gated CBCT, first aligned to spine, second to tumor in lung. Localization discrepancy was defined as the difference between tumor and spine registration. Agreement in tumor localization with the gated CBCT was further evaluated by calculating a normalized cross correlation (NCC) of pixel intensities within a volume-of-interest enclosing the tumor in lung. RESULTS: Tumor localization discrepancy was reduced with RMC-CBCT(tx) in 17 out of 22 cases relative to no correction. If one considers cases in which tumor motion is 5 mm or more in the RCCT, tumor localization discrepancy is reduced with RMC-CBCT(tx) in 14 out of 17 cases (p = 0.04), and with RMC-CBCT(sim) in 13 out of 17 cases (p = 0.05). Differences in localization discrepancy between correction models [RMC-CBCT(sim) vs RMC-CBCT(tx)] were less than 2 mm. In 21 out of 22 cases, improvement in NCC was higher with RMC-CBCT(tx) relative to no correction (p < 0.0001). Differences in NCC between RMC-CBCT(sim) and RMC-CBCT(tx) were small. CONCLUSIONS: Motion-corrected CBCT improves lung tumor localization accuracy and reduces motion artifacts in nearly all cases. Motion correction at end expiration using RCCT acquired at simulation yields similar results to that using a RCCT on the treatment day (2-3 weeks after simulation).

Reduction of irregular breathing artifacts in respiration-correlated CT images using a respiratory motion model.

PURPOSE: Respiration-correlated CT (RCCT) images produced with commonly used phase-based sorting of CT slices often exhibit discontinuity artifacts between CT slices, caused by cycle-to-cycle amplitude variations in respiration. Sorting based on the displacement of the respiratory signal yields slices at more consistent respiratory motion states and hence reduces artifacts, but missing image data (gaps) may occur. The authors report on the application of a respiratory motion model to produce an RCCT image set with reduced artifacts and without missing data. METHODS: Input data consist of CT slices from a cine CT scan acquired while recording respiration by monitoring abdominal displacement. The model-based generation of RCCT images consists of four processing steps: (1) displacement-based sorting of CT slices to form volume images at 10 motion states over the cycle; (2) selection of a reference image without gaps and deformable registration between the reference image and each of the remaining images; (3) generation of the motion model by applying a principal component analysis to establish a relationship between displacement field and respiration signal at each motion state; (4) application of the motion model to deform the reference image into images at the 9 other motion states. Deformable image registration uses a modified fast free-form algorithm that excludes zero-intensity voxels, caused by missing data, from the image similarity term in the minimization function. In each iteration of the minimization, the displacement field in the gap regions is linearly interpolated from nearest neighbor nonzero intensity slices. Evaluation of the model-based RCCT examines three types of image sets: cine scans of a physical phantom programmed to move according to a patient respiratory signal, NURBS-based cardiac torso (NCAT) software phantom, and patient thoracic scans. RESULTS: Comparison in physical motion phantom shows that object distortion caused by variable motion amplitude in phase-based sorting is visibly reduced with model-based RCCT. Comparison of model-based RCCT to original NCAT images as ground truth shows best agreement at motion states whose displacement-sorted images have no missing slices, with mean and maximum discrepancies in lung of 1 and 3 mm, respectively. Larger discrepancies correlate with motion states having a larger number of missing slices in the displacement-sorted images. Artifacts in patient images at different motion states are also reduced. Comparison with displacement-sorted patient images as a ground truth shows that the model-based images closely reproduce the ground truth geometry at different motion states. CONCLUSIONS: Results in phantom and patient images indicate that the proposed method can produce RCCT image sets with reduced artifacts relative to phase-sorted images, without the gaps inherent in displacement-sorted images. The method requires a reference image at one motion state that has no missing data. Highly irregular breathing patterns can affect the method's performance, by introducing artifacts in the reference image (although reduced relative to phase-sorted images), or in decreased accuracy in the image prediction of motion states containing large regions of missing data.

Automation of Hessian-Based Tubularity Measure Response Function in 3D Biomedical Images.

