Three-dimensional (3D) phase-resolved functional lung (PREFUL) is a functional magnetic resonance imaging (MRI) technique that allows for quantification of regional ventilation of the whole human lung volume, using tidal breathing and contrast-agent free acquisition for 8 min. Here, we present an MR protocol to collect and analyze 3D PREFUL imaging data.
Pulmonary magnetic resonance imaging (MRI) offers a variety of radiation-free techniques tailored to assess regional lung ventilation or its surrogates. These techniques encompass direct measurements, exemplified by hyperpolarized gas MRI and fluorinated gas MRI, as well as indirect measurements facilitated by oxygen-enhanced MRI and proton-based Fourier decomposition (FD) MRI. In recent times, there has been substantial progress in the field of FD MRI, which involved improving spatial/temporal resolution, refining sequence design and postprocessing, and developing a comprehensive whole-lung approach.
The two-dimensional (2D) phase-resolved functional lung (PREFUL) MRI stands out as an FD-based approach developed for the comprehensive assessment of regional ventilation and perfusion dynamics, all within a single MR acquisition. Recently, a new advancement has been made with the development of 3D PREFUL to assess dynamic ventilation of the entire lung using 8 min exam with a self-gated sequence.
The 3D PREFUL acquisition involves employing a stack-of-stars spoiled-gradient-echo sequence with a golden angle increment. Following the compressed sensing image reconstruction of approximately 40 breathing phases, all the reconstructed respiratory-resolved images undergo registration onto a fixed breathing phase. Subsequently, the ventilation parameters are extracted from the registered images.
In a study cohort comprising healthy volunteers and patients with chronic obstructive pulmonary disease, the 3D PREFUL ventilation parameters demonstrated strong correlations with measurements obtained from pulmonary function tests. Additionally, the interscan repeatability of the 3D PREFUL technique was deemed to be acceptable, indicating its reliability for repeated assessments of the same individuals.
In summary, 3D PREFUL ventilation MRI provides a whole lung coverage and captures ventilation dynamics with enhanced spatial resolution compared to 2D PREFUL. 3D PREFUL technique offers a cost-effective alternative to hyperpolarized 129Xe MRI, making it an attractive option for patient-friendly evaluation of pulmonary ventilation.
According to the World Health Organization (WHO), chronic respiratory diseases are the third leading cause of death, placing a significant burden on healthcare1. The diagnosis and monitoring of several respiratory diseases involve pulmonary functional tests, which lack spatial information and may not be feasible in young patients. Multiple other nuclear medicine modalities provide spatial measures of ventilation, such as positron emission tomography (PET)2, single-photon emission computed tomography (SPECT)3, or computed tomography4. A significant drawback of these techniques is the usage of ionizing radiation for imaging. Therefore, those measurements are not feasible in sensitive populations like children or for repeated measurements in clinical studies.
Magnetic resonance imaging methods gained interest in the last two decades because they offer radiation free measurement of pulmonary function. Above all, hyperpolarized gas MRI techniques5 have been used for the assessment of pulmonary ventilation in healthy controls and patients with pulmonary disease while achieving a superior signal-to-noise (SNR) ratio when compared to 19F6 or oxygen-enhanced MRI7. The common disadvantages of gas MRI techniques include the requirement for additional personnel, hardware costs, or the need for patient cooperation during the breathold measurement. To address these limitations, methods based on Fourier Decomposition (FD) MRI8 have been introduced for evaluating pulmonary ventilation and perfusion. As an alternative to FD MRI, a phase-resolved functional lung (PREFUL) MRI9 was developed, validated10,11,12, and tested in multiple studies, including multicentre studies13,14. Nevertheless, the 2D acquisition results in long acquisition times for complete lung coverage and limited spatial resolution. An elegant solution is offered by self-gated 3D acquisitions, albeit at the expense of the perfusion information15,16,17. One of these techniques, 3D PREFUL MRI17, has been developed not only to assess static ventilation but also ventilation dynamics during an 8 min MR exam, providing superior image resolution when compared to 2D methods.
