Overview and Recommendations for Prospective Multi-institutional Spatially Fractionated Radiation Therapy Clinical Trials

PURPOSE

The highly heterogeneous dose delivery of spatially fractionated radiation therapy (SFRT) is a profound departure from standard radiation planning and reporting approaches. Early SFRT studies have shown excellent clinical outcomes. However, prospective multi-institutional clinical trials of SFRT are still lacking. This NRG Oncology/American Association of Physicists in Medicine working group consensus aimed to develop recommendations on dosimetric planning, delivery, and SFRT dose reporting to address this current obstacle toward the design of SFRT clinical trials.

METHODS AND MATERIALS

Working groups consisting of radiation oncologists, radiobiologists, and medical physicists with expertise in SFRT were formed in NRG Oncology and the American Association of Physicists in Medicine to investigate the needs and barriers in SFRT clinical trials.

RESULTS

Upon reviewing the SFRT technologies and methods, this group identified challenges in several areas, including the availability of SFRT, the lack of treatment planning system support for SFRT, the lack of guidance in the physics and dosimetry of SFRT, the approximated radiobiological modeling of SFRT, and the prescription and combination of SFRT with conventional radiation therapy.

CONCLUSIONS

Recognizing these challenges, the group further recommended several areas of improvement for the application of SFRT in cancer treatment, including the creation of clinical practice guidance documents, the improvement of treatment planning system support, the generation of treatment planning and dosimetric index reporting templates, and the development of better radiobiological models through preclinical studies and through conducting multi-institution clinical trials.

Prostate Tumor Regression during Radiation Therapy in Relation to Image Derived Estimates of Fibrosis Using Ultrashort Echo Time MRI

PURPOSE/OBJECTIVE(S)

An inflammatory response due to radiation injury can lead to the activation of fibroblasts and their transformation into myofibroblasts that produce organized collagen layers. We term these organized collagen layers "acute fibrosis" (FA). Small animal imaging has shown that accumulation of fibrosis is accelerated at a higher radiation dose with later remodeling of type III collagen into densely packed type I collagen structures, which later transform into what we term "chronic fibrosis" (FC). In prostate cancer (PCa) patients treated with EBRT, we evaluated image derived FA (IDFA) and FC (IDFC) estimates using non-contrast and late gadolinium contrast enhanced (LGE) IR UTE, respectively. We hypothesized that fibrosis changes during RT are associated to tumor regression.

MATERIALS/METHODS

Four PCa patients undergoing radiation and hormone suppression were included. 1.5T MR imaging was acquired at three time-points (TPs); pre-RT, on-RT (30-40 Gy), and post-RT (60-78 Gy). IDFA imaging: Stack of spirals dual echo (TE = 50, 2690µs, TI = 60ms) IR UTE research application. Subtracting the two echoes yielded short TE signal intensity (SI). IDFC imaging: stack of spirals dual echo IR UTE research application, TE = 50µs, TI = 200ms, acquired 15 minutes following Gadovist administration. Remnant tumor was contoured at each TP with T2, ADC, and DCE. Relative quantification; IDFA = IR UTE SI/Gluteal Muscle SI and IDFC = LGE IR UTE SI/Gluteal Muscle SI. The sum of normalized IDFA and IDFC SI within the remnant tumor at each TP was divided by the corresponding tumor volume yielding tumor intensity (TI) FA and FC accumulation estimates: TIFA and TIFC. Mean delivered dose within the treated volume to regions where post-RT IDFA and IDFC > 100% gluteal muscle SI was determined. Univariate relationships were evaluated using correlation coefficients (r).

RESULTS

The coefficients of variation for TIFA = 10% and TIFC = 11%. IDFA and IDFC were observed pre-RT on the prostate gland periphery, and on RT and post RT within the prostate gland. IDFA and IDFC mean doses were 74% and 85% of prescription, respectively. Tumor regression was incomplete by post-RT. Initial tumor burden was inversely correlated with pre-RT TIFC (r = -0.98, p = .01). Tumor volume change post RT was inversely correlated with larger on-RT TIFA (r = -0.76) and post-RT TIFC (r = -0.78).

CONCLUSION

In this small pilot study, results suggest that IDFA and IDFC may be associated with improved tumor regression. They also suggest higher delivered dose is related to regions of greater IDFC. Quantitative fibrosis may be an early marker of RT response. Caution must be taken when interpreting these results given the small sample size. Future work will include a larger cohort of patients and include post-RT imaging.

Image Derived Estimates of Fibrosis Using Ultrashort Echo Time MRI in Gynecologic Cancer

PURPOSE/OBJECTIVE(S)

Fibrosis forms during and after radiation therapy (RT) due to wound-healing and cell death. In gynecologic cancer patients treated with EBRT and HDR, we evaluated image derived (ID) acute fibrosis (IDFA) and chronic fibrosis (IDFC) estimates using non-contrast and late gadolinium contrast enhanced (LGE) inversion recovery ultrashort echo time (IR UTE) MRI, respectively. We hypothesized that that ID markers can quantify FA and FC within tumor as a result of response to RT and that fibrosis changes over the course of RT are associated with tumor regression.

MATERIALS/METHODS

Subjects: Three subjects with cervical squamous cell carcinoma (SCC), 1 subject with cervical adenocarcinoma, and 2 subjects with vaginal SCC undergoing RT were included. Image Acquisition & Analysis: 1.5T MR imaging at four time-points (TPs): pre-RT, on-RT, post-RT, and post 3mo RT. IDFA imaging: Stack of spirals dual echo (TE = 50, 2690µs), TI = 60ms IR UTE research application. Subtracting the two echoes yielded short TE signal intensity (SI). IDFC imaging: stack of spirals dual echo IR UTE research application, TE = 50µs, TI = 200ms, acquired 15 minutes following contrast. Remnant tumor was contoured at each TP with T2, ADC, and DCE. Relative IDFA, IDFC quantification: voxel-wise IR UTE and LGE IR UTE signal intensity (SI) normalized to gluteal muscle SI. The sum of normalized IDFA and IDFC SI within the remnant tumor at each TP was divided by the corresponding tumor volume yielding tumor intensity (TI) TIFA and TIFC fibrosis accumulation estimates.

STATISTICS

The coefficients of variation (COV) for TIFA and TIFC were calculated by varying tumor margins and re-computing TIFA and TIFC. Univariate relationships were evaluated using linear regression.

RESULTS

The COVs for TIFA = 13% and TIFC = 7%. IDFA and IDFC were observed pre-RT on the tumor periphery, on-RT and post-RT within the tumor, with IDFA potentially transforming into IDFC. Table 1 shows that a greater decrease in tumor volume post RT was correlated with a larger pre-RT TIFA (r = -0.85, p = .03). Decrease in tumor volume was correlated with a larger TIFC post-RT (r = -0.79, p = .05). Post 3 month TIFC was associated with tumor reduction (r = 0.82, n = 4).