The blood vessels and nerve trees consist of tubular objects interconnected into a complex tree- or web-like structure that has a range of structural scale 5 µm diameter capillaries to 3 cm aorta. This large-scale range presents two major problems; one is just making the measurements, and the other is the exponential increase of component numbers with decreasing scale. With the remarkable increase in the volume imaged by, and resolution of, modern day 3D imagers, it is almost impossible to make manual tracking of the complex multiscale parameters from those large image data sets. In addition, the manual tracking is quite subjective and unreliable. We propose a solution for automation of an adaptive nonsupervised system for tracking tubular objects based on multiscale framework and use of Hessian-based object shape detector incorporating National Library of Medicine Insight Segmentation and Registration Toolkit (ITK) image processing libraries.

Dual-energy CT iodine-subtraction virtual unenhanced technique to detect urinary stones in an iodine-filled collecting system: a phantom study.

OBJECTIVE: The objective of our study was to evaluate the feasibility of virtual unenhanced images reconstructed from a dual-energy CT scan to depict urinary stones in an iodine solution in a phantom study. MATERIALS AND METHODS: Twenty urinary stones of different sizes (1.4-4.2 mm in short-axis diameter) were placed in plastic containers. The containers were consecutively filled with different concentrations of iodine solution (21, 43, 64, 85, and 107 mg/dL; CT attenuation value range, 510-2,310 H at 120 kVp). Dual-energy CT was repeated with 80-140 and 100-140 kVp pairs, two collimation-slice thickness combinations, and the presence or absence of a 4-cm-thick oil gel around the phantom. The iodine-subtraction virtual unenhanced images were reconstructed using commercial software. The images were evaluated by three radiologists in consensus for the visibility of the stones and the presence of residual nonsubtracted iodine. Stone visibility rates were compared between the 80-140 and 100-140 kVp pairs and the five different iodine concentrations. RESULTS: Stone visibility rates with the 80-140 kVp pair were 99%, 93%, 96%, 94%, and 3% and those with the 100-140 kVp pair were 98%, 95%, 99%, 94%, and 99% for an iodine concentration of 21, 43, 64, 85, and 107 mg/dL, respectively. The poor visibility rate with 80-140 kVp and 107 mg/dL iodine concentration was due to the failure of iodine subtraction. CONCLUSION: Dual-energy CT iodine-subtraction virtual unenhanced technique is capable of depicting urinary stones in iodine solutions of a diverse range of concentrations in a phantom study.

Noninvasive differentiation of uric acid versus non-uric acid kidney stones using dual-energy CT.

RATIONALE AND OBJECTIVES: To determine the accuracy and sensitivity for dual-energy computed tomography (DECT) discrimination of uric acid (UA) stones from other (non-UA) renal stones in a commercially implemented product. MATERIALS AND METHODS: Forty human renal stones comprising uric acid (n=16), hydroxyapatite (n=8), calcium oxalate (n=8), and cystine (n=8) were inserted in four porcine kidneys (10 each) and placed inside a 32-cm water tank anterior to a cadaver spine. Spiral dual-energy scans were obtained on a dual-source, 64-slice computed tomography (CT) system using a clinical protocol and automatic exposure control. Scanning was performed at two different collimations (0.6 mm and 1.2 mm) and within three phantom sizes (medium, large, and extra large) resulting in a total of six image datasets. These datasets were analyzed using the dual-energy software tool available on the CT system for both accuracy (number of stones correctly classified as either UA or non-UA) and sensitivity (for UA stones). Stone characterization was correlated with micro-CT. RESULTS: For the medium and large phantom sizes, the DECT technique demonstrated 100% accuracy (40/40), regardless of collimation. For the extra large phantom size and the 0.6-mm collimation (resulting in the noisiest dataset), three (two cystine and one small UA) stones could not be classified (93% accuracy and 94% sensitivity). For the extra large phantom size and the 1.2-mm collimation, the dual-energy tool failed to identify two small UA stones (95% accuracy and 88% sensitivity). CONCLUSIONS: In an anthropomorphic phantom model, dual-energy CT can accurately discriminate uric acid stones from other stone types.