Following the original publication describing the method17, the technique's repeatability has been assessed and compared to clinically the well-established spirometric measurements18, and more recently, an accelerated image registration framework has been introduced while improving the repeatability of ventilation parameters and preserving the image quality of ventilation maps19. This patient-friendly and easily scalable technique, capable of quantitatively mapping regional ventilation in the human lung, has the potential to significantly contribute to early detection, diagnosis of respiratory disease, or follow-up monitoring of therapy effects20. These treatment changes in regional lung function, such as improved ventilation volume, have the potential to become biomarkers for assessing the impact of therapies and could be a useful tool in multicenter clinical studies.
The article aims to provide a comprehensive and visual explanation of the 3D PREFUL technology, with the goal of fostering a wider translation of this technique.
This study received approval from the ethics committee of Hannover Medical School, ensuring that it adhered to rigorous ethical standards throughout the research process. The investigation strictly followed the guidelines outlined in the Declaration of Helsinki, emphasizing the ethical conduct of medical research. Additionally, informed consent was diligently obtained from all study participants (or their parents or legal guardians) before their participation in the MRI examination.
NOTE: The inclusion/exclusion criteria for studies involving 3D PREFUL MRI vary and depend on the study design. Typically, inclusion criteria for healthy volunteers in a 3D PREFUL MRI examination involve individuals who (a) are free from any significant medical conditions that could affect the study outcomes, (b) are not taking any medications that may interfere with the MRI results, (c) have no claustrophobia, anxiety, metallic implants or devices, (d) meet specific age and gender criteria relevant to the study objectives, (e) have no smoking history, (f) have normal findings of spirometry (FEV1/FVC > 0.7 and both FEV1 and FVC > 80% of predictive value). Exclusion criteria for all study participants undergoing a 3D PREFUL MRI examination may include: (a) pregnancy, (b) have any condition that may compromise the safety or validity of the 3D PREFUL MRI examination, (c) intolerance to gadolinium-based contrast agents when dynamic contrast-enhanced MRI in included in a study protocol.
1. Pre-MRI interview
2. Preparation of the radiographer for the MR exam
3. Preparing the subject for the MR exam
4. MRI exam
5. Raw data transfer
6. Data analysis and postprocessing
The simplest 3D PREFUL imaging protocol includes two sequences. One anatomical localizer + one free breathing measurement using golden-angle 3D stack-of-stars acquisition. The stack-of-stars trajectory is shown in Figure 1. This trajectory combines radial sampling (in-plane) with linear cartesian sampling (in z-direction). Initially, samples in the kz partition direction are acquired, followed by the rotation of the golden angle to sample the kx-ky plane. The golden angle acquisition scheme promotes self-gating with uniform coverage of k-space, facilitating image reconstruction with heavily undersampled data.The whole exam does not exceed 10 minutes. Further details of the 3D PREFUL acquisition are placed in the Table of Materials.
Figure 1: Golden ratio rotated stack of stars trajectory. Note radial sampling applied along in-plane dimension (kx-ky) and Cartesian sampling applied along slice direction (kz). This scheme results in cylindrical coverage of the acquired k-space. In this example, three partitions, including 32 radial projections per partition, are depicted. Please click here to view a larger version of this figure.
A list of typical MRI parameters used for 3D PREFUL imaging on 1.5 T scanner is presented in Table 1B. For the 3T scanner, the sequence parameters listed in Table 2 are proposed.
After the data acquisition and data transfer are completed, the image reconstruction part starts. This step is done by a MATLAB code that belongs to the intellectual property (IP) of a company (BioVisioneers GmbH) and cannot be shared openly. The script is fully automated once the path to the raw data is provided. A schematic overview of the reconstruction procedure is depicted in Figure 2.
