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Background: Biomarkers estimating local lung ventilation have been derived from computed tomography (CT) imaging using various image acquisition and post‐processing techniques. CT‐ventilation biomarkers have potential clinical use in functional avoidance radiation therapy (RT), in which RT treatment plans are optimized to reduce dose delivered to highly ventilated lung. Widespread clinical implementation of CT‐ventilation biomarkers necessitates understanding of biomarker repeatability. Performing imaging within a highly controlled experimental design enables quantification of error associated with remaining variables. Purpose: To characterize CT‐ventilation biomarker repeatability and dependence on image acquisition and post‐processing methodology in anesthetized and mechanically ventilated pigs. Methods: Five mechanically ventilated Wisconsin Miniature Swine (WMS) received multiple consecutive four‐dimensional CT (4DCT) and maximum inhale and exhale breath‐hold CT (BH‐CT) scans on five dates to generate CT‐ventilation biomarkers. Breathing maneuvers were controlled with an average tidal volume difference <200 cc. As surrogates for ventilation, multiple local expansion ratios (LERs) were calculated from the acquired CT scans using Jacobian‐based post‐processing techniques. LER2$LER_2$ measured local expansion between an image pair using either inhale and exhale BH‐CT images or two 4DCT breathing phase images. LERN$LER_N$ measured the maximum local expansion across the 4DCT breathing phase images. Breathing maneuver consistency, intra‐ and interday biomarker repeatability, image acquisition and post‐processing technique dependence were quantitatively analyzed. Results: Biomarkers showed strong agreement with voxel‐wise Spearman correlation ρ>0.9$\rho > 0.9$ for intraday repeatability and ρ>0.8$\rho > 0.8$ for all other comparisons, including between image acquisition techniques. Intra‐ and interday repeatability were significantly different (p < 0.01). LER2 and LERN post‐processing did not significantly affect intraday repeatability. Conclusions: 4DCT and BH‐CT ventilation biomarkers derived from consecutive scans show strong agreement in controlled experiments with nonhuman subjects. [ABSTRACT FROM AUTHOR]

Background: Chest computed tomography (CT) enables characterization of pulmonary diseases by producing high‐resolution and high‐contrast images of the intricate lung structures. Deformable image registration is used to align chest CT scans at different lung volumes, yielding estimates of local tissue expansion and contraction. Purpose: We investigated the utility of deep generative models for directly predicting local tissue volume change from lung CT images, bypassing computationally expensive iterative image registration and providing a method that can be utilized in scenarios where either one or two CT scans are available. Methods: A residual regression convolutional neural network, called Reg3DNet+, is proposed for directly regressing high‐resolution images of local tissue volume change (i.e., Jacobian) from CT images. Image registration was performed between lung volumes at total lung capacity (TLC) and functional residual capacity (FRC) using a tissue mass‐ and structure‐preserving registration algorithm. The Jacobian image was calculated from the registration‐derived displacement field and used as the ground truth for local tissue volume change. Four separate Reg3DNet+ models were trained to predict Jacobian images using a multifactorial study design to compare the effects of network input (i.e., single image vs. paired images) and output space (i.e., FRC vs. TLC). The models were trained and evaluated on image datasets from the COPDGene study. Models were evaluated against the registration‐derived Jacobian images using local, regional, and global evaluation metrics. Results: Statistical analysis revealed that both factors – network input and output space – were significant determinants for change in evaluation metrics. Paired‐input models performed better than single‐input models, and model performance was better in the output space of FRC rather than TLC. Mean structural similarity index for paired‐input models was 0.959 and 0.956 for FRC and TLC output spaces, respectively, and for single‐input models was 0.951 and 0.937. Global evaluation metrics demonstrated correlation between registration‐derived Jacobian mean and predicted Jacobian mean: coefficient of determination (r2) for paired‐input models was 0.974 and 0.938 for FRC and TLC output spaces, respectively, and for single‐input models was 0.598 and 0.346. After correcting for effort, registration‐derived lobar volume change was strongly correlated with the predicted lobar volume change: for paired‐input models r2 was 0.899 for both FRC and TLC output spaces, and for single‐input models r2 was 0.803 and 0.862, respectively. Conclusions: Convolutional neural networks can be used to directly predict local tissue mechanics, eliminating the need for computationally expensive image registration. Networks that use paired CT images acquired at TLC and FRC allow for more accurate prediction of local tissue expansion compared to networks that use a single image. Networks that only require a single input image still show promising results, particularly after correcting for effort, and allow for local tissue expansion estimation in cases where multiple CT scans are not available. For single‐input networks, the FRC image is more predictive of local tissue volume change compared to the TLC image. [ABSTRACT FROM AUTHOR]

