A clinical and time savings evaluation of a deep learning automatic contouring algorithm.

Automatic contouring algorithms may streamline clinical workflows by reducing normal organ-at-risk (OAR) contouring time. Here we report the first comprehensive quantitative and qualitative evaluation, along with time savings assessment for a prototype deep learning segmentation algorithm from Siemens Healthineers. The accuracy of contours generated by the prototype were evaluated quantitatively using the Sorensen-Dice coefficient (Dice), Jaccard index (JC), and Hausdorff distance (Haus). Normal pelvic and head and neck OAR contours were evaluated retrospectively comparing the automatic and manual clinical contours in 100 patient cases. Contouring performance outliers were investigated. To quantify the time savings, a certified medical dosimetrist manually contoured de novo and, separately, edited the generated OARs for 10 head and neck and 10 pelvic patients. The automatic, edited, and manually generated contours were visually evaluated and scored by a practicing radiation oncologist on a scale of 1-4, where a higher score indicated better performance. The quantitative comparison revealed high (> 0.8) Dice and JC performance for relatively large organs such as the lungs, brain, femurs, and kidneys. Smaller elongated structures that had relatively low Dice and JC values tended to have low Hausdorff distances. Poor performing outlier cases revealed common anatomical inconsistencies including overestimation of the bladder and incorrect superior-inferior truncation of the spinal cord and femur contours. In all cases, editing contours was faster than manual contouring with an average time saving of 43.4% or 11.8 minutes per patient. The physician scored 240 structures with > 95% of structures receiving a score of 3 or 4. Of the structures reviewed, only 11 structures needed major revision or to be redone entirely. Our results indicate the evaluated auto-contouring solution has the potential to reduce clinical contouring time. The algorithm's performance is promising, but human review and some editing is required prior to clinical use.

Improving Structure Delineation for Radiation Therapy Planning Using Dual-Energy CT.

PURPOSE: We present the advantages of using dual-energy CT (DECT) for radiation therapy (RT) planning based on our clinical experience. METHODS: DECT data acquired for 20 representative patients of different tumor sites and/or clinical situations with dual-source simultaneous scanning (Drive, Siemens) and single-source sequential scanning (Definition, Siemens) using 80 and 140-kVp X-ray beams were analyzed. The data were used to derive iodine maps, fat maps, and mono-energetic images (MEIs) from 40 to 190 keV to exploit the energy dependence of X-ray attenuation. The advantages of using these DECT-derived images for RT planning were investigated. RESULTS: When comparing 40 keV MEIs to conventional 120-kVp CT, soft tissue contrast between the duodenum and pancreatic head was enhanced by a factor of 2.8. For a cholangiocarcinoma patient, contrast between tumor and surrounding tissue was increased by 96 HU and contrast-to-noise ratio was increased by up to 60% for 40 keV MEIs compared to conventional CT. Simultaneous dual-source DECT also preserved spatial resolution in comparison to sequential DECT as evidenced by the identification of vasculature in a pancreas patient. Volume of artifacts for five patients with titanium implants was reduced by over 95% for 190 keV MEIs compared to 120-kVp CT images. A 367-cm3 region of photon starvation was identified by low CT numbers in the soft tissue of a mantle patient in a conventional CT scan but was eliminated in a 190 keV MEI. Fat maps enhanced image contrast as demonstrated by a meningioma patient. CONCLUSION: The use of DECT for RT simulation offers clinically meaningful advantages through improved simulation workflow and enhanced structure delineation for RT planning.

An image-based deep learning framework for individualizing radiotherapy dose.