Figure 2: Schematic overview of the 3D Phase Resolved Functional Lung (PREFUL) MRI method. At first, the data is acquired using an 8-minute-long free-breathing MR acquisition with a stack-of-stars trajectory. After the data is transferred from MR scanner to a computing unit, low-resolution 3D images with a temporal resolution of approximately 100 ms are reconstructed. The lung parenchyma of each image is segmented, and the lung volume is computed. The lung volume information is further used as a gating signal. Based on the amplitude and phase of the gating signal, the radial projections are sorted into respiratory bins that cover one respiratory cycle. The binning part is followed by the dynamic reconstruction of full-resolution images, which are subsequently registered to the end-inspiratory level. After several postprocessing steps, the regional ventilation (RVent) cycle is calculated, and the image analysis part, including parameter extraction, is performed. The RVent cycle is assessed by calculating the flow-volume loop (FVL) for each voxel. The FVL-CM ventilation maps are extracted through an assessment of each voxel FVL against healthy reference FVL, with similarity being evaluated using cross-correlation metric. For both RVent and FVL-CM, the global total ventilation defect percentage (VDP) values quantified. Furthermore, the dynamics of the RVent cycle is analyzed through time-to-peak analysis, resulting in a ventilation time-to-peak (VTTP) parameter map. Additionally, the deviation of the expected peak ventilation at 50% of the RVent cycle is quantified in the VTTPDev map. Please click here to view a larger version of this figure.
After the image reconstruction procedure, all images are spatially aligned to a single fixed state. The registration itself is performed on CPU using Advanced Normalization Tools (ANTs27) or Forsberg registration package28. While the ANTs package remains a gold standard for image registration tasks in MRI and CT imaging, the Forsberg registration facilitates a 10-fold faster registration procedure up to 9 minutes with comparable results19. The registration package might be chosen depending on the user priorities and available computing units. After the registration is done, the registered morphological images are again reinverted so that the lung appears dark in the grayscale image.
In Figure 3, the next step of the pipeline, which involves lung parenchyma segmentation, is depicted. First, the lung parenchyma is segmented from the end-inspiratory image using a convolutional neuronal network with nnUnet architecture29. The lung parenchyma segmentation is followed by a vessel recognition30, which is excluded from the final segmentation mask.
Figure 3: Exemplary results of deep learning-based segmentation for a 32-year-old male subject. The top row displays morphological images of eight representative coronal slices. In the second and third rows, one can observe the corresponding lung parenchyma mask and the final mask with the exclusion of vessels, respectively. Please click here to view a larger version of this figure.
An example of incorrect lung parenchyma segmentation is depicted in Figure 4. It is essential to visually inspect the deep-learning-based segmentations, and if deemed unsatisfactory, manual corrections should be considered to enhance the accuracy of the final lung parenchyma mask.
Figure 4: Exemplary results of incorrect deep learning-based segmentation for a 57-year-old male subject. The top row displays morphological images of eight representative coronal slices. In the second and third rows, one can observe the corresponding lung parenchyma mask and the final mask with the exclusion of vessels, respectively. As evident, several fibrotic regions are erroneously recognized as vessels or non-pulmonary structures. These inaccuracies have been corrected manually, as demonstrated in the fourth row. Please click here to view a larger version of this figure.
After several filtering steps outlined in the protocol, ventilation surrogates are calculated. 3D PREFUL MRI generates quantitative maps for static regional ventilation (RVent), dynamic flow-volume correlation metrics (FVL-CM), and two parameters based on ventilation time-to-peak (VTTP) analysis, as illustrated in Figure 5. This figure presents eight coronal slices of a 32-year-old healthy male. Note the expected homogenous distribution of all ventilation parameters.
Figure 5: 3D PREFUL MRI of a healthy volunteer (32-year-old male). Representative morphological (top row) and 3D PREFUL MRI ventilation parameters (second to fifth row) maps for a healthy volunteer (32-year-old male). The static regional ventilation is represented by regional ventilation (RVent), while the ventilation dynamics is assessed using the flow-volume loop correlation metric (FVL-CM), ventilation time-to-peak (VTTP), and deviation of VTTP (VTTPDev) represent ventilation parameters assessing the ventilation dynamics. As expected, homogenous ventilation values are observed for all ventilation parameter maps. Please click here to view a larger version of this figure.
