Deformable Registration for Longitudinal Breast MRI Screening

Longitudinal registration of T1-weighted breast MRI: A registration algorithm (FLIRE) and clinical application

PURPOSE

MRI is commonly used to aid breast cancer diagnosis and treatment evaluation. For patients with breast cancer, neoadjuvant chemotherapy aims to reduce the tumor size and extent of surgery necessary. The current clinical standard to measure breast tumor response on MRI uses the longest tumor diameter. Radiologists also account for other tissue properties including tumor contrast or pharmacokinetics in their assessment. Accurate longitudinal image registration of breast tissue is critical to properly compare response to treatment at different timepoints.

METHODS

In this study, a deformable Fast Longitudinal Image Registration (FLIRE) algorithm was optimized for breast tissue. FLIRE was then compared to the publicly available software packages with high accuracy (DRAMMS) and fast runtime (Elastix). Patients included in the study received longitudinal T1-weighted MRI without fat saturation at two to six timepoints as part of asymptomatic screening (n = 27) or throughout neoadjuvant chemotherapy treatment (n = 32). T1-weighted images were registered to the first timepoint with each algorithm.

RESULTS

Alignment and runtime performance were compared using two-way repeated measure ANOVAs (P < 0.05). Across all patients, Pearson's correlation coefficient across the entire image volume was slightly higher with statistical significance and had less variance for FLIRE (0.98 ± 0.01 stdev) compared to DRAMMS (0.97 ± 0.03 stdev) and Elastix (0.95 ± 0.03 stdev). Additionally, FLIRE runtime (10.0 mins) was 9.0 times faster than DRAMMS (89.6 mins) and 1.5 times faster than Elastix (14.5 mins) on a Linux workstation.

CONCLUSION

FLIRE demonstrates promise for time-sensitive clinical applications due to its accuracy, robustness across patients and timepoints, and speed.

Incorporating heterogeneity and anisotropy for surgical applications in breast deformation modeling

BACKGROUND

Simulating soft-tissue breast deformations is of interest for many applications including image fusion, longitudinal registration, and image-guided surgery. For the surgical use case, positional changes cause breast deformations that compromise the use of preoperative imaging to inform tumor excision. Even when acquiring imaging in the supine position, which better reflects surgical presentation, deformations still occur due to arm motion and orientation changes. A biomechanical modeling approach to simulate supine breast deformations for surgical applications must be both accurate and compatible with the clinical workflow.

METHODS

A supine MR breast imaging dataset from n = 11 healthy volunteers was used to simulate surgical deformations by acquiring images in arm-down and arm-up positions. Three linear-elastic modeling approaches with varying levels of complexity were used to predict deformations caused by this arm motion: a homogeneous isotropic model, a heterogeneous isotropic model, and a heterogeneous anisotropic model using a transverse-isotropic constitutive model.

FINDINGS

The average target registration errors for subsurface anatomical features were 5.4 ± 1.5 mm for the homogeneous isotropic model, 5.3 ± 1.5 mm for the heterogeneous isotropic model, and 4.7 ± 1.4 mm for the heterogeneous anisotropic model. A statistically significant improvement in target registration error was observed between the heterogeneous anisotropic model and both the homogeneous and the heterogeneous isotropic models (P < 0.01).

INTERPRETATION

While a model that fully incorporates all constitutive complexities of anatomical structure likely achieves the best accuracy, a computationally tractable heterogeneous anisotropic model provided significant improvement and may be applicable for image-guided breast surgeries.

Development and validation of a four-dimensional registration technique for DCE breast MRI

BACKGROUND

Patient motion can degrade image quality of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) due to subtraction artifacts. By objectively and subjectively assessing the impact of principal component analysis (PCA)-based registration on pretreatment DCE-MRIs of breast cancer patients, we aim to validate four-dimensional registration for DCE breast MRI.