CONCLUSION

In this pilot study, we developed reproducible methods to quantify IDFA and IDFC within remnant tumor. Our results suggest that IDFA and IDFC may be associated with tumor response based on tumor volume.

Clinical Characterization of a Table Mounted Range Shifter Board for Synchrotron-Based Intensity Modulated Proton Therapy for Pediatric Craniospinal Irradiation

Purpose: To report our design, manufacturing, commissioning and initial clinical experience with a table-mounted range shifter board (RSB) intended to replace the machine-mounted range shifter (MRS) in a synchrotron-based pencil beam scanning (PBS) system to reduce penumbra and normal tissue dose for image-guided pediatric craniospinal irradiation (CSI). Methods: A custom RSB was designed and manufactured from a 3.5 cm thick slab of polymethyl methacrylate (PMMA) to be placed directly under patients, on top of our existing couch top. The relative linear stopping power (RLSP) of the RSB was measured using a multi-layer ionization chamber, and output constancy was measured using an ion chamber. End-to-end tests were performed using the MRS and RSB approaches using an anthropomorphic phantom and radiochromic film measurements. Cone beam CT (CBCT) and 2D planar kV X-ray image quality were compared with and without the RSB present using image quality phantoms. CSI plans were produced using MRS and RSB approaches for two retrospective pediatric patients, and the resultant normal tissue doses were compared. Results: The RLSP of the RSB was found to be 1.163 and provided computed penumbra of 6.9 mm in the phantom compared to 11.8 mm using the MRS. Phantom measurements using the RSB demonstrated errors in output constancy, range, and penumbra of 0.3%, -0.8%, and 0.6 mm, respectively. The RSB reduced mean kidney and lung dose compared to the MRS by 57.7% and 46.3%, respectively. The RSB decreased mean CBCT image intensities by 86.8 HU but did not significantly impact CBCT or kV spatial resolution providing acceptable image quality for patient setup. Conclusions: A custom RSB for pediatric proton CSI was designed, manufactured, modeled in our TPS, and found to significantly reduce lateral proton beam penumbra compared to a standard MRS while maintaining CBCT and kV image-quality and is in routine use at our center.

Planning and Treatment Recommendations for Breast Proton Therapy From a Single Center's Experience

Purpose

Proton therapy use for breast cancer has grown due to advantages in coverage and potentially reduced late toxicities compared with conventional radiation therapy. We aimed to provide recommendations for robustness criteria, daily imaging, and quality assurance computed tomography (QA CT) frequency for these patients.

Methods and Materials

All patients treated for localized breast cancer at the Johns Hopkins Proton Center between November 2019 and February 2022 were eligible for inclusion. Daily shift information was extracted and examined through control charts. If an adaptive plan was used, the time to replan was recorded. Three and 5 mm setup uncertainty was used to calculate robustness. Robust evaluation of QA CTs was compared with initial robustness range for breast/chest wall and lymph node target coverage.

Results

Sixty-six patients were included: 19 with intact breast, 25 with non-reconstructed chest wall, and 22 with chest wall plus expanders or implants. Sixteen percent, 13%, and 41% of breast, chest wall, and expander/implant patients had a replan. Only patients with expanders or implants required 2 adaptive plans. Daily shift data showed large variation and did not correlate with plan adaptation. Patients without adaptive plans had QA CTs with dose-volume histogram metrics within robustness more frequently than those with adaptive plans. Using 3 mm robustness for patients who did not require an adaptive plan, 91% to 100% of patients had QA CTs within robustness, while 55% to 60% of patients with an adaptive plan had QA CTs within robustness for the axilla, internal mammary nodes, and supraclavicular nodes. Five millimeter setup uncertainty did not significantly improve this.

Conclusions

We recommend using daily cone beam CT because of the large variation in daily setup with 3 mm setup uncertainty in robustness analysis. If daily cone beam CT imaging is not available, then larger setup uncertainty should be used. Two QA CTs should be conducted during treatment if the patient has expanders or implants; otherwise, one QA CT is sufficient.

Treatment Planning of Bulky Tumors Using Pencil Beam Scanning Proton GRID Therapy

Purpose

To compare spatially fractionated radiation therapy (GRID) treatment planning techniques using proton pencil-beam-scanning (PBS) and photon therapy.

Materials and Methods

PBS and volumetric modulated arc therapy (VMAT) GRID plans were retrospectively generated for 5 patients with bulky tumors. GRID targets were arranged along the long axis of the gross tumor, spaced 2 and 3 cm apart, and treated with a prescription of 18 Gy. PBS plans used 2- to 3-beam multiple-field optimization with robustness evaluation. Dosimetric parameters including peak-to-edge ratio (PEDR), ratio of dose to 90% of the valley to dose to 10% of the peak VPDR(D90/D10), and volume of normal tissue receiving at least 5 Gy (V5) and 10 Gy (V10) were calculated. The peak-to-valley dose ratio (PVDR), VPDR(D90/D10), and organ-at-risk doses were prospectively assessed in 2 patients undergoing PBS-GRID with pretreatment quality assurance computed tomography (QACT) scans.

Results

PBS and VMAT GRID plans were generated for 5 patients with bulky tumors. Gross tumor volume values ranged from 826 to 1468 cm³. Peak-to-edge ratio for PBS was higher than for VMAT for both spacing scenarios (2-cm spacing, P = .02; 3-cm spacing, P = .01). VPDR(D90/D10) for PBS was higher than for VMAT (2-cm spacing, P = .004; 3-cm spacing, P = .002). Normal tissue V5 was lower for PBS than for VMAT (2-cm spacing, P = .03; 3-cm spacing, P = .02). Normal tissue mean dose was lower with PBS than with VMAT (2-cm spacing, P = .03; 3-cm spacing, P = .02). Two patients treated using PBS GRID and assessed with pretreatment QACT scans demonstrated robust PVDR, VPDR(D90/D10), and organs-at-risk doses.

Conclusions

The PEDR was significantly higher for PBS than VMAT plans, indicating lower target edge dose. Normal tissue mean dose was significantly lower with PBS than VMAT. PBS GRID may result in lower normal tissue dose compared with VMAT plans, allowing for further dose escalation in patients with bulky disease.

Evaluating Proton Dose and Associated Range Uncertainty Using Daily Cone-Beam CT

Purpose

This study aimed to quantitatively evaluate the range uncertainties that arise from daily cone-beam CT (CBCT) images for proton dose calculation compared to CT using a measurement-based technique.

Methods

For head and thorax phantoms, wedge-shaped intensity-modulated proton therapy (IMPT) treatment plans were created such that the gradient of the wedge intersected and was measured with a 2D ion chamber array. The measured 2D dose distributions were compared with 2D dose planes extracted from the dose distributions using the IMPT plan calculated on CT and CBCT. Treatment plans of a thymoma cancer patient treated with breath-hold (BH) IMPT were recalculated on 28 CBCTs and 9 CTs, and the resulting dose distributions were compared.

