This work was funded by the German Center for Lung Research (DZL). The authors would like to express a deep gratitude towards all, who contributed and supported the further development of PREFUL, in particular: Marcel Gutberlet, Till F. Kaireit, Lea Behrendt, Julian Glandorf, Sonja L&#252;diger, Tawfik Moher Alsady, Katharina B&#252;nemann, Marius Wernz, Robin M&#252;ller, Maximilian Zubke, Gesa P&#246;hler, Agilo Kern, Cristian Crisosto, Milan Speth, Arnd Obert, Julienne Scheller, Jim Wild, Edwin van Beek, Helen Marshall, Jens Gottlieb, Martha Dohna, Diane Renz, Anna-Maria Dittrich, Tobias Welte, Jens Hohlfeld, Patrick Zardo, Giles Santyr, Franz Wolfgang Hirsch, Robert Grimm, Bastian Bier, Bassem Ismail, Andr&#233; Fischer, Berthold Kiefer, Gregor Thoermer and Rebecca Ramb. Furthermore, the authors would also like to thank the radiographers and study participants. In particular, we thank Frank Schr&#246;der and Sven Thiele from the Department of Radiology (Hannover Medical School) for outstanding technical assistance in performing the MRI examinations.

Advanced Lung Imaging for Disease Detection and Monitoring

Filip Klime&#353;, Andreas Voskrebenzev, and Jens Vogel-Claussen are shareholders of BioVisioneers GmbH, a company, which has an interest in pulmonary magnetic resonance imaging methods.