Background: Functional lung avoidance radiation therapy (RT) is a technique being investigated to preferentially avoid specific regions of the lung that are predicted to be more susceptible to radiation‐induced damage. Reducing the dose delivered to high functioning regions may reduce the occurrence radiation‐induced lung injuries (RILIs) and toxicities. However, in order to develop effective lung function‐sparing plans, accurate predictions of post‐RT ventilation change are needed to determine which regions of the lung should be spared. Purpose: To predict pulmonary ventilation change following RT for nonsmall cell lung cancer using machine learning. Methods: A conditional generative adversarial network (cGAN) was developed with data from 82 human subjects enrolled in a randomized clinical trial approved by the institution's IRB to predict post‐RT pulmonary ventilation change. The inputs to the network were the pre‐RT pulmonary ventilation map and radiation dose distribution. The loss function was a combination of the binary cross‐entropy loss and an asymmetrical structural similarity index measure (aSSIM) function designed to increase penalization of under‐prediction of ventilation damage. Network performance was evaluated against a previously developed polynomial regression model using a paired sample t‐test for comparison. Evaluation was performed using eight‐fold cross‐validation. Results: From the eight‐fold cross‐validation, we found that relative to the polynomial model, the cGAN model significantly improved predicting regions of ventilation damage following radiotherapy based on true positive rate (TPR), 0.14±0.15 to 0.72±0.21, and Dice similarity coefficient (DSC), 0.19±0.16 to 0.46±0.14, but significantly declined in true negative rate, 0.97±0.05 to 0.62±0.21, and accuracy, 0.79±0.08 to 0.65±0.14. Additionally, the average true positive volume increased from 104±119 cc in the POLY model to 565±332 cc in the cGAN model, and the average false negative volume decreased from 654±361 cc in the POLY model to 193±163 cc in the cGAN model. Conclusions: The proposed cGAN model demonstrated significant improvement in TPR and DSC. The higher sensitivity of the cGAN model can improve the clinical utility of functional lung avoidance RT by identifying larger volumes of functional lung that can be spared and thus decrease the probability of the patient developing RILIs. [ABSTRACT FROM AUTHOR]

Purpose: To create a dose‐response model that predicts lung ventilation change following radiation therapy, and examine the effects of out‐of‐phase ventilation. Methods: The dose‐response model was built using 27 human subjects who underwent radiation therapy (RT) from an IRB‐approved trial. For each four‐dimensional computed tomography, two ventilation maps were created by calculating the N‐phase local expansion ratio (LERN) using most or all breathing phases and the 2‐phase LER (LER2) using only the end inspiration and end expiration breathing phases. A polynomial regression model was created using the LERN ventilation maps pre‐RT and post‐RT and dose distributions for each subject, and crossvalidated with a leave‐one‐out method. Further validation of the model was performed using 15 additional human subjects using common statistical operating characteristics and gamma pass rates. Results: For voxels receiving 20 Gy or greater, there was a significant increase from 52% to 59% (P = 0.03) in the gamma pass rates of the LERN model predicted post‐RT Jacobian maps to the actual post‐RT Jacobian maps, relative to the LER2 model. Additionally, accuracy significantly increased (P = 0.03) from 68% to 75% using the LERN model, relative to the LER2 model. Conclusions: The LERN model was significantly more accurate than the LER2 model at predicting post‐RT ventilation maps. More accurate post‐RT ventilation maps will aid in producing a higher quality functional avoidance treatment plan, allowing for potentially better normal tissue sparing. [ABSTRACT FROM AUTHOR]