To simplify the ventilation maps, ventilation defect (VD) maps are derived for RVent and FVL-CM, which enable faster interpretation of the results. The exemplary VD maps are presented in Figure 6. For both VDRVent and VDFVL-CM, the VDP values were found to be 3.6% and 3.0%, respectively, falling within the healthy normal range. Ideally, and depending on the age of the healthy volunteers, the VDP value should not exceed 10%. The above-mentioned parameters (RVent, FVL-CM, and their VD maps) were validated in several studies11,12,35,36 and are sensitive in the detection of disease as well as in the detection of therapy-induced effects20,34,37,38,39.
Figure 6: Representative RVent and FVL-CM maps, including their ventilation defect maps. The RVent (top row) and FVL-CM (second row) maps, including their ventilation defect maps (third and fourth row) derived by 3D PREFUL MRI for a healthy volunteer (32-year-old male), are depicted. In VD maps, healthy regions are green, and ventilation-deficit areas are marked in red. Please click here to view a larger version of this figure.
Several pulmonary MRI studies have been reported at both 1.5 T and 3 T magnetic field strengths. While there is the theoretical advantage of 3T due to increased signal-to-noise ratio (SNR), this advantage may be outweighed by more pronounced magnetic susceptibility effects at 3T. The exact influence of magnetic field strengths on the image quality of 3D PREFUL ventilation maps is currently unknown. Here, we present feasibility results (Figure 7) obtained for a healthy volunteer (35-year-old male) using a 3 T MR scanner. Note the more heterogeneous appearance of ventilation parameters at 3 T when compared to 1.5T (Figure 5).
Figure 7: 3D PREFUL MRI of a healthy volunteer (35-year-old male). Representative morphological (top row) and 3D PREFUL MRI ventilation parameters (second to fifth row) maps for a healthy volunteer (35-year-old male) at 3T. The static regional ventilation is represented by regional ventilation (RVent), while the ventilation dynamics is assessed using the flow-volume loop correlation metric (FVL-CM), ventilation time-to-peak (VTTP), and deviation of VTTP (VTTPDev) represent ventilation parameters assessing the ventilation dynamics. Please click here to view a larger version of this figure.
Comparison/validation of 3D PREFUL derived with more direct measurements is currently not published. There are several studies reporting a positive correlation between VDP values and spatial overlap of ventilation defects between 2D PREFUL technique and 129Xe11,12,14 and 19F MRI35. A recent comparison between 3D PREFUL-derived ventilation and direct ventilation measurements using 19F MRI in patients with chronic obstructive pulmonary disease (COPD), asthma, and healthy volunteers revealed a moderate to strong correlation at a global level40. Presented is an example comparison (Figure 8) for a patient diagnosed with chronic obstructive lung disease (54-year-old female, FEV1 = 42% predicted value), which was examined using 3D PREFUL and 19F wash-in MRI.
Figure 8: 3D PREFUL MRI of a 54-year-old female COPD patient. Morphological images (top row), ventilation parameters, and respective ventilation defect maps of a 54-year-old female COPD patient (FEV1 = 42 % pred., FVC = 102% pred.) obtained via 3D PREFUL MRI (2nd and 3rd row) and 19F MRI (4th and 5th row) are presented, along with a comparison of ventilation defect maps from both methods (last row). Overall, across all slices, the Sørensen-Dice coefficient was 25.5% in defective areas and 80.6% in healthy regions, resulting in a total spatial overlap of 69.2%. Notably, there's a visual correlation between matched ventilation areas in healthy and defective regions (depicted in dark green). Please click here to view a larger version of this figure.