Background: Radiotherapy continues to be delivered uniformly without consideration of individual tumor characteristics. To advance toward more precise treatments in radiotherapy, we queried the lung computed tomography (CT)-derived feature space to identify radiation sensitivity parameters that can predict treatment failure and hence guide the individualization of radiotherapy dose. Methods: We used a cohort-based registry of 849 patients with cancer in the lung treated with high dose radiotherapy using stereotactic body radiotherapy. We input pre-therapy lung CT images into a multi-task deep neural network, Deep Profiler, to generate an image fingerprint that primarily predicts time to event treatment outcomes and secondarily approximates classical radiomic features. We validated our findings in an independent study population (n = 95). Deep Profiler was combined with clinical variables to derive iGray, an individualized dose that estimates treatment failure probability to be <5%. Findings: Radiation treatments in patients with high Deep Profiler scores fail at a significantly higher rate than in those with low scores. The 3-year cumulative incidences of local failure were 20.3% (95% CI: 16.0-24.9) and 5.7% (95% CI: 3.5-8.8), respectively. Deep Profiler independently predicted local failure (hazard ratio 1.65, 95% 1.02-2.66, p = 0.04). Models that included Deep Profiler and clinical variables predicted treatment failures with a concordance index of 0.72 (95% CI: 0.67-0.77), a significant improvement compared to classical radiomics or clinical variables alone (p = <0.001 and <0.001, respectively). Deep Profiler performed well in an external study population (n = 95), accurately predicting treatment failures across diverse clinical settings and CT scanner types (concordance index = 0.77 [95% CI: 0.69-0.92]). iGray had a wide dose range (21.1-277 Gy, BED), suggested dose reductions in 23.3% of patients and can be safely delivered in the majority of cases. Interpretation: Our results indicate that there are image-distinct subpopulations that have differential sensitivity to radiotherapy. The image-based deep learning framework proposed herein is the first opportunity to use medical images to individualize radiotherapy dose.

The Feasibility of Integrating Resting-State fMRI Networks into Radiotherapy Treatment Planning.

BACKGROUND: Functional magnetic resonance imaging (fMRI) presents the ability to selectively protect functionally significant regions of the brain when primary brain tumors are treated with radiation therapy. Previous research has focused on task-based fMRI of language and sensory networks; however, there has been limited investigation on the inclusion of resting-state fMRI into the design of radiation treatment plans. METHODS AND MATERIALS: In this pilot study of 9 patients with primary brain tumors, functional data from the default mode network (DMN), a network supporting cognitive functioning, was obtained from resting-state fMRI and retrospectively incorporated into the design of radiation treatment plans. We compared the dosimetry of these fMRI DMN avoidance treatment plans with standard of care treatment plans to demonstrate feasibility. In addition, we used normal tissue complication probability models to estimate the relative benefit of fMRI DMN avoidance treatment plans over standard of care treatment plans in potentially reducing memory loss, a surrogate for cognitive function. RESULTS: On average, we achieved 20% (P = 0.002) and 12% (P = 0.002) reductions in the mean and maximum doses, respectively, to the DMN without compromising the dose coverage to the planning tumor volume or the dose-volume constraints to organs at risk. Normal tissue complication probability models revealed that when the fMRI DMN was considered during radiation treatment planning, the probability of developing memory loss was lowered by more than 20%. CONCLUSION: In this pilot study, we demonstrated the feasibility of including rs-MRI data into the design of radiation treatment plans to spare cognitively relevant brain regions during radiation therapy. These results lay the groundwork for future clinical trials that incorporate such treatment planning methods to investigate the long-term behavioral impact of this reduction in dose to the cognitive areas and their neural networks that support cognitive performance.

Evaluating the impact of extended field-of-view CT reconstructions on CT values and dosimetric accuracy for radiation therapy.

PURPOSE: Wide bore CT scanners use extended field-of-view (eFOV) reconstruction algorithms to attempt to recreate tissue truncated due to large patient habitus. Radiation therapy planning systems rely on accurate CT numbers in order to correctly plan and calculate radiation dose. This study looks at the impact of eFOV reconstructions on CT numbers and radiation dose calculations in real patient geometries. METHODS: A large modular phantom based on real patient geometries was created to surround a CIRS Model 062M phantom. The modular sections included a smooth patient surface, a skin fold in the patient surface, and the addition of arms for simulation of the patient in arms up or arms down position. This phantom was used to evaluate the accuracy of CT numbers for three extended FOV algorithms implemented on Siemens CT scanners: eFOV, HDFOV, and HDProFOV. Six different configurations of the phantoms were scanned and images were reconstructed for the three different extended FOV algorithms. The CIRS phantom inserts and overall phantom geometry were contoured in each image, and the Hounsfield units (HU) numbers were compared to an image of the phantom within the standard scan FOV (sFOV) without the modular sections. To evaluate the effect on dose calculations, six radiotherapy patients previously treated at our institution (three head and neck and three chest/pelvis) whose body circumferences extended past the 50 cm sFOV in the treatment planning CT were used. Images acquired on a Siemens Sensation Open scanner were reconstructed using sFOV, eFOV and HDFOV algorithms. A physician and dosimetrist identified the radiation target, critical organs, and external patient contour. A benchmark CT was created for each patient, consisting of an average of the 3 CT reconstructions with a density override applied to regions containing truncation artifacts. The benchmark CT was used to create an optimal radiation treatment plan. The plan was copied onto each CT reconstruction without density override and dose was recalculated. RESULTS: Tissue extending past the sFOV impacts the HU numbers for tissues inside and outside the sFOV when using extended FOV reconstructions. On average, the HU for all CIRS density inserts in the arms up (arms down) position varied by 43 HU (67 HU), 39 HU (73 HU), and 18 HU (51 HU) for the eFOV, HDFOV, and HDProFOV scans, respectively. In the patient dose calculations, patients with a smooth patient contour had the least deviation from the benchmark in the HDFOV (0.1-0.5%) compared to eFOV (0.4-1.8%) reconstructions. In cases with large amounts of tissue and irregular skin folds, the eFOV deviated the least from the benchmark (range 0.2-0.6% dose difference) compared to HDFOV (range 1.3-1.8% dose difference). CONCLUSIONS: All reconstruction algorithms demonstrated good CT number accuracy in the center of the image. Larger artifacts are seen near and extending outside the scan FOV, however, dose calculations performed using typical beam arrangements using the extended FOV reconstructions were still mostly within 2.5% of best estimated reference values.