Purpose: CT ventilation imaging (CTVI) is being used to achieve functional avoidance lung cancer radiation therapy in three clinical trials (NCT02528942, NCT02308709, NCT02843568). To address the need for common CTVI validation tools, we have built the Ventilation And Medical Pulmonary Image Registration Evaluation (VAMPIRE) Dataset, and present the results of the first VAMPIRE Challenge to compare relative ventilation distributions between different CTVI algorithms and other established ventilation imaging modalities. Methods: The VAMPIRE Dataset includes 50 pairs of 4DCT scans and corresponding clinical or experimental ventilation scans, referred to as reference ventilation images (RefVIs). The dataset includes 25 humans imaged with Galligas 4DPET/CT, 21 humans imaged with DTPA‐SPECT, and 4 sheep imaged with Xenon‐CT. For the VAMPIRE Challenge, 16 subjects were allocated to a training group (with RefVI provided) and 34 subjects were allocated to a validation group (with RefVI blinded). Seven research groups downloaded the Challenge dataset and uploaded CTVIs based on deformable image registration (DIR) between the 4DCT inhale/exhale phases. Participants used DIR methods broadly classified into B‐splines, Free‐form, Diffeomorphisms, or Biomechanical modeling, with CT ventilation metrics based on the DIR evaluation of volume change, Hounsfield Unit change, or various hybrid approaches. All CTVIs were evaluated against the corresponding RefVI using the voxel‐wise Spearman coefficient rS, and Dice similarity coefficients evaluated for low function lung (DSClow) and high function lung (DSChigh). Results: A total of 37 unique combinations of DIR method and CT ventilation metric were either submitted by participants directly or derived from participant‐submitted DIR motion fields using the in‐house software, VESPIR. The rS and DSC results reveal a high degree of inter‐algorithm and intersubject variability among the validation subjects, with algorithm rankings changing by up to ten positions depending on the choice of evaluation metric. The algorithm with the highest overall cross‐modality correlations used a biomechanical model‐based DIR with a hybrid ventilation metric, achieving a median (range) of 0.49 (0.27–0.73) for rS, 0.52 (0.36–0.67) for DSClow, and 0.45 (0.28–0.62) for DSChigh. All other algorithms exhibited at least one negative rS value, and/or one DSC value less than 0.5. Conclusions: The VAMPIRE Challenge results demonstrate that the cross‐modality correlation between CTVIs and the RefVIs varies not only with the choice of CTVI algorithm but also with the choice of RefVI modality, imaging subject, and the evaluation metric used to compare relative ventilation distributions. This variability may arise from the fact that each of the different CTVI algorithms and RefVI modalities provides a distinct physiologic measurement. Ultimately this variability, coupled with the lack of a "gold standard," highlights the ongoing importance of further validation studies before CTVI can be widely translated from academic centers to the clinic. It is hoped that the information gleaned from the VAMPIRE Challenge can help inform future validation efforts. [ABSTRACT FROM AUTHOR]

Purpose: Regional ventilation and its response to radiation dose can be estimated using four‐dimensional computed tomography (4DCT) and image registration. This study investigated the impact of radiation therapy (RT) on ventilation and the dependence of radiation‐induced ventilation change on pre‐RT ventilation derived from 4DCT. Methods and materials: Three 4DCT scans were acquired from each of 12 subjects: two scans before RT and one scan 3 months after RT. The 4DCT datasets were used to generate the pre‐RT and post‐RT ventilation maps by registering the inhale phase image to the exhale phase image and computing the Jacobian determinant of the resulting transformation. The ventilation change between pre‐RT and post‐RT was calculated by taking a ratio of the post‐RT Jacobian map to the pre‐RT Jacobian map. The voxel‐wise ventilation change between pre‐ and post‐RT was investigated as a function of dose and pre‐RT ventilation. Results: Lung regions receiving over 20 Gy exhibited a significant decrease in function (3.3%, P < 0.01) compared to those receiving less than 20 Gy. When the voxels were stratified into high and low pre‐RT function by thresholding the Jacobian map at 10% volume expansion (Jacobian = 1.1), high‐function voxels exhibited 4.8% reduction in function for voxels receiving over 20 Gy, a significantly greater decline (P = 0.037) than the 2.4% reduction in function for low‐function voxels. Ventilation decreased linearly with dose in both high‐function and low‐function regions. High‐function regions showed a significantly larger decline in ventilation (P ≪ 0.001) as dose increased (1.4% ventilation reduction/10 Gy) compared to low‐function regions (0.3% ventilation reduction/10 Gy). With further stratification of pre‐RT ventilation, voxels exhibited increasing dose‐dependent ventilation reduction with increasing pre‐RT ventilation, with the largest pre‐RT Jacobian bin (pre‐RT Jacobian between 1.5 and 1.6) exhibiting a ventilation reduction of 4.8% per 10 Gy. Conclusions: Significant ventilation reductions were measured after radiation therapy treatments, and were dependent on the dose delivered to the tissue and the pre‐RT ventilation of the tissue. For a fixed radiation dose, lung tissue with high pre‐RT ventilation experienced larger decreases in post‐RT ventilation than lung tissue with low pre‐RT ventilation. [ABSTRACT FROM AUTHOR]