Critical steps 
One of the most common pitfalls during acquisition is inadequate signal scaling, which causes a loss of information during DICOM conversion by reduced precision of digital data representation. Consequently, this can lead to problems during the postprocessing stage. Another even more critical pitfall is the acquisition of multiple slices in an interleaved fashion. Thereby, the effective temporal resolution of the individual slices is critically reduced. Additionally, depending on the distance of the slices, this can have an impact on the perfusion contrast and quantification since the inflow relies on fresh spins without magnetization history. Special care is required during protocol setup, especially regarding gradient strength, asymmetric echo, bandwidth, and parallel imaging. Deviations from the suggested settings for even just one of these parameters can lead to inadequate TE and temporal resolution.
The postprocessing consists of multiple steps, which should be followed in the described order. For example, a registration after low-pass filtering is not meaningful. Consequently, failure at one step leads to a breakdown during the next steps. This makes the registration stage especially important. Since there is no single registration algorithm, depending on the respective implementation, parameters must be set empirically. Without finetuning of these parameters, a false registration will prevent the generation of any meaningful result. Another possibly time-consuming and critical step during postprocessing is segmentation. False segmentations can lead to completely wrong parameter calculations (e.g., by including non-lung regions) in the final report. Such mis-segmentations are more likely to occur with deep learning algorithms, which are accustomed to certain image appearances and are applied to images from another vendor/machine with a slightly different appearance. A visual quality check of segmentation accuracy, with potential manual correction, is therefore mandatory.
Troubleshooting 
The typical troubleshooting procedure is to follow all steps one by one and check the plausibility of the intermediate results. The procedure for the main steps is as follows: Check that the images are acquired in free breathing with the correct sequence and settings. Next, check that the dynamic range of the signals is appropriate (~50 AU in the lung parenchyma). If raw data are still available, repeat the reconstruction of the images with an appropriate scaling factor avoiding the need for a new acquisition of data. Check that the registration was performed without major artifacts and remaining motion. Next, check if small ROIs show a time series with expected ventilation- and perfusion-related modulations. Then, check if the applied filters alter the images in the expected manner (e.g., no high-frequency modulations in low-pass filtered data). Check if the synthesized respiratory and cardiac cycles are physiologic and don&#39;t show sudden jumps. Check the segmentation accuracy. Note that a search on a finer resolution level might be necessary as soon as the main step, during which the problem occurs, was identified.
Limitations 
Although the presented protocol is known to produce reproducible and sensitive results, the numbers of involved steps and parameters during acquisition and post-processing allow for nearly endless optimization and are intertwined. Therefore, a bottom-up approach should be followed by first addressing optimizations of the sequence protocol (e.g., regarding SNR and functional contrast-to-noise ratio). For the following postprocessing optimizations, a predefined ground truth in the form of a digital lung model might be useful40. As presented, this model mimics a free-breathing acquisition and includes several classes to simulate ventilation/perfusion defects. Including a known deformation due to movement, registration algorithms can also be tested directly. Despite these advantages, each model is inherently limited by the accuracy of mapping complex reality to a finite and simplified model.
The thresholds presented in this protocol were found to show reasonable results for healthy volunteers and across different patient cohorts by empirical analysis. Nevertheless, as outlined before, adjustment is likely required depending on the sequence, field strength, and cohort.
A general limitation of PREFUL is the extensive post-processing, which is not readily available as a medical product yet, although first work-in-progress versions from Siemens Healthineers and BioVisioneers are available for scientific purposes in a scientific collaboration/commercial setting. Calculations typically involve parallel processing, which poses especially high demands on CPU and RAM and might require modern workstations or server solutions to effectively process large amounts of data. Further, the time-consuming postprocessing steps currently impede an instant presentation of the results, which would be desirable for the clinical workflow.
Comparison to other methods 
There are a multitude of similar approaches like PREFUL, including the predecessor Fourier Decomposition and its other derivates such as Matrix Pencil Decomposition41 and the slightly different approach Self-gated Non-Contrast-enhanced Functional Lung MRI (SENCEFUL MRI)42. While Fourier Decomposition and similar methods operate in the frequency domain, PREFUL uses less strict Fourier-filtering and subsequent calculation of amplitudes in the time domain. Therefore, there is no requirement to select specific peaks corresponding to ventilation/perfusion. This can result in less susceptibility to respiration variability, which is known to occur in human subjects.
While PREFUL performs image sorting, SENCEFUL uses sorting of k-space lines, leading to more flexibility. Nevertheless, SENCEFUL requires sequences with self-gating capabilities, while PREFUL can be performed with a conventional spoiled gradient echo sequence. Similarly, bSSFP commonly used in Fourier Decomposition-based approaches is known for better SNR and blood flow contrast but typically requires more optimization for lung acquisition especially at 3T43. Nevertheless, other than that there is no reason to not combine PREFUL with bSSFP acquisition44.
All these signal-based approaches assume that certain unwanted signal influences, including T1, T2/T2*, diffusion, through-plane motion, and non-orthogonally perfused voxels, are negligible. While the progressed validation of PREFUL indirectly suggests that indeed such influences are not critical, Triphan et al. showed that there is a dependence on the effective T1 and TE, which is explained by the different weighting of the blood and parenchymal components depending on the TE45. In this light, the initial advantage of bSSFP to visualize blood due to T2/T1 contrast might pose an additional challenge to establish an accurate quantification in comparison to the simpler contrast mechanics of an SPGRE. Nevertheless, further studies that directly address the influence of various MR-variables, for example, as performed by Glandorf et al. for contrast media46,47, are desirable as they can directly quantify the effect on PREFUL.
Importance 
Being a free-breathing, contrast-media-free method, PREFUL shares many advantages with the previously mentioned related methods: 1) No ionizing radiation and contrast agent application, 2) No requirement for additional hardware or personnel, 3) acquisition, which depends only on minimal patient compliance. These advantages make PREFUL a convenient monitoring tool, especially for vulnerable groups such as children with chronic pulmonary disease. Although SNR is low with SPGRE sequence, the availability, and a lack of requirement for additional sequence programming/sharing further promote the dissemination of this approach.
As discussed in the introduction section, the number of studies showing good validation, reproducibility, sensitivity results, and monitoring capabilities show that the importance of this technique and corresponding dynamic parameters is on a rising trajectory and will be further supported by wide dissemination.