RESULTS

After applying a four-dimensional, PCA-based registration algorithm to 154 pretreatment DCE-MRIs of histopathologically well-described breast cancer patients, we quantitatively determined image quality in unregistered and registered images. For subjective assessment, we ranked motion severity in a clinical reading setting according to four motion categories (0: no motion, 1: mild motion, 2: moderate motion, 3: severe motion with nondiagnostic image quality). The median of images with either moderate or severe motion (median category 2, IQR 0) was reassigned to motion category 1 (IQR 0) after registration. Motion category and motion reduction by registration were correlated (Spearman's rho: 0.83, p < 0.001). For objective assessment, we performed perfusion model fitting using the extended Tofts model and calculated its volume transfer coefficient K^trans as surrogate parameter for motion artifacts. Mean K^trans decreased from 0.103 (± 0.077) before registration to 0.097 (± 0.070) after registration (p < 0.001). Uncertainty in perfusion quantification was reduced by 7.4% after registration (± 15.5, p < 0.001).

CONCLUSIONS

Four-dimensional, PCA-based image registration improves image quality of breast DCE-MRI by correcting for motion artifacts in subtraction images and reduces uncertainty in quantitative perfusion modeling. The improvement is most pronounced when moderate-to-severe motion artifacts are present.

Supine magnetic resonance image registration for breast surgery: insights on material mechanics

Purpose

Breast conserving surgery (BCS) is a common procedure for early-stage breast cancer patients. Supine preoperative magnetic resonance (MR) breast imaging for visualizing tumor location and extent, while not standard for procedural guidance, is being explored since it more closely represents the surgical presentation compared to conventional diagnostic imaging positions. Despite this preoperative imaging position, deformation is still present between the supine imaging and surgical state. As a result, a fast and accurate image-to-physical registration approach is needed to realize image-guided breast surgery.

Approach

In this study, three registration methods were investigated on healthy volunteers' breasts ( n = 11 ) with the supine arm-down position simulating preoperative imaging and supine arm-up position simulating intraoperative presentation. The registration methods included (1) point-based rigid registration using synthetic fiducials, (2) nonrigid biomechanical model-based registration using sparse data, and (3) a data-dense three-dimensional diffeomorphic image-based registration from the Advanced Normalization Tools (ANTs) repository. Additionally, deformation metrics (volume change and anisotropy) were calculated from the ANTs deformation field to better understand breast material mechanics.

Results

The average target registration errors (TRE) were 10.4 ± 2.3 , 6.4 ± 1.5 , and 2.8 ± 1.3    mm (mean ± standard deviation) and the average fiducial registration errors (FRE) were 7.8 ± 1.7 , 2.5 ± 1.1 , and 3.1 ± 1.1    mm for the point-based rigid, nonrigid biomechanical, and ANTs registrations, respectively. The mechanics-based deformation metrics revealed an overall anisotropic tissue behavior and a statistically significant difference in volume change between glandular and adipose tissue, suggesting that nonrigid modeling methods may be improved by incorporating material heterogeneity and anisotropy.

Conclusions

Overall, registration accuracy significantly improved with increasingly flexible and data-dense registration methods. Analysis of these outcomes may inform the future development of image guidance systems for lumpectomy procedures.

The application of multiple metrics in deformable image registration for target volume delineation of breast tumor bed

BACKGROUND AND PURPOSE

For postoperative breast cancer patients, deformable image registration (DIR) is challenged due to the large deformations and non-correspondence caused by tumor resection and clip insertion. To deal with it, three metrics (fiducial-, region-, and intensity-based) were jointly used in DIR algorithm for improved accuracy.

MATERIALS AND METHODS

Three types of metrics were combined to form a single-objective function in DIR algorithm. Fiducial-based metric was used to minimize the distance between the corresponding point sets of two images. Region-based metric was used to improve the overlap between the corresponding areas of two images. Intensity-based metric was used to maximize the correlation between the corresponding voxel intensities of two images. The two CT images, one before surgery and the other after surgery, were acquired from the same patient in the same radiotherapy treatment position. Twenty patients who underwent breast-conserving surgery and postoperative radiotherapy were enrolled in this study.