Objectives/Hypothesis: To develop a growth model of the minimum cross-sectional area of the normal pediatric trachea with measurements from magnetic resonance images (MRIs) to supplement the clinical criteria used to determine if a child with tracheal stenosis needs surgery. Study Design: Retrospective imaging review. Methods: A total of 81 patients were imaged for a variety of clinical reasons and declared to have normal tracheas fully visible in their T1 magnetic resonance image. Regression analysis was used to identify any contribution that age, gender, and z scores for height and weight have in predicting the minimum cross-sectional area of the trachea. Results: The best-fit model for minimum cross-sectional area is: Area=-0.00451*age4+0.177*age³-2.05*age²+12.6*age 18.02 (area in mm² and age in years). Gender and z scores for height and weight did not provide any additional explanation of variance in tracheal size. Conclusions: Our study demonstrates the potential to create a growth model of the normal trachea based on crosssectional area of the trachea using MRIs. Even with the relatively small number of patients used to build it, the model has demonstrated some ability to be used as an objective prediction tool when deciding a treatment path for a patient. With continued development of precise, objective measures to diagnose the severity of the tracheal stenosis, more patients can be given early and accurate prognosis and be treated appropriately. [ABSTRACT FROM AUTHOR]

Purpose: Four-dimensional computed tomography (4DCT) can be used to make measurements of pulmonary function longitudinally. The sensitivity of such measurements to identify change depends on measurement uncertainty. Previously, intrasubject reproducibility of Jacobian-based measures of lung tissue expansion was studied in two repeat prior-RT 4DCT human acquisitions. Difference in respiratory effort such as breathing amplitude and frequency may affect longitudinal function assessment. In this study, the authors present normalization schemes that correct ventilation images for variations in respiratory effort and assess the reproducibility improvement after effort correction. Methods: Repeat 4DCT image data acquired within a short time interval from 24 patients prior to radiation therapy (RT) were used for this analysis. Using a tissue volume preserving deformable image registration algorithm, Jacobian ventilation maps in two scanning sessions were computed and compared on the same coordinate for reproducibility analysis. In addition to computing the ventilation maps from end expiration to end inspiration, the authors investigated the effort normalization strategies using other intermediated inspiration phases upon the principles of equivalent tidal volume (ETV) and equivalent lung volume (ELV). Scatter plots and mean square error of the repeat ventilation maps and the Jacobian ratio map were generated for four conditions: no effort correction, global normalization, ETV, and ELV. In addition, gamma pass rate was calculated from a modified gamma index evaluation between two ventilation maps, using acceptance criterions of 2 mm distance-to-agreement and 5% ventilation difference. Results: The pattern of regional pulmonary ventilation changes as lung volume changes. All effort correction strategies improved reproducibility when changes in respiratory effort were greater than 150 cc (p < 0.005 with regard to the gamma pass rate). Improvement of reproducibility was correlated with respiratory effort difference (R = 0.744 for ELV in the cohort with tidal volume difference greater than 100 cc). In general for all subjects, global normalization, ETV and ELV significantly improved reproducibility compared to no effort correction (p = 0.009, 0.002, 0.005 respectively). When tidal volume difference was small (less than 100 cc), none of the three effort correction strategies improved reproducibility significantly (p = 0.52, 0.46, 0.46 respectively). For the cohort (N = 13) with tidal volume difference greater than 100 cc, the average gamma pass rate improves from 57.3% before correction to 66.3% after global normalization, and 76.3% after ELV. ELV was found to be significantly better than global normalization (p = 0.04 for all subjects, and p = 0.003 for the cohort with tidal volume difference greater than 100 cc). Conclusions: All effort correction strategies improve the reproducibility of the authors' pulmonary ventilation measures, and the improvement of reproducibility is highly correlated with the changes in respiratory effort. ELV gives better results as effort difference increase, followed by ETV, then global. However, based on the spatial and temporal heterogeneity in the lung expansion rate, a single scaling factor (e.g., global normalization) appears to be less accurate to correct the ventilation map when changes in respiratory effort are large. [ABSTRACT FROM AUTHOR]