Results

The range uncertainties for the head phantom were determined to be 1.2% with CBCT, compared to 0.5% for CT, whereas the range uncertainties for the thorax phantom were 2.1% with CBCT, compared to 0.8% for CT. The doses calculated on CBCT and CT were similar with similar anatomy changes. For the thymoma patient, the primary source of anatomy change was the BH uncertainty, which could be up to 8 mm in the superior-inferior (SI) direction.

Conclusion

We developed a measurement-based range uncertainty evaluation method with high sensitivity and used it to validate the accuracy of CBCT-based range and dose calculation. Our study demonstrated that the CBCT-based dose calculation could be used for daily dose validation in selected proton patients.

Dosimetric evaluation of cone-beam CT-based synthetic CTs in pediatric patients undergoing intensity-modulated proton therapy

PURPOSE

To evaluate dosimetric changes detected using synthetic computed tomography (sCT) derived from online cone-beam CTs (CBCT) in pediatric patients treated using intensity-modulated proton therapy (IMPT).

METHODS

Ten pediatric patients undergoing IMPT and aligned daily using proton gantry-mounted CBCT were identified for retrospective analysis with treated anatomical sites fully encompassed in the CBCT field of view. Dates were identified when the patient received both a CBCT and a quality assurance CT (qCT) for routine dosimetric evaluation. sCTs were generated based on a deformable registration between the initial plan CT (pCT) and CBCT. The clinical IMPT plans were re-computed on the same day qCT and sCT, and dosimetric changes due to tissue change or response from the initial plan were computed using each image. Linear regression analysis was performed to determine the correlation between dosimetric changes detected using the qCT and the sCT. Gamma analysis was also used to compare the dose distributions computed on the qCT and sCT.

RESULTS

The correlation coefficients (p-values) between qCTs and sCTs for changes detected in target coverage, overall maximum dose, and organ at risk dose were 0.97 (< .001), 0.84 (.002) and 0.91 (< .001), respectively. Mean ± SD gamma pass rates of the sCT-based dose compared to the qCT-based dose at 3%/3 mm, 3%/2 mm, and 2%/2 mm criteria were 96.5%±4.5%, 93.2%±6.3%, and 91.3%±7.8%, respectively. Pass rates tended to be lower for targets near lung.

CONCLUSION

While insufficient for re-planning, sCTs provide approximate dosimetry without administering additional imaging dose in pediatric patients undergoing IMPT. Dosimetric changes detected using sCTs are correlated with changes detected using clinically-standard qCTs; however, residual differences in dosimetry remain a limitation. Further improvements in sCT image quality may both improve online dosimetric evaluation and reduce imaging dose for pediatric patients by reducing the need for routine qCTs.

Radiomic modeling to predict risk of vertebral compression fracture after stereotactic body radiation therapy for spinal metastases

OBJECTIVE

In the treatment of spinal metastases with stereotactic body radiation therapy (SBRT), vertebral compression fracture (VCF) is a common and potentially morbid complication. Better methods to identify patients at high risk of radiation-induced VCF are needed to evaluate prophylactic measures. Radiomic features from pretreatment imaging may be employed to more accurately predict VCF. The objective of this study was to develop and evaluate a machine learning model based on clinical characteristics and radiomic features from pretreatment imaging to predict the risk of VCF after SBRT for spinal metastases.

METHODS

Vertebral levels C2 through L5 containing metastases treated with SBRT were included if they were naive to prior surgery or radiation therapy, target delineation was based on consensus guidelines, and 1-year follow-up data were available. Clinical features, including characteristics of the patient, disease, and treatment, were obtained from chart review. Radiomic features were extracted from the planning target volume (PTV) on pretreatment CT and T1-weighted MRI. Clinical and radiomic features selected by least absolute shrinkage and selection operator (LASSO) regression were included in random forest classification models, which were trained to predict VCF within 1 year after SBRT. Model performance was assessed with leave-one-out cross-validation.

RESULTS

Within 1 year after SBRT, 15 of 95 vertebral levels included in the analysis demonstrated new or progressive VCF. Selected clinical features included BMI, performance status, total prescription dose, dose to 99% of the PTV, lumbar location, and 2 components of the Spine Instability Neoplastic Score (SINS): lytic tumor character and spinal misalignment. Selected radiomic features included 5 features from CT and 3 features from MRI. The best-performing classification model, derived from a combination of selected clinical and radiomic features, demonstrated a sensitivity of 0.844, specificity of 0.800, and area under the receiver operating characteristic (ROC) curve (AUC) of 0.878. This model was significantly more accurate than alternative models derived from only selected clinical features (AUC = 0.795, p = 0.048) or only components of the SINS (AUC = 0.579, p < 0.0001).

CONCLUSIONS

In the treatment of spinal metastases with SBRT, a machine learning model incorporating both clinical features and radiomic features from pretreatment imaging predicted VCF at 1 year after SBRT with excellent sensitivity and specificity, outperforming models developed from clinical features or components of the SINS alone. If validated, these findings may allow more judicious selection of patients for prophylactic interventions.

Radiation Versus Immune Checkpoint Inhibitor Associated Pneumonitis: Distinct Radiologic Morphologies

BACKGROUND

Patients with non-small cell lung cancer may develop pneumonitis after thoracic radiotherapy (RT) and immune checkpoint inhibitors (ICIs). We hypothesized that distinct morphologic features are associated with different pneumonitis etiologies.

MATERIALS AND METHODS

We systematically compared computed tomography (CT) features of RT- versus ICI-pneumonitis. Clinical and imaging features were tested for association with pneumonitis severity. Lastly, we constructed an exploratory radiomics-based machine learning (ML) model to discern pneumonitis etiology.

RESULTS

Between 2009 and 2019, 82 patients developed pneumonitis: 29 after thoracic RT, 23 after ICI, and 30 after RT + ICI. Fifty patients had grade 2 pneumonitis, 22 grade 3, and 7 grade 4. ICI-pneumonitis was more likely bilateral (65% vs. 28%; p = .01) and involved more lobes (66% vs. 45% involving at least three lobes) and was less likely to have sharp border (17% vs. 59%; p = .004) compared with RT-pneumonitis. Pneumonitis morphology after RT + ICI was heterogeneous, with 47% bilateral, 37% involving at least three lobes, and 40% sharp borders. Among all patients, risk factors for severe pneumonitis included poor performance status, smoking history, worse lung function, and bilateral and multifocal involvement on CT. An ML model based on seven radiomic features alone could distinguish ICI- from RT-pneumonitis with an area under the receiver-operating curve of 0.76 and identified the predominant etiology after RT + ICI concordant with multidisciplinary consensus.