The respiratory system, with its intricate mechanisms, is vulnerable to various diseases. Prominently, chronic respiratory conditions such as chronic obstructive pulmonary disease (COPD), cystic fibrosis (CF), and chronic thromboembolic pulmonary hypertension (CTEPH) considerably reduce life expectancy1. As a result, early diagnosis, monitoring, and therapeutic response assessment have become paramount.
Pulmonary function tests (PFTs) can derive global lung function parameters like the Tiffeneau-Pinelli index, defined as the ratio of the forced expiratory volume in one second (FEV1) and forced vital capacity (FVC)2. Such parameters are well established in the clinical routine but lack regional information and require a high level of patient compliance. In this regard, imaging can offer additional insights and possibilities for more sensitive parameters. Computed tomography (CT) offers high-resolution imaging of parenchymal morphology, and recent techniques like parametric-response mapping also retrieve functional information3. Nevertheless, single photon emission computed tomography (SPECT) remains the current gold standard for depicting ventilation and perfusion (V/Q) in the lung4. Common to all, the mentioned imaging modalities require exposure to ionizing radiation, which needs special consideration in cases of monitoring and vulnerable groups. Consequently, there is an ongoing effort to promote MRI as an alternative modality.
Inherently, the lung is a challenging organ for MRI due to its low proton density and fast signal decay5. Among the multitude of approaches, the most widespread solutions include the use of hyperpolarized gas (e.g., 129Xe MRI) for ventilation6 and intravenous gadolinium-based contrast agent application for perfusion depiction7. These methods offer a high signal-to-noise (SNR) ratio and are widely considered gold standard methods in the MR community. A more recent approach avoids the application of any contrast agent and is feasible with conventional proton MR in free breathing with a total acquisition time of ~1 min/slice. Thus, potential adverse events and recently debated long-term effects of contrast agents are avoided and easier dissemination without the requirement for additional hyperpolarization and multi-nuclear hardware is enabled. Additionally, the problem of finding an adequate inflation state, which can affect the derived ventilation defect values8 is avoided by the free-breathing acquisition.
This indirect MR signal-based approach was first introduced by Zapke et al. who utilized the reciprocal relationship of proton-weighted signal S and lung volume V: S~1/V.9 It is based on the process of transforming images acquired in free-breathing to one common inflation state (typically in an intermediate position between end-expiration and end-inspiration), thereby compensating for motion and allowing to analyze the signal time series in each voxel. Thereafter, a ventilation measurement can be derived from these so-called registered images by using equation (1) by Klime&#353; et al.10:
<img alt="Equation 1" src="/files/ftp_upload/66380/66380eq01.jpg" style="margin: auto;" />&#160; &#160; &#160;(1)
With the volumes/signals in inspiration (Insp), expiration (Exp), and registered state (Reg). Thereafter, the method was expanded by introducing Fourier Decomposition to differentiate between signal modulations associated with breathing frequency (ventilation) and pulse frequency (perfusion) and therefore, derive a perfectly spatially matched V/Q map from one acquisition11. This is made possible by the typical gap between breathing and heart frequencies, so that both components which are on top of each other in the time domain are effectively discriminated in the frequency domain by Fourier analysis. After the transition from low-field (0.35T) to 1.5T with an optimized balanced steady-state free precession sequence (bSSFP)12, this method started to gain more attention with several follow-up studies13,14,15.
Since breathing and pulse are subject to variability and commercially available bSSFP (gradient compensated) imaging at 1.5T can result in substantial banding artifacts (clear lines of signal void), a related method was proposed with spoiled gradient echo sequence (SPGRE) in combination with broad low-pass and high-pass filtering16,17. This captures the more complex spectrum of real breathing- and pulse-related modulations. The following calculation of the amplitude in the time domain avoids the necessity to select one specific frequency peak. Further optimization was achieved by splitting the typical one-step registration towards one reference state into two separate steps. Thereby the fact that during free-breathing a range of different respiration phases are acquired between end-inspiration and end-expiration with varying degrees of required deformation towards a fixed state is utilized. After choosing several groups and identifying the group of the individual images the following procedure is performed: 1) Registration inside the respective respiration state group, 2) Step-by-Step inter-group registration from one adjacent group to the next (e.g., 1-&#62;2, 2-&#62;3,&#8230;) to the group representing the reference group. This approach was further expanded by phase estimation for each image to establish a higher apparent temporal resolution to facilitate the analysis of ventilation and perfusion dynamics, leading to the phase-resolved functional lung (PREFUL) MR terminology to differentiate this branch from other related techniques18. Follow-up studies made use of the additional information provided by the full respiration and cardiac cycles and showed potentially increased sensitivity of such parameters19,20,21.
Validation with the gold standard SPECT revealed a dice coefficient of &#8805;67% for defect regions22, and more direct ventilation measurement with 129Xe showed ventilation defect percentages correlation &#8805;62% in a mixed COPD/CF/healthy cohort23 and 84% in a CF multi-center, multi-vendor cohort24, which also demonstrated a similar correlation to lung clearance index of PREFUL and 129Xe (r = 0.82 and r = 0.91). The perfusion analysis of the same study showed that there were no significant differences in spatial overlap with DCE among the evaluated centers25. Concordance with DCE and agreement of PREFUL results between centers was also reported for a prospective sub-study including nine centers26. A reproducibility analysis in COPD patients resulted in a coefficient of variation below 15% for all parameters27. Current studies suggest that the FVL parameter has higher predictive power and sensitivity to detect treatment changes compared to the &#34;static&#34; ventilation parameter, which only takes the end-inspiratory and end-expiratory phases into account. Responsiveness to treatment with regional flow-volume loop (FVL) measurements was demonstrated after inhaler treatment with indacaterol-glycopyrronium (IND/GLY) in COPD28. In concordance, the FVL parameter predicted graft loss in double-lung transplant patients, whereas spirometry could not (P = .02 vs. P = 0.33)29. First feasibility studies show that functional pulmonary imaging with PREFUL can be realized in free-breathing infants and neonates with standard clinical MRI hardware30,31. Glandorf et al. compared PREFUL parameters at 1.5T and 3T (SPGRE sequence) and found no significant differences for most parameters, which were highly reproducible despite the difference in field strength32. This might be an important advantage, as not every site has access to 1.5T or lower field strength scanners. Recently, the feasibility and detection of persistent symptoms after COVID-19 infection at 0.55T was demonstrated by evaluating bSSFP data with PREFUL33.
In summary, despite being a relatively novel technique, PREFUL has been extensively studied. Important criteria such as validation with more direct and established measurements, reproducibility, sensitivity for pathology, and responsiveness for treatment and progression changes were assessed. Nevertheless, still, only a few specialized centers are using this technique despite the low technological requirements. Therefore, the aim of this work is to summarize the latest methodology of PREFUL MR in written and visual form. This information can be used to establish this technique in more centers and thus, in the long-term, lead to a more mature technique.