Purpose: Lung function depends on lung expansion and contraction during the respiratory cycle. Respiratory-gated CT imaging and image registration can be used to estimate the regional lung volume change by observing CT voxel density changes during inspiration or expiration. In this study, the authors examine the reproducibility of intensity-based estimates of lung tissue expansion and contraction in three mechanically ventilated sheep and ten spontaneously breathing humans. The intensity-based estimates are compared to the estimates of lung function derived from image registration deformation field. Methods: 4DCT data set was acquired for a cohort of spontaneously breathing humans and anesthetized and mechanically ventilated sheep. For each subject, two 4DCT scans were performed with a short time interval between acquisitions. From each 4DCT data set, an image pair consisting of a volume reconstructed near end inspiration and a volume reconstructed near end exhalation was selected. The end inspiration and end exhalation images were registered using a tissue volume preserving deformable registration algorithm. The CT density change in the registered image pair was used to compute intensity-based specific air volume change (SAC) and the intensity-based Jacobian (IJAC), while the transformation-based Jacobian (TJAC) was computed directly from the image registration deformation field. IJAC is introduced to make the intensity-based and transformation-based methods comparable since SAC and Jacobian may not be associated with the same physiological phenomenon and have different units. Scan-to-scan variations in respiratory effort were corrected using a global scaling factor for normalization. A gamma index metric was introduced to quantify voxel-by-voxel reproducibility considering both differences in ventilation and distance between matching voxels. The authors also tested how different CT prefiltering levels affected intensity-based ventilation reproducibility. Results: Higher reproducibility was found for anesthetized mechanically ventilated animals than for the humans for both the intensity-based (IJAC) and transformation-based (TJAC) ventilation estimates. The human IJAC maps had scan-to-scan correlation coefficients of 0.45 ± 0.14, a gamma pass rate 70 ± 8 without normalization and 75 ± 5 with normalization. The human TJAC maps had correlation coefficients 0.81 ± 0.10, a gamma pass rate 86 ± 11 without normalization and 93 ± 4 with normalization. The gamma pass rate and correlation coefficient of the IJAC maps gradually increased with increased smoothing, but were still much lower than those of the TJAC maps. Conclusions: The transformation-based ventilation maps show better reproducibility than the intensity-based maps, especially in human subjects. Reproducibility was also found to depend on variations in respiratory effort; all techniques were better when applied to images from mechanically ventilated sheep compared to spontaneously breathing human subjects. Nevertheless, intensity-based techniques applied to mechanically ventilated sheep were less reproducible than the transformation-based applied to spontaneously breathing humans, suggesting the method used to determine ventilation maps is important. Prefiltering of the CT images may help to improve the reproducibility of the intensity-based ventilation estimates, but even with filtering the reproducibility of the intensity-based ventilation estimates is not as good as that of transformation-based ventilation estimates. [ABSTRACT FROM AUTHOR]