CONCLUSION

RT- and ICI-pneumonitis exhibit distinct spatial features on CT. Bilateral and multifocal lung involvement is associated with severe pneumonitis. Integrating these morphologic features in the clinical management of patients who develop pneumonitis after RT and ICIs may improve treatment decision-making.

IMPLICATIONS FOR PRACTICE

Patients with non-small cell lung cancer often receive thoracic radiation and immune checkpoint inhibitors (ICIs), both of which can cause pneumonitis. This study identified similarities and differences in pneumonitis morphology on computed tomography (CT) scans among pneumonitis due to radiotherapy (RT) alone, ICI alone, and the combination of both. Patients who have bilateral CT changes involving at least three lobes are more likely to have ICI-pneumonitis, whereas those with unilateral CT changes with sharp borders are more likely to have radiation pneumonitis. After RT and/or ICI, severe pneumonitis is associated with bilateral and multifocal CT changes. These results can help guide clinicians in triaging patients who develop pneumonitis after radiation and during ICI treatment.

Predicting acute radiation induced xerostomia in head and neck Cancer using MR and CT Radiomics of parotid and submandibular glands

Purpose

To analyze baseline CT/MR-based image features of salivary glands to predict radiation-induced xerostomia 3-months after head-and-neck cancer (HNC) radiotherapy.

Methods

A retrospective analysis was performed on 266 HNC patients who were treated using radiotherapy at our institution between 2009 and 2018. CT and T1 post-contrast MR images along with NCI-CTCAE xerostomia grade (3-month follow-up) were prospectively collected at our institution. CT and MR images were registered on which parotid/submandibular glands were contoured. Image features were extracted for ipsilateral/contralateral parotid and submandibular glands relative to the location of the primary tumor. Dose-volume-histogram (DVH) parameters were also acquired. Features were pre-selected based on Spearman correlation before modelling by examining the correlation with xerostomia (p < 0.05). A shrinkage regression analysis of the pre-selected features was performed using LASSO. The internal validity of the variable selection was estimated by repeating the entire variable selection procedure using a leave-one-out-cross-validation. The most frequently selected variables were considered in the final model. A generalized linear regression with repeated ten-fold cross-validation was developed to predict radiation-induced xerostomia at 3-months after radiotherapy. This model was tested in an independent dataset (n = 50) of patients who were treated at the same institution in 2017–2018. We compared the prediction performances under eight conditions (DVH-only, CT-only, MR-only, CT + MR, DVH + CT, DVH + CT + MR, Clinical+CT + MR, and Clinical+DVH + CT + MR) using the area under the receiver operating characteristic curve (ROC-AUC).

Results

Among extracted features, 7 CT, 5 MR, and 2 DVH features were selected. The internal cohort (n = 216) ROC-AUC values for DVH, CT, MR, and Clinical+DVH + CT + MR features were 0.73 ± 0.01, 0.69 ± 0.01, 0.70 ± 0.01, and 0.79 ± 0.01, respectively. The validation cohort (n = 50) ROC-AUC values for DVH, CT, MR, and Clinical+DVH + CT + MR features were 0.63, 0.57, 0.66, and 0.68, respectively. The DVH-ROC was not significantly different than the CT-ROC (p = 0.8) or MR-ROC (p = 0.4). However, the CT + MR-ROC was significantly different than the CT-ROC (p = 0.03), but not the Clinical+DVH + CT + MR model (p = 0.5).

Conclusion

Our results suggest that baseline CT and MR image features may reflect baseline salivary gland function and potential risk for radiation injury. The integration of baseline image features into prediction models has the potential to improve xerostomia risk stratification with the ultimate goal of truly personalized HNC radiotherapy.

Electronic supplementary material

The online version of this article (10.1186/s13014-019-1339-4) contains supplementary material, which is available to authorized users.

Comparison of treatment planning approaches for spatially fractionated irradiation of deep tumors

Purpose

The purpose of this work was to compare the dosimetry and delivery times of 3D‐conformal (3DCRT)‐, volumetric modulated arc therapy (VMAT)‐, and tomotherapy‐based approaches for spatially fractionated radiation therapy for deep tumor targets.

Methods

Two virtual GRID phantoms were created consisting of 7 “target” cylinders (1‐cm diameter) aligned longitudinally along the tumor in a honey‐comb pattern, mimicking a conventional GRID block, with 2‐cm center‐to‐center spacing (GRID_2 cm) and 3‐cm center‐to‐center spacing (GRID_3 cm), all contained within a larger cylinder (8 and 10 cm in diameter for the GRID_2 cm and GRID_3 cm, respectively). In a single patient, a GRID_3 cm structure was created within the gross tumor volume (GTV). Tomotherapy, VMAT (6 MV + 6 MV‐flattening‐filter‐free) and multi‐leaf collimator segment 3DCRT (6 MV) plans were created using commercially available software. Two tomotherapy plans were created with field widths (TOMO_2.5 cm) 2.5 cm and (TOMO_5 cm) 5 cm. Prescriptions for all plans were set to deliver a mean dose of 15 Gy to the GRID targets in one fraction. The mean dose to the GRID target and the heterogeneity of the dose distribution (peak‐to‐valley and peak‐to‐edge dose ratios) inside the GRID target were obtained. The volume of normal tissue receiving 7.5 Gy was determined.

Results

The peak‐to‐valley ratios for GRID_2 cm/GRID_3 cm/Patient were 2.1/2.3/2.8, 1.7/1.5/2.8, 1.7/1.9/2.4, and 1.8/2.0/2.8 for the 3DCRT, VMAT, TOMO_5 cm, and TOMO_2.5 cm plans, respectively. The peak‐to‐edge ratios for GRID_2 cm/GRID_3 cm/Patient were 2.8/3.2/5.4, 2.1/1.8/5.4, 2.0/2.2/3.9, 2.1/2.7/5.2 and for the 3DCRT, VMAT, TOMO_5 cm, and TOMO_2.5 cm plans, respectively. The volume of normal tissue receiving 7.5 Gy was lowest in the TOMO_2.5 cm plan (GRID_2 cm/GRID_3 cm/Patient = 54 cm³/19 cm³/10 cm³). The VMAT plans had the lowest delivery times (GRID_2 cm/GRID_3 cm/Patient = 17 min/8 min/9 min).

Conclusion

Our results present, for the first time, preliminary evidence comparing IMRT‐GRID approaches which result in high‐dose “islands” within a target, mimicking what is achieved with a conventional GRID block but without high‐dose “tail” regions outside of the target. These approaches differ modestly in their ability to achieve high peak‐to‐edge ratios and also differ in delivery times.