3D PREFUL MRI can be used to measure regional responses to therapy20. As an example, Figure 9 shows a comparison of three slices from the lung of a cystic fibrosis subject (43-year-old female) at baseline (on the left,FEV1 = 94% predicted value) and after CFTR-modulator therapy (on the right, FEV1 = 112% predicted value). Because both measurements are spatially matched, 3D PREFUL enables regional treatment response analysis, as illustrated at the bottom of Figure 9. Please note the increased values of the flow-volume-loop correlation metric as well as the reduction in ventilation defects after therapy.
Figure 9: 3D PREFUL measurements of a 43-year-old female cystic fibrosis (CF) patient. Exemplary ventilation marker maps of baseline (left) and post-treatment (right) 3D PREFUL measurements of a 43-year-old female CF patient. VDPFVL-CM decreased from 18.0% (baseline) to 3.6% (post-treatment). FEV1 % pred. baseline: 94%, FEV1 % pred. post-treatment: 112%. LCI baseline: 10.48, LCI post-treatment: 9.39. The corresponding treatment response maps are depicted at the bottom. Please note the green areas indicating the resolved ventilation after the therapy. Please click here to view a larger version of this figure.
Table 1: List of typical MRI parameters used for localizer and 3D PREFUL acquisition on a 1.5T scanner. (A) Localizer and (B) 3D PREFUL acquisition. Please click here to download this Table.
Table 2: Proposed MRI parameters for 3D PREFUL acquisition on a 3T scanner. Please click here to download this Table.
Table 3: Exemplary statistical report of 3D PREFUL Parameters for a healthy volunteer (35-year-old male) Please click here to download this Table.
One of the most critical pitfalls is the acquisition itself. Care must be taken to ensure that the MR sequence parameters are set properly. Importantly, echo time, repetition time, number of slices, matrix size, and pixel bandwidth are the crucial parameters. Deviations from the recommended settings may result in insufficient temporal resolution or prolonged acquisitions. Also, data transfer is important to ensure that the data are completely delivered to the selected computer or server for evaluation. The data evaluation proceeds entirely automatically once the path to the acquired data is set. Postprocessing commences with image reconstruction, where retrospective binning plays a crucial role. Verifying the binning signal is recommended to ensure a clear depiction of the breathing pattern.After inspecting the binning signal, no additional steps are necessary, as the algorithm itself handles poor breathing patterns by excluding outliers (data points above the 95th percentile and below the 10th percentile). After image reconstruction, the process of image registration occurs. This step should be verified as follows: the registered images should be free from breathing and cardiac motion. Specifically, the positions of the diaphragm and heart should be 'frozen'. The next step involves deep-learning-based segmentation of lung parenchyma and vessels. This segmentation process is highly time-consuming for the entire 3D lung volume; hence, the use of AI-based tools is beneficial. However, in certain situations, such as the occurrence of peripheral consolidations, these algorithms may not yield accurate results. Consequently, the segmentation mask should be thoroughly checked and manually corrected if necessary. All of these steps are critical for evaluating the ventilation parameters and may result in inaccurate calculations.
Since the original publication17, several developments have been made in the 3D PREFUL technique. The sorting algorithm of the acquired radial projections has been fully automated and improved so that the whole lung is segmented using Otsu's thresholding method and used as a navigator signal for binning data into respiratory phases. Further, the manual lung parenchyma and vessel segmentation have been replaced by fully automatic deep-learning-based algorithms to reduce time-consuming procedures. Additionally, the processing time of the image registration part might be reduced up to 10 minutes if the Forsberg registration toolbox is implemented19. Along with previous developments, the implemented code enables to coregister images of two-time points (follow-up measurements) and evaluate the voxelwise progression of the disease or therapy-induced ventilation changes20. The current version of the Matlab script does not require any user interaction and could be implemented in a pipeline for the automated processing of data with specific flags in the future.
The main disadvantage of 3D PREFUL is that no local blood flow measurement is feasible since the inflow effect cannot be captured when using a nonselective hard RF pulse for excitation. Secondly, currently, the whole postprocessing (including data transfer, image reconstruction, image registration, and ventilation analysis) requires 2 hours for each subject. Thirdly, currently, only a preliminary comparison of 3D PREFUL-derived ventilation parameters to more validated techniques was demonstrated40. Fourthly, the evaluation of acquired data is performed on a high-performance computer; therefore, the results are not directly available to the physician at the console. Fifthly, feasibility studies of 3D PREFUL on different field strengths and various scanner vendors are lacking.