Purpose: Regional lung volume change as a function of lung inflation serves as an index of parenchymal and airway status as well as an index of regional ventilation and can be used to detect pathologic changes over time. In this paper, the authors propose a new regional measure of lung mechanics-the specific air volume change by corrected Jacobian. The authors compare this new measure, along with two existing registration based measures of lung ventilation, to a regional ventilation measurement derived from xenon-CT (Xe-CT) imaging. Methods: 4DCT and Xe-CT datasets from four adult sheep are used in this study. Nonlinear, 3D image registration is applied to register an image acquired near end inspiration to an image acquired near end expiration. Approximately 200 annotated anatomical points are used as landmarks to evaluate registration accuracy. Three different registration based measures of regional lung mechanics are derived and compared: the specific air volume change calculated from the Jacobian (SAJ); the specific air volume change calculated by the corrected Jacobian (SACJ); and the specific air volume change by intensity change (SAI). The authors show that the commonly used SAI measure can be derived from the direct SAJ measure by using the air-tissue mixture model and assuming there is no tissue volume change between the end inspiration and end expiration datasets. All three ventilation measures are evaluated by comparing to Xe-CT estimates of regional ventilation. Results: After registration, the mean registration error is on the order of 1 mm. For cubical regions of interest (ROIs) in cubes with size 20 mm × 20 mm × 20 mm, the SAJ and SACJ measures show significantly higher correlation (linear regression, average r2 = 0.75 and r2 = 0.82) with the Xe-CT based measure of specific ventilation (sV) than the SAI measure. For ROIs in slabs along the ventral-dorsal vertical direction with size of 150 mm × 8 mm × 40 mm, the SAJ, SACJ, and SAI all show high correlation (linear regression, average r2 = 0.88, r2 = 0.92, and r2 = 0.87) with the Xe-CT based sV without significant differences when comparing between the three methods. The authors demonstrate a linear relationship between the difference of specific air volume change and difference of tissue volume in all four animals (linear regression, average r2 = 0.86). Conclusions: Given a deformation field by an image registration algorithm, significant differences between the SAJ, SACJ, and SAI measures were found at a regional level compared to the Xe-CT sV in four sheep that were studied. The SACJ introduced here, provides better correlations with Xe-CT based sV than the SAJ and SAI measures, thus providing an improved surrogate for regional ventilation. [ABSTRACT FROM AUTHOR]

Purpose: Lung function depends on lung expansion and contraction during the respiratory cycle. Respiratory-gated CT imaging and 3D image registration can be used to locally estimate lung tissue expansion and contraction (regional lung volume change) by computing the determinant of the Jacobian matrix of the image registration deformation field. In this study, the authors examine the reproducibility of Jacobian-based measures of lung tissue expansion in two repeat 4DCT acquisitions of mechanically ventilated sheep and free-breathing humans. Methods: 4DCT image data from three white sheep and nine human subjects were used for this analysis. In each case, two 4DCT studies were acquired for each subject within a short time interval. The animal subjects were anesthetized and mechanically ventilated, while the humans were awake and spontaneously breathing based on respiratory pacing audio cues. From each 4DCT data set, an image pair consisting of a volume reconstructed near end inspiration and a volume reconstructed near end exhalation was selected. The end inspiration and end exhalation images were registered using a tissue volume preserving deformable registration algorithm and the Jacobian of the registration deformation field was used to measure regional lung expansion. The Jacobian map from the baseline data set was compared to the Jacobian map from the followup data by measuring the voxel-by-voxel Jacobian ratio. Results: In the animal subjects, the mean Jacobian ratio (baseline scan Jacobian divided by followup scan Jacobian, voxel-by-voxel) was 0.9984±0.021 (mean ± standard deviation, averaged over the entire lung region). The mean Jacobian ratio was 1.0224±0.058 in the human subjects. The reproducibility of the Jacobian values was found to be strongly dependent on the reproducibility of the subject's respiratory effort and breathing pattern. Conclusions: Lung expansion, a surrogate for lung function, can be assessed using two or more respiratory-gated CT image acquisitions. The results show that good reproducibility can be obtained in anesthetized, mechanically ventilated animals, but variations in respiratory effort and breathing patterns reduce reproducibility in spontaneously-breathing humans. The global linear normalization can globally compensate for breathing effort differences, but a homogeneous scaling does not account for differences in regional lung expansion rates. Additional work is needed to develop compensation procedures or normalization schemes that can account for local variations in lung expansion during respiration. [ABSTRACT FROM AUTHOR]