RESULTS

For target registration error, the difference between the proposed and the conventional registration methods was statistically significant for soft tissue (2.06 vs. 7.82, p = 0.00024 < 0.05) and body boundary (3.70 vs. 6.93, p = 0.021 < 0.05). For visual assessment, the proposed method achieved better matching result for soft tissue and body boundary.

CONCLUSIONS

Comparing to the conventional method, the registration accuracy of the proposed method was significantly improved. This method provided a feasible way for target volume delineation of tumor bed in postoperative radiotherapy of breast cancer patients.

Using Breast Tissue Information and Subject-Specific Finite-Element Models to Optimize Breast Compression Parameters for Digital Mammography

Digital mammography has become a first-line diagnostic tool for clinical breast cancer screening due to its high sensitivity and specificity. Mammographic compression force is closely associated with image quality and patient comfort. Therefore, optimizing breast compression parameters is essential. Subjects were recruited for digital mammography and breast magnetic resonance imaging (MRI) within a month. Breast MRI images were used to calculate breast volume and volumetric breast density (VBD) and construct finite element models. Finite element analysis was performed to simulate breast compression. Simulated compressed breast thickness (CBT) was compared with clinical CBT and the relationships between compression force, CBT, breast volume, and VBD were established. Simulated CBT had a good linear correlation with the clinical CBT (R2 = 0.9433) at the clinical compression force. At 10, 12, 14, and 16 daN, the mean simulated CBT of the breast models was 5.67, 5.13, 4.66, and 4.26 cm, respectively. Simulated CBT was positively correlated with breast volume (r > 0.868) and negatively correlated with VBD (r < –0.338). The results of this study provides a subject-specific and evidence-based suggestion of mammographic compression force for radiographers considering image quality and patient comfort.

Breast image registration for surgery: Insights on material mechanics modeling

Breast conserving surgery (BCS) is a common procedure for early-stage breast cancer patients. Supine preoperative magnetic resonance (MR) breast imaging for visualizing tumor location and extent, while not standard for procedural guidance, more closely represents the surgical presentation compared to conventional diagnostic pendant positioning. Optimal utilization for surgical guidance, however, requires a fast and accurate image-to-physical registration from preoperative imaging to intraoperative surgical presentation. In this study, three registration methods were investigated on healthy volunteers' breasts (n=11) with the arm-down position simulating preoperative imaging and arm-up position simulating intraoperative data. The registration methods included: (1) point-based rigid registration using synthetic fiducials, (2) non-rigid biomechanical model-based registration using sparse data, and (3) a data-dense 3D diffeomorphic image-based registration from the Advanced Normalization Tools (ANTs) repository. The average target registration errors (TRE) were 10.4 ± 2.3, 6.4 ± 1.5, and 2.8 ± 1.3 mm (mean ± standard deviation) and the average fiducial registration errors (FRE) were 7.8 ± 1.7, 2.5 ± 1.1, and 3.1 ± 1.1 mm (mean ± standard deviation) for the point-based rigid, nonrigid biomechanical, and ANTs registrations, respectively. Additionally, common mechanics-based deformation metrics (volume change and anisotropy) were calculated from the ANTs deformation field. The average metrics revealed anisotropic tissue behavior and a statistical difference in volume change between glandular and adipose tissue, suggesting that nonrigid modeling methods may be improved by incorporating material heterogeneity and anisotropy. Overall, registration accuracy significantly improved with increasingly flexible registration methods, which may inform future development of image guidance systems for lumpectomy procedures.