Dose/Volume histogram patterns in Salivary Gland subvolumes influence xerostomia injury and recovery

Xerostomia is a common consequence of radiotherapy in head and neck cancer. The objective was to compare the regional radiation dose distribution in patients that developed xerostomia within 6 months of radiotherapy and those recovered from xerostomia within 18 months post-radiotherapy. We developed a feature generation pipeline to extract dose volume histogram features from geometrically defined ipsilateral/contralateral parotid glands, submandibular glands, and oral cavity surrogates for each patient. Permutation tests with multiple comparisons were performed to assess the dose difference between injury vs. non-injury and recovery vs. non-recovery. Ridge logistic regression models were applied to predict injury and recovery using clinical features along with dose features (D10-D90) of the subvolumes extracted from oral cavity and salivary gland contours + 3 mm peripheral shell. Model performances were assessed by the area under the receiver operating characteristic curve (AUC) using nested cross-validation. We found that different regional dose/volume metrics patterns exist for injury vs. recovery. Compared to injury, recovery has increased importance to the subvolumes receiving lower dose. Within the subvolumes, injury tends to have increased importance towards D10 from D90. This suggests that different threshold for xerostomia injury and recovery. Injury is induced by the subvolumes receiving higher dose, and the ability to recover can be preserved by further reducing the dose to subvolumes receiving lower dose.

Distinguishing True Progression From Radionecrosis After Stereotactic Radiation Therapy for Brain Metastases With Machine Learning and Radiomics

PURPOSE

Treatment effect or radiation necrosis after stereotactic radiosurgery (SRS) for brain metastases is a common phenomenon often indistinguishable from true progression. Radiomics is an emerging field that promises to improve on conventional imaging. In this study, we sought to apply a radiomics-based prediction model to the problem of diagnosing treatment effect after SRS.

METHODS AND MATERIALS

We included patients in the Johns Hopkins Health System who were treated with SRS for brain metastases who subsequently underwent resection for symptomatic growth. We also included cases of likely treatment effect in which lesions grew but subsequently regressed spontaneously. Lesions were segmented semiautomatically on preoperative T1 postcontrast and T2 fluid-attenuated inversion recovery magnetic resonance imaging, and radiomic features were extracted with software developed in-house. Top-performing features on univariate logistic regression were entered into a hybrid feature selection/classification model, IsoSVM, with parameter optimization and further feature selection performed using leave-one-out cross-validation. Final model performance was assessed by 10-fold cross-validation with 100 repeats. All cases were independently reviewed by a board-certified neuroradiologist for comparison.

RESULTS

We identified 82 treated lesions across 66 patients, with 77 lesions having pathologic confirmation. There were 51 radiomic features extracted per segmented lesion on each magnetic resonance imaging sequence. An optimized IsoSVM classifier based on top-ranked radiomic features had sensitivity and specificity of 65.38% and 86.67%, respectively, with an area under the curve of 0.81 on leave-one-out cross-validation. Only 73% of cases were classifiable by the neuroradiologist, with a sensitivity of 97% and specificity of 19%.

CONCLUSIONS

Radiomics holds promise for differentiating between treatment effect and true progression in brain metastases treated with SRS. A predictive model built on radiomic features from an institutional cohort performed well on cross-validation testing. These results warrant further validation in independent datasets. Such work could prove invaluable for guiding management of individual patients and assessing outcomes of novel interventions.

Development of a pulmonary imaging biomarker pipeline for phenotyping of chronic lung disease

We designed and generated pulmonary imaging biomarker pipelines to facilitate high-throughput research and point-of-care use in patients with chronic lung disease. Image processing modules and algorithm pipelines were embedded within a graphical user interface (based on the .NET framework) for pulmonary magnetic resonance imaging (MRI) and x-ray computed-tomography (CT) datasets. The software pipelines were generated using C++ and included: (1) inhaled He3/Xe129 MRI ventilation and apparent diffusion coefficients, (2) CT-MRI coregistration for lobar and segmental ventilation and perfusion measurements, (3) ultrashort echo-time H1 MRI proton density measurements, (4) free-breathing Fourier-decomposition H1 MRI ventilation/perfusion and free-breathing H1 MRI specific ventilation, (5) multivolume CT and MRI parametric response maps, and (6) MRI and CT texture analysis and radiomics. The image analysis framework was implemented on a desktop workstation/tablet to generate biomarkers of regional lung structure and function related to ventilation, perfusion, lung tissue texture, and integrity as well as multiparametric measures of gas trapping and airspace enlargement. All biomarkers were generated within 10 min with measurement reproducibility consistent with clinical and research requirements. The resultant pulmonary imaging biomarker pipeline provides real-time and automated lung imaging measurements for point-of-care and high-throughput research.

Pulmonary 3He Magnetic Resonance Imaging Biomarkers of Regional Airspace Enlargement in Alpha-1 Antitrypsin Deficiency

RATIONALE AND OBJECTIVES

Thoracic x-ray computed tomography (CT) and hyperpolarized ³He magnetic resonance imaging (MRI) provide quantitative measurements of airspace enlargement in patients with emphysema. For patients with panlobular emphysema due to alpha-1 antitrypsin deficiency (AATD), sensitive biomarkers of disease progression and response to therapy have been difficult to develop and exploit, especially those biomarkers that correlate with outcomes like quality of life. Here, our objective was to generate and compare CT and diffusion-weighted inhaled-gas MRI measurements of emphysema including apparent diffusion coefficient (ADC) and MRI-derived mean linear intercept (L_m) in patients with AATD, chronic obstructive pulmonary disease (COPD) ex-smokers, and elderly never-smokers.

MATERIALS AND METHODS

We enrolled patients with AATD (n = 8; 57 ± 7 years), ex-smokers with COPD (n = 8; 77 ± 6 years), and a control group of never-smokers (n = 5; 64 ± 2 years) who underwent thoracic CT, MRI, spirometry, plethysmography, the St. George's Respiratory Questionnaire, and the 6-minute walk test during a single 2-hour visit. MRI-derived ADC, L_m, surface-to-volume ratio, and ventilation defect percent were generated for the apical, basal, and whole lung as was CT lung area ≤-950 Hounsfield units (RA₉₅₀), low attenuating clusters, and airway count.

RESULTS

In patients with AATD, there was a significantly different MRI-derived ADC (P = .03), L_m (P < .0001), and surface-to-volume ratio (P < .0001), but not diffusing capacity of carbon monoxide, residual volume or total lung capacity, or CT RA₉₅₀ (P > .05) compared to COPD ex-smokers with a significantly different St. George's Respiratory Questionnaire.

CONCLUSIONS

In this proof-of-concept demonstration, we evaluated CT and MRI lung emphysema measurements and observed significantly worse MRI biomarkers of emphysema in patients with AATD compared to patients with COPD, although CT RA₉₅₀ and diffusing capacity of carbon monoxide were not significantly different, underscoring the sensitivity of MRI measurements of AATD emphysema.

Free-breathing Functional Pulmonary MRI: Response to Bronchodilator and Bronchoprovocation in Severe Asthma

RATIONALE AND OBJECTIVES

Ventilation heterogeneity is a hallmark feature of asthma. Our objective was to evaluate ventilation heterogeneity in patients with severe asthma, both pre- and post-salbutamol, as well as post-methacholine (MCh) challenge using the lung clearance index, free-breathing pulmonary ¹H magnetic resonance imaging (FDMRI), and inhaled-gas MRI ventilation defect percent (VDP).