Purpose: Radiation therapy (RT) for lung cancer is commonly limited to subtherapeutic doses due to unintended toxicity to normal lung tissue. Reducing the frequency of occurrence and magnitude of normal lung function loss may benefit from treatment plans that incorporate the regional lung and radiation dose information. In this article, the authors propose a method that quantitatively measures the regional changes in lung tissue function following a course of radiation therapy by using 4DCT and image registration techniques. Methods: 4DCT data sets before and after RT from two subjects are used in this study. Nonlinear 3D image registration is applied to register an image acquired near end inspiration to an image acquired near end expiration to estimate the pulmonary function. The Jacobian of the image registration transformation, indicating local lung expansion or contraction, serves as an index of regional pulmonary function. Approximately 120 annotated vascular bifurcation points are used as landmarks to evaluate registration accuracy. The authors compare regional pulmonary function before and after RT to the planned radiation dose at different locations of the lung. Results: In all registration pairs, the average landmark distances after registration are on the order of 1 mm. The pulmonary function change as indicated by the Jacobian change ranges from -0.15 to 0.1 in the contralateral lung and -0.22 to 0.23 in the ipsilateral lung for subject A, and ranges from -0.4 to 0.39 in the contralateral lung and -0.25 to 0.5 in the ipsilateral lung for subject B. Both of the subjects show larger range of the increase in the pulmonary function in the ipsilateral lung than the contralateral lung. For lung tissue regions receiving a radiation dose larger than 24 Gy, a decrease in pulmonary function was observed. For regions receiving a radiation dose smaller than 24 Gy, either an increase or a decrease in pulmonary function was observed. The relationship between the pulmonary function change and the radiation dose varies at different locations. Conclusions: With the use of 4DCT and image registration techniques, the pulmonary function prior to and following a course of radiation therapy can be measured. In the preliminary application of this approach for two subjects, changes in pulmonary function were observed with a weak correlation between the dose and pulmonary function change. In certain sections of the lung, detected locally compromised pulmonary function may have resulted from radiation injury. [ABSTRACT FROM AUTHOR]

Tracking lung tissues during the respiratory cycle has been a challenging task for diagnostic CT and CT-guided radiotherapy. We propose an intensity- and landmark-based image registration algorithm to perform image registration and warping of 3D pulmonary CT image data sets, based on consistency constraints and matching corresponding airway branchpoints. In this paper, we demonstrate the effectivenss and accuracy of this algorithm in tracking lung tissues by both animal and human data sets. In the animal study, the result showed a tracking accuracy of 1.9 mm between 50% functional residual capacity (FRC) and 85% total lung capacity (TLC) for 12 metal seeds implanted in the lungs of a breathing sheep under precise volume control using a pulmonary ventilator. Visual inspection of the human subject results revealed the algorithm’s potential not only in matching the global shapes, but also in registering the internal structures (e.g., oblique lobe fissures, pulmonary artery branches, etc.). These results suggest that our algorithm has significant potential for warping and tracking lung tissue deformation with applications in diagnostic CT, CT-guided radiotherapy treatment planning, and therapeutic effect evaluation. [ABSTRACT FROM AUTHOR]

Purpose To develop and test a computer-aided diagnosis (CAD) system to improve the performance of radiologists in classifying lesions on breast MRI (BMRI). Materials and Methods A CAD system was developed that uses a semiautomated segmentation method. After segmentation, 42 features based on lesion shape, texture, and enhancement kinetics were computed, and the 13 best features were selected and used as inputs to a backpropagation neural network (BNN). The BNN was trained and tested using the leave-one-out method on 80 BMRI lesions (37 benign, 43 malignant). Lesion histopathology was used as the reference standard. Five human readers classified the 80 lesions first without and then with CAD assistance. The performance of the computer classifier and the human readers was assessed using receiver operating characteristic curves; the performance of the human readers was also evaluated using multireader multicase (MRMC) analysis. Results The performance of the human readers significantly improved when aided by the CAD system ( P < 0.05). MRMC analysis showed that human reader performance with and without CAD system assistance can be generalized to the population of cases ( P < 0.001). Conclusion A CAD system based on lesion morphology and enhancement kinetics can improve the performance of human readers in classifying lesions on breast MRI. J. Magn. Reson. Imaging 2007. © 2006 Wiley-Liss, Inc. [ABSTRACT FROM AUTHOR]