Vascularity and Dynamic Contrast-Enhanced Breast Magnetic Resonance Imaging

Angiogenesis is a key step in the initiation and progression of an invasive breast cancer. High microvessel density by morphological characterization predicts metastasis and poor survival in women with invasive breast cancers. However, morphologic characterization is subject to variability and only can evaluate a limited portion of an invasive breast cancer. Consequently, breast Magnetic Resonance Imaging (MRI) is currently being evaluated to assess vascularity. Recently, through the new field of radiomics, dynamic contrast enhanced (DCE)-MRI is being used to evaluate vascular density, vascular morphology, and detection of aggressive breast cancer biology. While DCE-MRI is a highly sensitive tool, there are specific features that limit computational evaluation of blood vessels. These include (1) DCE-MRI evaluates gadolinium contrast and does not directly evaluate biology, (2) the resolution of DCE-MRI is insufficient for imaging small blood vessels, and (3) DCE-MRI images are very difficult to co-register. Here we review computational approaches for detection and analysis of blood vessels in DCE-MRI images and present some of the strategies we have developed for co-registry of DCE-MRI images and early detection of vascularization.

Quantitative Measures of Background Parenchymal Enhancement Predict Breast Cancer Risk

BACKGROUND. Higher categories of background parenchymal enhancement (BPE) increase breast cancer risk. However, current clinical BPE categorization is subjective. OBJECTIVE. Using a semiautomated segmentation algorithm, we calculated quantitative BPE measures and investigated the utility of individual features and feature pairs in significantly predicting subsequent breast cancer risk compared with radiologist-assigned BPE category. METHODS. In this retrospective case-control study, we identified 95 women at high risk of breast cancer but without a personal history of breast cancer who underwent breast MRI. Of these women, 19 subsequently developed breast cancer and were included as cases. Each case was age matched to four control patients (76 control patients total). Sociodemographic characteristics were compared between the cases and matched control patients using the Mann-Whitney U test. From each dynamic contrast-enhanced MRI examination, quantitative fibroglandular tissue and BPE measures were computed by averaging enhancing voxels above enhancement ratio thresholds (0-100%), totaling the enhancing volume above thresholds (BPE volume in cm³), and estimating the percentage of enhancing tissue above thresholds relative to total breast volume (BPE%) on each gadolinium-enhanced phase. For the 91 imaging features generated, we compared predictive performance using conditional logistic regression with 80:20 hold-out cross validation and ROC curve analysis. ROC AUC was the figure of merit. Sensitivity, specificity, PPV, and NPV were also computed. All feature pairs were exhaustively searched to identify those with the highest AUC and Youden index. A DeLong test was used to compare predictive performance (AUCs). RESULTS. Women subsequently diagnosed with breast cancer were more likely to have mild, moderate, or marked BPE (odds ratio, 3.0; 95% CI, 0.9-10.0; p = .07). According to ROC curve analysis, a BPE category threshold greater than minimal resulted in a maximized AUC (0.62) in distinguishing cases from control patients. Compared with BPE category, the first gadolinium-enhanced (phase 1) BPE% at the 30% and 40% enhancement ratio thresholds yielded significantly higher AUC values of 0.85 (p = .0007) and 0.84 (p = .0004), respectively. Feature combinations showed similar AUC values with improved sensitivity. CONCLUSION. Preliminary data indicate that quantitative BPE measures may outperform radiologist-assigned category in breast cancer risk prediction. CLINICAL IMPACT. Future risk prediction models that incorporate quantitative measures warrant additional investigation.

Glenoid bone strain after anatomical total shoulder arthroplasty: In vitro measurements with micro-CT and digital volume correlation

Glenoid implant loosening remains a major source of failure and concern after anatomical total shoulder arthroplasty (aTSA). It is assumed to be associated with eccentric loading and excessive bone strain, but direct measurement of bone strain after aTSA is not available yet. Therefore, our objective was to develop an in vitro technique for measuring bone strain around a loaded glenoid implant. A custom loading device (1500 N) was designed to fit within a micro-CT scanner, to use digital volume correlation for measuring displacement and calculating strain. Errors were evaluated with three pairs of unloaded scans. The average displacement random error of three pairs of unloaded scans was 6.1 µm. Corresponding systematic and random errors of strain components were less than 806.0 µε and 2039.9 µε, respectively. The average strain accuracy (MAER) and precision (SDER) were 694.3 µε and 440.3 µε, respectively. The loaded minimum principal strain (8738.9 µε) was 12.6 times higher than the MAER (694.3 µε) on average, and was above the MAER for most of the glenoid bone volume (98.1%). Therefore, this technique proves to be accurate and precise enough to eventually compare glenoid implant designs, fixation techniques, or to validate numerical models of specimens under similar loading.