MATERIALS AND METHODS

Sixteen severe asthmatics (49 ± 10 years) provided written informed consent to an ethics board-approved protocol. Spirometry, plethysmography, and multiple breath nitrogen washout to measure the lung clearance index were performed during a single visit within 15 minutes of MRI. Inhaled-gas MRI and FDMRI were performed pre- and post-bronchodilator to generate VDP. For asthmatics with forced expiratory volume in 1 second (FEV₁) >70%_predicted, MRI was also performed before and after MCh challenge. Wilcoxon signed-rank tests, Spearman correlations, and a repeated-measures analysis of variance were performed.

RESULTS

Hyperpolarized ³He (P = .02) and FDMRI (P = .02) VDP significantly improved post-salbutamol and for four asthmatics who could perform MCh (n = 4). ³He and FDMRI VDP significantly increased at the provocative concentration of MCh, resulting in a 20% decrease in FEV₁ (PC₂₀) and decreased post-bronchodilator (P = .02), with a significant difference between methods (P = .01). FDMRI VDP was moderately correlated with ³He VDP (ρ = .61, P = .01), but underestimated VDP relative to ³He VDP (-6 ± 9%). Whereas ³He MRI VDP was significantly correlated with the lung clearance index, FDMRI was not (ρ = .49, P = .06).

CONCLUSIONS

FDMRI VDP generated in free-breathing asthmatic patients was correlated with static inspiratory breath-hold ³He MRI VDP but underestimated VDP relative to ³He MRI VDP. Although less sensitive to salbutamol and MCh, FDMRI VDP may be considered for asthma patient evaluations at centers without inhaled-gas MRI.

Thoracic CT-MRI coregistration for regional pulmonary structure-function measurements of obstructive lung disease

PURPOSE

Recent pulmonary imaging research has revealed that in patients with chronic obstructive pulmonary disease (COPD) and asthma, structural and functional abnormalities are spatially heterogeneous. This novel information may help optimize treatment in individual patients, monitor interventional efficacy, and develop new treatments. Moreover, by automating the measurement of regional biomarkers for the 19 different anatomical lung segments, there is an opportunity to embed imaging biomarkers into clinically acceptable clinical workflows and improve lung disease clinical care. Therefore, to exploit the regional structure-function information provided by thoracic imaging, and as a first step toward this goal, our objective was to develop a fully automated registration pipeline for thoracic x-ray computed tomography (CT) and inhaled gas functional magnetic resonance imaging (MRI) whole lung and segmental structure-function biomarkers.

METHODS

Thirty-five patients including 15 severe, poorly controlled asthmatics and 20 COPD patients [classified according to the global initiative for chronic obstructive lung disease (GOLD) criteria)] provided written informed consent to a study protocol approved by Health Canada and underwent pulmonary function tests, MRI, and CT during a single 2-hour visit. Using this diverse patient dataset, we developed and evaluated a joint deformable registration approach to simultaneously coregister CT with both ¹ H and ³ He MRI by enforcing the similarity of the deformation fields from the two individual registrations. We derived a simpler model that was equivalent to the original challenging optimization problem through variational analysis and the simpler model gave rise to an efficient numerical solver that was parallelized on a graphics processing unit. The coregistered CT-³ He MRI and whole lung/segmental lung masks were used to generate whole lung and segmental ³ He MRI ventilation defect percent (VDP). To estimate fiducial localization reproducibility, a single observer manually identified 109 pairs of CT and ³ He MRI fiducials for 35 patient images on five separate occasions and determined the fiducial localization error (FLE). CT-³ He MRI registration accuracy was evaluated using the target registration error (TRE). Whole lung VDP generated using the algorithm was compared with VDP generated using a previously validated semiautomated approach and computational efficiency was evaluated using run time.

RESULTS

In 35 patients including 15 with severe asthma and 20 with COPD, mean forced expiratory volume in 1 s (FEV₁ ) was 63±24%_pred and FEV₁ /forced vital capacity (FVC) was 54 ± 17%. FLE was 0.16 mm and 0.34 mm for ³ He MRI and CT, respectively. TRE was 4.5 ± 2.0 mm, 4.0 ± 1.7 mm, 4.8 ± 2.3 mm for asthma, COPD GOLD II, and GOLD III groups, respectively, with a mean of 4.4 ± 2.0 mm for the entire dataset. TRE was significantly improved for joint CT-¹ H/³ He MRI registration compared with CT-¹ H MRI rigid registration (P < 0.0001). Whole lung VDP generated using the pipeline was not significantly different (P = 0.37) compared to a semiautomated method with which it was strongly correlated (r = 0.93, P < 0.0001). The fully automated pipeline required 11 ± 0.4 min to generate whole lung and segmental VDP.

CONCLUSIONS

For a diverse group of patients with COPD and asthma, whole lung and segmental VDP was measured using an automated lung image analysis pipeline which provides a way to incorporate lung functional biomarkers into clinical research and patient care.

Anatomical pulmonary magnetic resonance imaging segmentation for regional structure-function measurements of asthma

PURPOSE

Pulmonary magnetic-resonance-imaging (MRI) and x-ray computed-tomography have provided strong evidence of spatially and temporally persistent lung structure-function abnormalities in asthmatics. This has generated a shift in their understanding of lung disease and supports the use of imaging biomarkers as intermediate endpoints of asthma severity and control. In particular, pulmonary (1)H MRI can be used to provide quantitative lung structure-function measurements longitudinally and in response to treatment. However, to translate such biomarkers of asthma, robust methods are required to segment the lung from pulmonary (1)H MRI. Therefore, their objective was to develop a pulmonary (1)H MRI segmentation algorithm to provide regional measurements with the precision and speed required to support clinical studies.

METHODS

The authors developed a method to segment the left and right lung from (1)H MRI acquired in 20 asthmatics including five well-controlled and 15 severe poorly controlled participants who provided written informed consent to a study protocol approved by Health Canada. Same-day spirometry and plethysmography measurements of lung function and volume were acquired as well as (1)H MRI using a whole-body radiofrequency coil and fast spoiled gradient-recalled echo sequence at a fixed lung volume (functional residual capacity + 1 l). We incorporated the left-to-right lung volume proportion prior based on the Potts model and derived a volume-proportion preserved Potts model, which was approximated through convex relaxation and further represented by a dual volume-proportion preserved max-flow model. The max-flow model led to a linear problem with convex and linear equality constraints that implicitly encoded the proportion prior. To implement the algorithm, (1)H MRI was resampled into ∼3 × 3 × 3 mm(3) isotropic voxel space. Two observers placed seeds on each lung and on the background of 20 pulmonary (1)H MR images in a randomized dataset, on five occasions, five consecutive days in a row. Segmentation accuracy was evaluated using the Dice-similarity-coefficient (DSC) of the segmented thoracic cavity with comparison to five-rounds of manual segmentation by an expert observer. The authors also evaluated the root-mean-squared-error (RMSE) of the Euclidean distance between lung surfaces, the absolute, and percent volume error. Reproducibility was measured using the coefficient of variation (CoV) and intraclass correlation coefficient (ICC) for two observers who repeated segmentation measurements five-times.