Technical Note: Enhancing soft tissue contrast and radiation-induced image changes with dual-energy CT for radiation therapy.

PURPOSE: The purpose of this work is to investigate the use of low-energy monoenergetic decompositions obtained from dual-energy CT (DECT) to enhance image contrast and the detection of radiation-induced changes of CT textures in pancreatic cancer. METHODS: The DECT data acquired for 10 consecutive pancreatic cancer patients during routine nongated CT-guided radiation therapy (RT) using an in-room CT (Definition AS Open, Siemens Healthcare, Malvern, PA) were analyzed. With a sequential DE protocol, the scanner rapidly performs two helical acquisitions, the first at a tube voltage of 80 kVp and the second at a tube voltage of 140 kVp. Virtual monoenergetic images across a range of energies from 40 to 140 keV were reconstructed using an image-based material decomposition. Intravenous (IV) bolus-free contrast enhancement in pancreas patient tumors was measured across a spectrum of monoenergies. For treatment response assessment, the changes in CT histogram features (including mean CT number (MCTN), entropy, kurtosis) in pancreas tumors were measured during treatment. The results from the monoenergetic decompositions were compared to those obtained from the standard 120 kVp CT protocol for the same subjects. RESULTS: Data of monoenergetic decompositions of the 10 patients confirmed the expected enhancement of soft tissue contrast as the energy is decreased. The changes in the selected CT histogram features in the pancreas during RT delivery were amplified with the low-energy monoenergetic decompositions, as compared to the changes measured from the 120 kVp CTs. For the patients studied, the average reduction in the MCTN in pancreas from the first to the last (the 28th) treatment fraction was 4.09 HU for the standard 120 kVp and 11.15 HU for the 40 keV monoenergetic decomposition. CONCLUSIONS: Low-energy monoenergetic decompositions from DECT substantially increase soft tissue contrast and increase the magnitude of radiation-induced changes in CT histogram textures during RT delivery for pancreatic cancer. Therefore, quantitative DECT may assist the detection of early RT response.

A Supervised Learning Tool for Prostate Cancer Foci Detection and Aggressiveness Identification using Multiparametric magnetic resonance imaging/magnetic resonance spectroscopy imaging.

Prostate cancer is the most frequently diagnosed cancer in men in the United States. The current main methods for diagnosing prostate cancer include prostate-specific antigen test and transrectal biopsy. Prostate-specific antigen screening has been criticized for overdiagnosis and unnecessary treatment, and transrectal biopsy is an invasive procedure with low sensitivity for diagnosis. We provided a quantitative tool using supervised learning with multiparametric imaging to be able to accurately detect cancer foci and its aggressiveness. A total of 223 specimens from patients who received magnetic resonance imaging (MRI) and magnetic resonance spectroscopy imaging prior to the surgery were studied. Multiparametric imaging included extracting T2-map, apparent diffusion coefficient (ADC) using diffusion-weighted MRI, Ktrans using dynamic contrast-enhanced MRI, and 3-dimensional-MR spectroscopy. A pathologist reviewed all 223 specimens and marked cancerous regions on each and graded them with Gleason scores, which served as the ground truth to validate our prediction model. In cancer aggressiveness prediction, the average area under the receiver operating characteristic curve (AUC) value was 0.73 with 95% confidence interval (0.72-0.74) and the average sensitivity and specificity were 0.72 (0.71-0.73) and 0.73 (0.71-0.75), respectively. For the cancer detection model, the average AUC value was 0.68 (0.66-0.70) and the average sensitivity and specificity were 0.73 (0.70-0.77) and 0.62 (0.60-0.68), respectively. Our method included capability to handle class imbalance using adaptive boosting with random undersampling. In addition, our method was noninvasive and allowed for nonsubjective disease characterization, which provided physician information to make personalized treatment decision.