An investigation of the effect of fat suppression and dimensionality on the accuracy of breast MRI segmentation using U-nets

PURPOSE

Accurate segmentation of the breast is required for breast density estimation and the assessment of background parenchymal enhancement, both of which have been shown to be related to breast cancer risk. The MRI breast segmentation task is challenging, and recent work has demonstrated that convolutional neural networks perform well for this task. In this study, we have investigated the performance of several two-dimensional (2D) U-Net and three-dimensional (3D) U-Net configurations using both fat-suppressed and nonfat-suppressed images. We have also assessed the effect of changing the number and quality of the ground truth segmentations.

MATERIALS AND METHODS

We designed eight studies to investigate the effect of input types and the dimensionality of the U-Net operations for the breast MRI segmentation. Our training data contained 70 whole breast volumes of T1-weighted sequences without fat suppression (WOFS) and with fat suppression (FS). For each subject, we registered the WOFS and FS volumes together before manually segmenting the breast to generate ground truth. We compared four different input types to the U-nets: WOFS, FS, MIXED (WOFS and FS images treated as separate samples), and MULTI (WOFS and FS images combined into a single multichannel image). We trained 2D U-Nets and 3D U-Nets with these data, which resulted in our eight studies (2D-WOFS, 3D-WOFS, 2D-FS, 3D-FS, 2D-MIXED, 3D-MIXED, 2D-MULTI, and 3D-MULT). For each of these studies, we performed a systematic grid search to tune the hyperparameters of the U-Nets. A separate validation set with 15 whole breast volumes was used for hyperparameter tuning. We performed Kruskal-Walis test on the results of our hyperparameter tuning and did not find a statistically significant difference in the ten top models of each study. For this reason, we chose the best model as the model with the highest mean dice similarity coefficient (DSC) value on the validation set. The reported test results are the results of the top model of each study on our test set which contained 19 whole breast volumes annotated by three readers fused with the STAPLE algorithm. We also investigated the effect of the quality of the training annotations and the number of training samples for this task.

RESULTS

The study with the highest average DSC result was 3D-MULTI with 0.96 ± 0.02. The second highest average is 2D WOFS (0.96 ± 0.03), and the third is 2D MULTI (0.96 ± 0.03). We performed the Kruskal-Wallis one-way ANOVA test with Dunn's multiple comparison tests using Bonferroni P-value correction on the results of the selected model of each study and found that 3D-MULTI, 2D-MULTI, 3D-WOFS, 2D-WOFS, 2D-FS, and 3D-FS were not statistically different in their distributions, which indicates that comparable results could be obtained in fat-suppressed and nonfat-suppressed volumes and that there is no significant difference between the 3D and 2D approach. Our results also suggested that the networks trained on single sequence images or multiple sequence images organized in multichannel images perform better than the models trained on a mixture of volumes from different sequences. Our investigation of the size of the training set revealed that training a U-Net in this domain only requires a modest amount of training data and results obtained with 49 and 70 training datasets were not significantly different.

CONCLUSIONS

To summarize, we investigated the use of 2D U-Nets and 3D U-Nets for breast volume segmentation in T1 fat-suppressed and without fat-suppressed volumes. Although our highest score was obtained in the 3D MULTI study, when we took advantage of information in both fat-suppressed and nonfat-suppressed volumes and their 3D structure, all of the methods we explored gave accurate segmentations with an average DSC on >94% demonstrating that the U-Net is a robust segmentation method for breast MRI volumes.