RESULTS

For five well-controlled asthmatics, forced expiratory volume in 1 s (FEV1)/forced vital capacity (FVC) was 83% ± 7% and FEV1 was 86 ± 9%pred. For 15 severe, poorly controlled asthmatics, FEV1/FV C = 66% ± 17% and FEV1 = 72 ± 27%pred. The DSC for algorithm and manual segmentation was 91% ± 3%, 92% ± 2% and 91% ± 2% for the left, right, and whole lung, respectively. RMSE was 4.0 ± 1.0 mm for each of the left, right, and whole lung. The absolute (percent) volume errors were 0.1 l (∼6%) for each of right and left lung and ∼0.2 l (∼6%) for whole lung. Intra- and inter-CoV (ICC) were <0.5% (>0.91%) for DSC and <4.5% (>0.93%) for RMSE. While segmentation required 10 s including ∼6 s for user interaction, the smallest detectable difference was 0.24 l for algorithm measurements which was similar to manual measurements.

CONCLUSIONS

This lung segmentation approach provided the necessary and sufficient precision and accuracy required for research and clinical studies.

Pulmonary MRI morphometry modeling of airspace enlargement in chronic obstructive pulmonary disease and alpha-1 antitrypsin deficiency

PURPOSE

We generated lung morphometry measurements using single-breath diffusion-weighted MRI and three different acinar duct models in healthy participants and patients with emphysema stemming from chronic obstructive lung disease (COPD) and alpha-1 antitrypsin deficiency (AATD).

METHODS

Single-breath-inhaled ³ He MRI with five diffusion sensitizations (b-value = 0, 1.6, 3.2, 4.8, and 6.4 s/cm² ) was used, and signal intensities were fit using a cylindrical and single-compartment acinar-duct model to estimate MRI-derived mean linear intercept (L_m ) and surface-to-volume ratio (S/V). A stretched exponential model was also developed to estimate the mean airway length and L_m .

RESULTS

We evaluated 42 participants, including 15 elderly never-smokers (69 ± 5 years), 12 ex-smokers without COPD (67 ± 11 years), 9 COPD ex-smokers (80 ± 6 years), and 6 AATD patients (59 ± 6 years). In the never- and ex-smokers, the diffusing capacity of the lung for carbon monoxide (DL_CO ) and computed tomography relative area of less than -950 Hounsfield units (RA₉₅₀ ) were normal, but these were abnormal in the COPD and AATD patients, which is reflective of emphysema. Although cylindrical and stretched-exponential-model estimates of L_m and S/V were not significantly different, the single-compartment-model estimates were significantly different (P < 0.05) for the never- and ex-smoker subgroups. All models estimated significantly worse L_m and S/V in the AATD and COPD subgroups compared with the never- and ex-smokers without emphysema.

CONCLUSIONS

Differences in airspace enlargement may be estimated using L_m and S/V, generated using MRI and a stretched-exponential or cylindrical model of the acinar ducts. Magn Reson Med 79:439-448, 2018. © 2017 International Society for Magnetic Resonance in Medicine.

Methods and Problems of a Stroke Rehabilitation Trial

There are conflicting views on the effectiveness of rehabilitation after stroke; several studies have attempted to compare progress following intensive therapy with that following community care1–6 or no formal treatment,7–9 but for a number of reasons the evidence is inconclusive. Consequently, we have been engaged in a randomised controlled trial of the effectiveness of different intensities of out-patient rehabilitation following stroke. This paper outlines the design, methods and problems of the study.

Ultrashort echo time MRI biomarkers of asthma

PURPOSE

To develop and assess ultrashort echo-time (UTE) magnetic resonance imaging (MRI) biomarkers of lung function in asthma patients.

MATERIALS AND METHODS

Thirty participants including 13 healthy volunteers and 17 asthmatics provided written informed consent to UTE and pulmonary function tests in addition to hyperpolarized-noble-gas 3T MRI and computed tomography (CT) for asthmatics only. The difference in MRI signal-intensity (SI) across four lung volumes (full-expiration, functional-residual-capacity [FRC], FRC+1L, and full-inspiration) was determined on a voxel-by-voxel basis to generate dynamic proton-density (DPD) maps. MRI ventilation-defect-percent (VDP), UTE SI, and DPD values as well as CT radiodensity were determined for whole lung and individual lobes.

RESULTS

Mean SI at full-expiration (P < 0.01), FRC (P < 0.05), and DPD (P < 0.01) were greater in healthy volunteers compared to asthmatics. In asthmatics, UTE SI at full-expiration and DPD were correlated with FEV₁ /FVC (SI r = 0.73/P = 0.002; DPD r = 0.75/P = 0.003), RV/TLC (SI r = -0.57/P = 0.02), or RV (DPD r = -0.62/P = 0.02), CT radiodensity (SI r = 0.83/P = 0.006; DPD r = 0.71/P = 0.01), and lobar VDP (SI r_s  = -0.33/P = 0.02; DPD r_s  = -0.47/P = 0.01).

CONCLUSION

In patients with asthma, UTE SI and dynamic proton-density were related to pulmonary function measurements, whole lung and lobar VDP, as well as CT radiodensity. Thus, UTE MRI biomarkers may reflect ventilation heterogeneity and/or gas-trapping in asthmatics using conventional equipment, making this approach potentially amenable for clinical use.

LEVEL OF EVIDENCE

2 J. Magn. Reson. Imaging 2017;45:1204-1215.

Ventilation Heterogeneity in Never-smokers and COPD:: Comparison of Pulmonary Functional Magnetic Resonance Imaging with the Poorly Communicating Fraction Derived From Plethysmography

RATIONALE AND OBJECTIVES

Pulmonary functional magnetic resonance imaging provides a way to quantify ventilation and its heterogeneity-a hallmark finding in chronic obstructive pulmonary disease (COPD). Unfortunately, the etiology and physiological meaning of ventilation defects and their relationship to pulmonary function and symptoms in COPD are not well understood. Another biomarker of ventilation heterogeneity is provided by the "poorly communicating fraction" (PCF), and is calculated as the ratio of total lung capacity to alveolar volume made using whole-body plethysmography. Our objective was to compare ventilation heterogeneity using hyperpolarized (3)He magnetic resonance imaging (MRI) and PCF measurements in elderly never-smokers and in ex-smokers with COPD.