Evaluation of template matching for tumor motion management with cine-MR images in lung cancer patients.

PURPOSE: Accurate determination of tumor position is crucial for successful application of motion compensated radiotherapy in lung cancer patients. This study tested the performance of an automated template matching algorithm in tracking the tumor position on cine-MR images by examining the tracking error and further comparing the tracking error to the interoperator variability of three human reviewers. METHODS: Cine-MR images of 12 lung cancer patients were analyzed. Tumor positions were determined both automatically with template matching and manually by a radiation oncologist and two additional reviewers trained by the radiation oncologist. Performance of the automated template matching was compared against the ground truth established by the radiation oncologist. Additionally, the tracking error of template matching, defined as the difference in the tumor positions determined with template matching and the ground truth, was investigated and compared to the interoperator variability for all patients in the anterior-posterior (AP) and superior-inferior (SI) directions, respectively. RESULTS: The median tracking error for ten out of the 12 patients studied in both the AP and SI directions was less than 1 pixel (= 1.95 mm). Furthermore, the median tracking error for seven patients in the AP direction and nine patients in the SI direction was less than half a pixel (= 0.975 mm). The median tracking error was positively correlated with the tumor motion magnitude in both the AP (R = 0.55, p = 0.06) and SI (R = 0.67, p = 0.02) directions. Also, a strong correlation was observed between tracking error and interoperator variability (y = 0.26 + 1.25x, R = 0.84, p < 0.001) with the latter larger. CONCLUSIONS: Results from this study indicate that the performance of template matching is comparable with or better than that of manual tumor localization. This study serves as preliminary investigations towards developing online motion tracking techniques for hybrid MRI-Linac systems. Accuracy of template matching makes it a suitable candidate to replace the labor intensive manual tumor localization for obtaining the ground truth when testing other motion management techniques.

Nitrite induces the extravasation of iron oxide nanoparticles in hypoxic tumor tissue.

Nitrite undergoes reconversion to nitric oxide under conditions characteristic of the tumor microenvironment, such as hypoxia and low pH. This selective conversion of nitrite into nitric oxide in tumor tissue has led to the possibility of using nitrite to enhance drug delivery and the radiation response. In this work, we propose to serially characterize the vascular response of brain tumor-bearing rats to nitrite using contrast-enhanced R2 * mapping. Imaging is performed using a multi-echo gradient echo sequence at baseline, post iron oxide nanoparticle injection and post-nitrite injection, whilst the animal is breathing air. The results indicate that nitrite sufficiently increases the vascular permeability in C6 gliomas, such that the iron oxide nanoparticles accumulate within the tumor tissue. When animals breathed 100% oxygen, the contrast agent remained within the vasculature, indicating that the conversion of nitrite to nitric oxide occurs in the presence of hypoxia within the tumor. The hypoxia-dependent, nitrite-induced extravasation of iron oxide nanoparticles observed herein has implications for the enhancement of conventional and nanotherapeutic drug delivery.

Evaluation of fractional regional ventilation using 4D-CT and effects of breathing maneuvers on ventilation.

PURPOSE: Current implementations of methods based on Hounsfield units to evaluate regional lung ventilation do not directly incorporate tissue-based mass changes that occur over the respiratory cycle. To overcome this, we developed a 4-dimensional computed tomography (4D-CT)-based technique to evaluate fractional regional ventilation (FRV) that uses an individualized ratio of tidal volume to end-expiratory lung volume for each voxel. We further evaluated the effect of different breathing maneuvers on regional ventilation. The results from this work will help elucidate the relationship between global and regional lung function. METHODS AND MATERIALS: Eight patients underwent 3 sets of 4D-CT scans during 1 session using free-breathing, audiovisual guidance, and active breathing control. FRV was estimated using a density-based algorithm with mass correction. Internal validation between global and regional ventilation was performed by use of the imaging data collected during the use of active breathing control. The impact of breathing maneuvers on FRV was evaluated comparing the tidal volume from 3 breathing methods. RESULTS: Internal validation through comparison between the global and regional changes in ventilation revealed a strong linear correlation (slope of 1.01, R2 of 0.97) between the measured global lung volume and the regional lung volume calculated by use of the "mass corrected" FRV. A linear relationship was established between the tidal volume measured with the automated breathing control system and FRV based on 4D-CT imaging. Consistently larger breathing volumes were observed when coached breathing techniques were used. CONCLUSIONS: The technique presented improves density-based evaluation of lung ventilation and establishes a link between global and regional lung ventilation volumes. Furthermore, the results obtained are comparable with those of other techniques of functional evaluation such as spirometry and hyperpolarized-gas magnetic resonance imaging. These results were demonstrated on retrospective analysis of patient data, and further research using prospective data is under way to validate this technique against established clinical tests.