<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Advanced Normalization Tools (ANTs)</td>
			<td>-</td>
			<td></td>
			<td>Image registration toolbox (https://stnava.github.io/ANTs/; https://github.com/fordanic/image-registration)</td>
		</tr>
		<tr>
			<td>Forsberg</td>
			<td>-</td>
			<td></td>
			<td>Image registration toolbox</td>
		</tr>
		<tr>
			<td>MRI</td>
			<td>Siemens Healthineers AG, Munich, Germany</td>
			<td></td>
			<td>0.55T / 1.5T / 3T Scanner</td>
		</tr>
		<tr>
			<td>PREFUL App</td>
			<td>BioVisioneers GmbH, Laatzen, Germany</td>
			<td></td>
			<td>PREFUL analysis, Figures and reports</td>
		</tr>
	</tbody>
</table>

phase-resolved functional lung mri for pulmonary ventilation and perfusion (v/q) assessment

Fourier decomposition is a contrast agent-free 1H MRI method for lung perfusion (Q) and ventilation (V) assessment. After image registration, the time series of each voxel is analyzed with regard to the cardiac and breathing frequency components.
Using a standard 2D spoiled gradient-echo sequence with a temporal resolution of ~300 ms, an image-sorting algorithm was developed to produce phase-resolved functional lung imaging (PREFUL) with an increased temporal resolution. Thus, it is feasible to evaluate regional flow volume loops (FVL) during tidal volume breathing and depict the propagation of the pulse wave during the cardiac cycle. This method can be applied at 1.5T or 3T with standard MR hardware without the necessity for sequence programming, as the described protocol can be implemented with the default SPGRE sequence on most systems.
PREFUL ventilation MRI has been validated using 129Xe and 19F gas imaging with good regional agreement. Perfusion-weighted PREFUL MRI has been validated using SPECT as well as dynamic contrast enhanced (DCE) MRI. PREFUL has been tested in a dual center dual vendor setting and is currently applied in several ongoing multicenter trials. Furthermore, it is feasible across a range of field strengths (0.55T-3T) and different age groups, including newborns.
Quantitative V/Q PREFUL MRI has been used in patients with cystic fibrosis, chronic obstructive pulmonary disease, chronic thromboembolic pulmonary hypertension, and corona virus disease-2019 to quantify disease and monitor treatment change after therapy. Furthermore, PREFUL V/Q imaging has been shown to predict transplant loss due to chronic lung allograft dysfunction in patients after lung transplantation. In summary, PREFUL MRI is a validated technique for quantitative ventilation and pulmonary pulse wave/perfusion imaging for regional pulmonary disease detection, quantification, and treatment monitoring with potential added value to the current clinical routine.