MATERIALS AND METHODS

One hundred forty-six participants (71 ± 8 years, range = 48-87 years) provided written informed consent including 45 elderly never-smokers (71 ± 6 years, range = 61-84 years) and 101 ex-smokers with COPD (71 ± 8 years, range = 48-87 years). During a single 2-hour visit, spirometry, plethysmography, and hyperpolarized (3)He MRI were acquired. The MRI-derived ventilation defect percent (VDP) and plethysmography measurements were acquired and PCF values were calculated. Linear regression, Pearson correlations, and Bland-Altman analysis were used to evaluate the relationships for PCF and MRI VDP.

RESULTS

PCF (P < 0.001) and VDP (P < 0.001) were significantly increased with increasing COPD severity. There was a significant relationship for VDP and PCF (r = 0.68, P < 0.001) in all subjects and COPD subjects alone (r = 0.61, P < 0.001). Bland-Altman analysis showed that PCF and VDP were significantly different (mean bias = 9.7, upper limit = 32, lower limit = -13, P < 0.001), and in severe-grade COPD, PCF overestimates of VDP were significantly greater.

CONCLUSIONS

In elderly never-smokers and in ex-smokers with COPD, PCF and VDP are moderately correlated estimates of COPD ventilation heterogeneity that may be reflecting similar pathophysiology.

Noninvasive quantification of alveolar morphometry in elderly never- and ex-smokers

Diffusion-weighted magnetic resonance imaging (MRI) provides a way to generate in vivo lung images with contrast sensitive to the molecular displacement of inhaled gas at subcellular length scales. Here, we aimed to evaluate hyperpolarized ³He MRI estimates of the alveolar dimensions in 38 healthy elderly never-smokers (73 ± 6 years, 15 males) and 21 elderly ex-smokers (70 ± 10 years, 14 males) with (n = 8, 77 ± 6 years) and without emphysema (n = 13, 65 ± 10 years). The ex-smoker and never-smoker subgroups were significantly different for FEV₁/FVC (P = 0.0001) and DL_CO (P = 0.009); while ex-smokers with emphysema reported significantly diminished FEV₁/FVC (P = 0.02) and a trend toward lower DL_CO (P = 0.05) than ex-smokers without emphysema. MRI apparent diffusion coefficients (ADC) and CT measurements of emphysema (relative area–CT density histogram, RA₉₅₀) were significantly different (P = 0.001 and P = 0.007) for never-smoker and ex-smoker subgroups. In never-smokers, the MRI estimate of mean linear intercept (260 ± 27 μm) was significantly elevated as compared to the results previously reported in younger never-smokers (210 ± 30 μm), and trended smaller than in the age-matched ex-smokers (320 ± 72 μm, P = 0.06) evaluated here. Never-smokers also reported significantly smaller internal (220 ± 24 μm, P = 0.01) acinar radius but greater alveolar sheath thickness (120 ± 4 μm, P < 0.0001) than ex-smokers. Never-smokers were also significantly different than ex-smokers without emphysema for alveolar sheath thickness but not ADC, while ex-smokers with emphysema reported significantly different ADC but not alveolar sheath thickness compared to ex-smokers without CT evidence of emphysema. Differences in alveolar measurements in never- and ex-smokers demonstrate the sensitivity of MRI measurements to the different effects of smoking and aging on acinar morphometry.

Oscillatory Positive Expiratory Pressure in Chronic Obstructive Pulmonary Disease

Evidence-based guidance for the use of airway clearance techniques (ACT) in chronic obstructive pulmonary disease (COPD) is lacking in-part because well-established measurements of pulmonary function such as the forced expiratory volume in 1s (FEV1) are relatively insensitive to ACT. The objective of this crossover study was to evaluate daily use of an oscillatory positive expiratory pressure (oPEP) device for 21-28 days in COPD patients who were self-identified as sputum-producers or non-sputum-producers. COPD volunteers provided written informed consent to daily oPEP use in a randomized crossover fashion. Participants completed baseline, crossover and study-end pulmonary function tests, St. George's Respiratory Questionnaire (SGRQ), Patient Evaluation Questionnaire (PEQ), Six-Minute Walk Test and (3)He magnetic resonance imaging (MRI) for the measurement of ventilation abnormalities using the ventilation defect percent (VDP). Fourteen COPD patients, self-identified as sputum-producers and 13 COPD-non-sputum-producers completed the study. Post-oPEP, the PEQ-ease-bringing-up-sputum was improved for sputum-producers (p = 0.005) and non-sputum-producers (p = 0.04), the magnitude of which was greater for sputum-producers (p = 0.03). There were significant post-oPEP improvements for sputum-producers only for FVC (p = 0.01), 6MWD (p = 0.04), SGRQ total score (p = 0.01) as well as PEQ-patient-global-assessment (p = 0.02). Clinically relevant post-oPEP improvements for PEQ-ease-bringing-up-sputum/PEQ-patient-global-assessment/SGRQ/VDP were observed in 8/7/9/6 of 14 sputum-producers and 2/0/3/3 of 13 non-sputum-producers. The post-oPEP change in (3)He MRI VDP was related to the change in PEQ-ease-bringing-up-sputum (r = 0.65, p = 0.0004) and FEV1 (r = -0.50, p = 0.009). In COPD patients with chronic sputum production, PEQ and SGRQ scores, FVC and 6MWD improved post-oPEP. FEV1 and PEQ-ease-bringing-up-sputum improvements were related to improved ventilation providing mechanistic evidence to support oPEP use in COPD. Clinical Trials # NCT02282189 and NCT02282202.

Globally optimal co-segmentation of three-dimensional pulmonary ¹H and hyperpolarized ³He MRI with spatial consistence prior

Pulmonary imaging using hyperpolarized (3)He/(129)Xe gas is emerging as a new way to understand the regional nature of pulmonary ventilation abnormalities in obstructive lung diseases. However, the quantitative information derived is completely dependent on robust methods to segment both functional and structural/anatomical data. Here, we propose an approach to jointly segment the lung cavity from (1)H and (3)He pulmonary magnetic resonance images (MRI) by constraining the spatial consistency of the two segmentation regions, which simultaneously employs the image features from both modalities. We formulated the proposed co-segmentation problem as a coupled continuous min-cut model and showed that this combinatorial optimization problem can be solved globally and exactly by means of convex relaxation. In particular, we introduced a dual coupled continuous max-flow model to study the convex relaxed coupled continuous min-cut model under a primal and dual perspective. This gave rise to an efficient duality-based convex optimization algorithm. We implemented the proposed algorithm in parallel using general-purpose programming on graphics processing unit (GPGPU), which substantially increased its computational efficiency. Our experiments explored a clinical dataset of 25 subjects with chronic obstructive pulmonary disease (COPD) across a wide range of disease severity. The results showed that the proposed co-segmentation approach yielded superior performance compared to single-channel image segmentation in terms of precision, accuracy and robustness.