Improving temporal resolution of pulmonary perfusion imaging in rats using the partially separable functions model.

Dynamic contrast-enhanced MRI (or DCE-MRI) is a useful tool for measuring blood flow and perfusion, and it has found use in the study of pulmonary perfusion in animal models. However, DCE-MRI experiments are difficult in small animals such as rats. A recently developed method known as Interleaved Radial Imaging and Sliding window-keyhole (IRIS) addresses this problem by using a data acquisition scheme that covers (k,t)-space with data acquired from multiple bolus injections of a contrast agent. However, the temporal resolution of IRIS is limited by the effects of temporal averaging inherent in the sliding window and keyhole operations. This article describes a new method to cover (k,t)-space based on the theory of partially separable functions (PSF). Specifically, a sparse sampling of (k,t)-space is performed to acquire two data sets, one with high-temporal resolution and the other with extended k-space coverage. The high-temporal resolution training data are used to determine the temporal basis functions of the PSF model, whereas the other data set is used to determine the spatial variations of the model. The proposed method was validated by simulations and demonstrated by an experimental study. In this particular study, the proposed method achieved a temporal resolution of 32 msec.

Ventilation/perfusion imaging in a rat model of airway obstruction.

The global increase in asthma, chronic obstructive pulmonary disease, and other pulmonary diseases has stimulated interest in preclinical rat models of pulmonary disease. Imaging methods for study of these models is particularly appealing since the results can be readily translated to the clinical setting. Comprehensive understanding of lung function can be achieved by performing registered pulmonary ventilation and perfusion imaging studies in the same animal. While ventilation imaging has been addressed for small animals, quantitative pulmonary perfusion imaging has not been feasible until recently, with our proposed technique for quantitative perfusion imaging using multiple contrast-agent injections and a view-sharing radial imaging technique. Here, we combine the method with registered ventilation imaging using hyperpolarized (3)He in an airway obstruction rodent model. To our knowledge, this is the first comprehensive quantitative assessment of lung function in small animals at high spatial resolution. Standard deviation of the log (V/Q) is used as a quantitative biomarker to differentiate heterogeneity between the control and treatment group. The estimated value of the biomarker lies within the normal range of values reported in the literature. The biomarker that was extracted using the imaging technique described in this work showed statistically significant differences between the control rats and those with airway obstruction.

Quantitative analysis of hyperpolarized 3He ventilation changes in mice challenged with methacholine.

The capability to use high-resolution (3)He MRI to depict regional ventilation changes and airway narrowing in mice challenged with methacholine (MCh) offers the opportunity to gain new insights into the study of asthma. However, to fully exploit the value of this novel technique, it is important to move beyond visual inspection of the images toward automated and quantitative analysis. To address this gap, we describe a postprocessing approach to create ventilation difference maps to better visualize and quantify regional ventilation changes before and after MCh challenge. We show that difference maps reveal subtle changes in airway caliber, and highlight both focal and diffuse regional alterations in ventilation. Ventilation changes include both hypoventilation and compensatory areas of hyperventilation. The difference maps can be quantified by a histogram plot of the ventilation changes, in which the standard deviation increases with MCh dose (R(2) = 0.89). This method of analysis is shown to be more sensitive than simple threshold-based detection of gross ventilation defects.

A high-precision contrast injector for small animal x-ray digital subtraction angiography.