3D Phase Resolved Functional Lung (PREFUL) MRI allows quantitative mapping of regional ventilation dynamics of the whole human lung during free breathing17. There are several cost-effective alternatives to 3D PREFUL, such as spirometry or multiple breath washout techniques; however, both lack spatial information and require breathing maneuvers, and they are therefore not feasible for young children or neonates. Alternative imaging methods expose patients to ionizing radiation, such as nuclear medicine methods CT, or are not widely available (hyperpolarized 129Xe or 19F gas imaging using MRI). 3D PREFUL provides an assessment of ventilation dynamics, can be performed using a standard clinical scanner, and does not require contrast agents, making it attractive to be translated to a clinical research setting. The fact that 3D PREFUL does not require the use of radiation or contrast agents and offers a patient-friendly, free-breathing acquisition makes it well-suited for repeat or longitudinal studies that quantitatively evaluate regional responses to therapy. Secondly, the 3D PREFUL assessment of lung ventilation may be especially valuable in young children, where radiation exposure should be avoided.
The capability of 3D PREFUL to generate functional ventilation images of the human lung holds the potential for early diagnosis. Specifically, the ability to produce repeatable and quantitative regional ventilation maps allows for longitudinal studies, e.g., disease progression or monitoring of therapy effects. Moreover, the regional information on ventilation dynamics can provide greater sensitivity to early changes related to lung disease that are not detectable by pulmonary function tests.
The authors have nothing to disclose.
This work was funded by the German Center for Lung Research (DZL). The authors profited from a number of beneficial discussions with colleagues, in particular with Marcel Gutberlet, Agilo Kern, Lea Behrendt, Arnd Obert, Julian Glandorf, Till Kaireit, Tawfik Moher Alsady, Gesa Pöhler, Maximilian Zubke, Robin Müller, Marius Wernz, Cristian Crisosto, Milan Speth, Julienne Scheller, and Sonja Lüdiger. The authors acknowledge Robert Grimm (Siemens Healthineers) for writing the MR imaging sequence, and further, the authors would also like to thank Frank Schröder and Sven Thiele from the Department of Radiology (Hannover Medical School) for outstanding technical assistance in performing the MRI examinations.
ANTs | Open source medical image analysis research community | Image registration toolbox | |
BART | Open source imaging | https://www.opensourceimaging.org/project/berkeley-advanced-reconstruction-toolbox-bart/ | Image reconstruction toolbox |
Body coil | Siemens / Philips / GE | https://www.siemens-healthineers.com/en-us/magnetic-resonance-imaging/options-and-upgrades/coils | At least 6-channel body coil; Philips [https://www.usa.philips.com/healthcare/solutions/magnetic-resonance/coils-overview ] |
Forsberg | Image registration toolbox | https://github.com/fordanic/image-registration | Image registration toolbox |
Image reconstruction code | BioVisioneers GmbH | www.biovisioneers.com | Software for the MR reconstruction and analysis |
Matlab | Mathworks | Software | |
MRI | Siemens / Philips / GE | https://www.siemens-healthineers.com/magnetic-resonance-imaging | Philips [https://www.philips.co.in/healthcare/solutions/magnetic-resonance], GE [https://www.gehealthcare.com/products/magnetic-resonance-imaging]; 1.5 T / 3 T |
Sequence | Siemens / Philips / GE | MRI sequence, 3D gradient echo sequence with stack-of-stars trajectory and golden angle increment; 1. Siemens Grasp-Vibe Sequence – https://t.ly/P31kM , 2. Philips Multi Vane XD Sequence https://t.ly/wMWZZ , 3. GE Lava-Star, Disco-Star – https://t.ly/vimm2 |
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