Author Spotlight: Enhancing Diagnostic Strategies and Biomarker Development for Comprehensive Lung Function Analysis

Phase-Resolved Functional Lung MRI for Pulmonary Ventilation and Perfusion (V/Q) Assessment

The lower part of Figure 2 illustrates the consequence of proper and inadequate scaling with a corresponding effect on the dynamic range. Figure 6 shows the inhomogeneous signal distribution, which is representative of scans without and with coil normalization. Avoiding a low dynamic range and images without coil normalization is recommended.
<img alt="Figure 6" class="xfigimg" src="/files/ftp_upload/66380/66380fig06.jpg" /> 
Figure 6: Exemplary images after acquisition without coil correction (incorrect) and with coil correction (correct). Note the artificial signal enhancement at the body boundaries near the coil elements. <a href="https://www.jove.com/files/ftp_upload/66380/66380fig06large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
Figure 7 illustrates successful and failed automatic segmentation. Note that the failed segmentation does not include all lung voxels, which will falsify further analysis and statistics. Special care is required for cases with infiltrates as such voxels can be misclassified as vessels due to their high signal by AI models or not segmented at all.
<img alt="Figure 7" class="xfigimg" src="/files/ftp_upload/66380/66380fig07.jpg" /> 
Figure 7: An example of automated segmentation resulting in a failed (Subject A: 83-year-old male with COPD) and a successful result (Subject B: 30-year-old female healthy control). The first row shows the images, which were used as input for the AI models. The second row shows the results of the first segmentation stage consisting of finding the lung boundary. The third row shows the final result after the exclusion of vessels. As shown by the blue arrows, the algorithm was challenged by the high-signal lung variances causing wrong lung boundary detection. Note that the images were normalized by maximal signal, which led to different results due to missing coil normalization of the scan performed on subject A. Red regions show the ROIs, which were automatically detected for perfusion-phase sorting. Abbreviations: COPD = chronic obstructive pulmonary disease; ROIs = regions of interest. <a href="https://www.jove.com/files/ftp_upload/66380/66380fig07large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
Figure 8 and Figure 9 show representative parameter maps for a healthy control (age 30, female) and a COPD patient (age 60, male). Note that the healthy control shows a more homogeneous ventilation and perfusion and thus, fewer defect voxels. The corresponding reports of the ROI statistics can be found in Table 4 and Table 5.
<img alt="Figure 8" class="xfigimg" src="/files/ftp_upload/66380/66380fig08.jpg" /> 
Figure 8: PREFUL parameter maps of a 30-year-old female healthy control. The perfusion (1st row), regional ventilation (2nd row), flow-volume loop correlation metric (3rd row), and thresholded V/Q maps (4th row). Note the homogeneous distribution of the parenchymal values and low defect percentages. Abbreviations: PREFUL = phase-resolved functional lung; V = ventilation; Q = perfusion. <a href="https://www.jove.com/files/ftp_upload/66380/66380fig08large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 9" class="xfigimg" src="/files/ftp_upload/66380/66380fig09.jpg" /> 
Figure 9: PREFUL parameter maps of a 60-year-old male COPD patient. The perfusion (1st row), regional ventilation (2nd row), flow-volume loop correlation metric (3rd row), and thresholded V/Q maps (4th row). Note the heterogeneous distribution of the parenchymal values and high defect percentages. Abbreviations: COPD = chronic obstructive pulmonary disease; PREFUL = phase-resolved functional lung; V = ventilation; Q = perfusion. <a href="https://www.jove.com/files/ftp_upload/66380/66380fig09large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
Table 4: Exemplary report of PREFUL parameters obtained for a healthy control (30-year-old female). Note the low coefficient-of-variation and defect values, which are in line with the parameter maps presented in Figure 8 for the same subject.&#160;See also Table 5 and Figure 9. Abbreviation: PREFUL = phase-resolved functional lung. <a href="https://www.jove.com/files/ftp_upload/66380/66380_Table_4_rev.xlsx" target="_blank">Please click here to download this Table.</a>
Table 5: Exemplary report of PREFUL parameters obtained for a COPD patient (60-year-old male). Note the high coefficient-of-variation and defect values, which are in line with the parameter maps presented in Figure 9 for the same subject.&#160;See also Table 4 and Figure 8. <a href="https://www.jove.com/files/ftp_upload/66380/66380_Table_5_rev.xlsx" target="_blank">Please click here to download this Table.</a>
Supplementary Material:&#160;Animated Explanation of the PREFUL Algorithm.&#160;<a href="https://www.jove.com/files/ftp_upload/66380/JOVE_All_Screen_Recordings.mp4" target="_blank">Please click here to download this file.</a>

Here, we describe the implementation of phase-resolved functional lung MRI as a contrast-agent-free proton MR technique for the assessment of pulmonary ventilation and perfusion dynamics. Validated and applicable across different field strengths and age groups, it could enhance clinical decision-making in the future by aiding in disease quantification and therapy monitoring.

Fourier decomposition is a contrast agent-free 1H MRI method for lung perfusion (Q) and ventilation (V) assessment. After image registration, the time ...

Phase-Resolved Functional Lung MRI for Pulmonary Ventilation and Perfusion (V/Q) Assessment

Abstract