The availability of genetically altered animal models of human disease for basic research has generated great interest in new imaging methodologies. Digital subtraction angiography (DSA) offers an appealing approach to functional imaging in small animals because of the high spatial and temporal resolution, and the ability to visualize and measure blood flow. The micro-injector described here meets crucial performance parameters to ensure optimal vessel enhancement without significantly increasing the total blood volume or producing overlap of enhanced structures. The micro-injector can inject small, reproducible volumes of contrast agent at high flow rates with computer-controlled timing synchronized to cardiopulmonary activity. Iterative bench-top and live animal experiments with both rat and mouse have been conducted to evaluate the performance of this computer-controlled micro-injector, a first demonstration of a new device designed explicitly for the unique requirements of DSA in small animals. Injection protocols were optimized and screened for potential physiological impact. For the optimized protocols, we found that changes in the time-density curves for representative regions of interest in the thorax were due primarily to physiological changes, independent of micro-injector parameters.

Left ventricle volume measurements in cardiac micro-CT: the impact of radiation dose and contrast agent.

Micro-CT-based cardiac function estimation in small animals requires measurement of left ventricle (LV) volume at multiple time points during the cardiac cycle. Measurement accuracy depends on the image resolution, its signal and noise properties, and the analysis procedure. This work compares the accuracy of the Otsu thresholding and a region sampled binary mixture approach, for live mouse LV volume measurement using 100 microm resolution datasets. We evaluate both analysis methods after varying the volume of injected contrast agent and the number of projections used for CT reconstruction with a goal of permitting reduced levels of both X-ray and contrast agent doses.

Pulmonary perfusion imaging in the rodent lung using dynamic contrast-enhanced MRI.

With the development of various models of pulmonary disease, there is tremendous interest in quantitative regional assessment of pulmonary function. While ventilation imaging has been addressed to a certain extent, perfusion imaging for small animals has not kept pace. In humans and large animals perfusion can be assessed using dynamic contrast-enhanced (DCE) MRI with a single bolus injection of a gadolinium (Gd)-based contrast agent. But the method developed for the clinic cannot be translated directly to image the rodent due to the combined requirements of higher spatial and temporal resolution. This work describes a novel image acquisition technique staggered over multiple, repeatable bolus injections of contrast agent using an automated microinjector, synchronized with image acquisition to achieve dynamic first-pass contrast enhancement in the rat lung. This allows dynamic first-pass imaging that can be used to quantify pulmonary perfusion. Further improvements are made in the spatial and temporal resolution by combining the multiple injection acquisition method with Interleaved Radial Imaging and "Sliding window-keyhole" reconstruction (IRIS). The results demonstrate a simultaneous increase in spatial resolution (<200 mum) and temporal resolution (<200 ms) over previous methods, with a limited loss in signal-to-noise-ratio.

3He MRI in mouse models of asthma.

In the study of asthma, a vital role is played by mouse models, because knockout or transgenic methods can be used to alter disease pathways and identify therapeutic targets that affect lung function. Assessment of lung function in rodents by available methods is insensitive because these techniques lack regional specificity. A more sensitive method for evaluating lung function in human asthma patients uses hyperpolarized (HP) (3)He MRI before and after bronchoconstriction induced by methacholine (MCh). We now report the ability to perform such (3)He imaging of MCh response in mice, where voxels must be approximately 3000 times smaller than in humans and (3)He diffusion becomes an impediment to resolving the airways. We show three-dimensional (3D) images that reveal airway structure down to the fifth branching and visualize ventilation at a resolution of 125 x 125 x 1000 microm(3). Images of ovalbumin (OVA)-sensitized mice acquired after MCh show both airway closure and ventilation loss. To also observe the MCh response in naive mice, we developed a non-slice-selective 2D protocol with 187 x 187 microm(2) resolution that was fast enough to record the MCh response and recovery with 12-s temporal resolution. The extension of (3)He MRI to mouse models should make it a valuable translational tool in asthma research.

Retrospective distortion correction for 3D MR diffusion tensor microscopy using mutual information and Fourier deformations.

Magnetic resonance diffusion tensor imaging (DTI) can be complicated by distortions that contribute to errors in tissue characterization and loss of fine structures. This work presents a correction scheme based on retrospective registration via mutual information (MI), using Fourier transform (FT)-based deformations to enhance the reliability of the entropy-based image registration. The registration methodology is applied to correct distortions in 3D high-resolution DTI datasets, incorporating a complete set of affine deformations. The results demonstrate that the proposed methodology can consistently and significantly reduce the number of misregistered pixels, leading to marked improvement in the visualization of internal brain white matter (WM) structure via DTI. Post-registration analysis revealed that eddy-current effects cannot fully account for the observed image distortions. Combined, these findings support the non-model-based, postprocessing approach for correcting distortions, and demonstrate the advantages of combining FT-based deformations and MI registration to enhance the practical utility of DTI.