1.

Introduction

Radiotherapy treatment planning using magnetic resonance imaging (MRI), alone or in combination with computed tomography (CT), has become increasingly common over the past couple of decades.^1–4 The superior soft tissue contrast of MRI compared with CT makes it an attractive imaging modality for target structure and organ-at-risk (OAR) segmentation.^5–7 Furthermore, recent advances in deformable image registration to spatially accurate CT images and electron density assignment using synthetic CT generation or alternative atlas-based approaches have helped address the primary pitfalls of combined magnetic resonance (MR)/CT-based treatment planning, namely geometric distortion and direct dose estimation.^8–16

MR-linear accelerator (Linac) users are major beneficiaries of these advances in MR-based treatment planning.^17,18 These hybrid MRI-Linac devices can acquire imaging data during each fraction of radiotherapy and incorporate these data into MR-compatible treatment planning systems.^19,20 Moreover, these daily images can be used in online or offline adaptive planning workflows when major changes in anatomy or tumor function are detected.^21,22 Thus a major area of research focuses on sequence development for MR-Linac devices to improve the visualization of relevant structures and discover and acquire useful imaging biomarkers for treatment response and resistance.^23,24

The treatment of head and neck cancer (HNC), especially human papillomavirus-associated HNC, using MR-Linac has been particularly successful.^25–27 These tumors are relatively radiosensitive, which warrants the use of the adaptive replanning utility with the MR-Linac.^28–30 Furthermore, delineation of the complex anatomy in the head and neck region is difficult to visualize with CT, with which most of the structures have a uniform signal and little contrast. Conversely, T2-weighted (T2w) MRI provides a higher signal-to-noise ratio as well as contrast with surrounding structures, which allows for clearer and more precise segmentation.^31,32 However, fat also appears hyperintense on these images, which can reduce the contrast in structures adjacent to fat and obfuscate their boundaries.^33,34 Fat is present in many areas in the head and neck such as at the skull base and fat pads in the face, the parapharyngeal space, the retropharyngeal space, and the submucosal spaces of the supraglottic larynx.³⁵ Thus for accurate segmentation of the target and OAR during radiotherapy planning, the use of fat-suppressed images is necessary.

To attenuate the fat signal while keeping the water signal within tissue intact, several fat-suppression methods have been established. These methods, which use prepulse inversion recovery and/or bandwidth strategies during image acquisition or postprocessing techniques during image reconstruction, have been described at length in the literature.^36–41 These include the short tau inversion recovery (STIR), chemically selective saturation (CHESS), spectral presaturation with inversion recovery (SPIR), spectral attenuated inversion recovery (SPAIR), and Dixon techniques. STIR suffers from a lower signal-to-noise ratio (SNR) because of the attenuation of all tissue signals with the same T1 as fat, but the technique is less sensitive to ${\mathrm{B}}_0$ inhomogeneities. CHESS improves the SNR by selectively attenuating the fat signal, but it suffers substantial effects of ${\mathrm{B}}_0$ and ${\mathrm{B}}_1$ inhomogeneities. SPIR is a hybrid of STIR and CHESS but suffers from the same ${\mathrm{B}}_0$ and ${\mathrm{B}}_1$ inhomogeneities as CHESS. Depending on the severity of the artifact, the strength of the fat suppression can be adjusted. SPAIR, like SPIR, selectively attenuates the fat signal, but it does so using an adiabatic pulse, which helps offset the effects of ${\mathrm{B}}_1$ inhomogeneities at the expense of a higher specific absorption rate (SAR) in the patient. SAR levels are typically capped according to level 1 and 2 power settings on the console. The Dixon technique results in moderate SNR, more robust fat suppression, and less sensitivity to both ${\mathrm{B}}_0$ and ${\mathrm{B}}_1$ inhomogeneities (with the help of postprocessing), although it usually requires longer scan times due to the acquisition of multiple images.

Although fat-suppressed sequences are commonly used in diagnostic systems, there has yet to be a sequence specific for head and neck treatment planning on the MR-Linac presented in the literature. Thus this study, whose purpose is to develop and optimize a three-dimensional (3D), fat-suppressed, T2w sequence that could be used on a 1.5 T Unity MR-Linac (Elekta AB, Stockholm, Sweden) for treatment planning purposes, is warranted and clinically impactful. Cast in the R-IDEAL (radiotherapy-predicate studies, idea, development, exploration, assessment, long-term study) framework, as per by the MR-Linac Consortium recommendations, this study is designed to stage 0 (radiotherapy predicate studies) to stage 2a (development).⁴² This study provides a methodological and rigorous foundation for the implementation of this technical development for MR-Linac clinical workflows and a starting point for future studies along the R-IDEAL pipeline, which is an assessment methodology for the evidence-based clinical evaluation of innovations in radiation oncology. The image quality analyses of the fat-suppressed sequence, along with the exam card of the optimized sequence itself, are presented here, so the sequence may be disseminated to Unity MR-Linac users.

A secondary goal of this study is to develop a comprehensive and robust image quality analysis platform to objectively score and rank candidate fat-suppressed sequences. Image quality assessment is generally split into 2 classes: subjective approach (which is based on human perception) and objective approach (based on mathematical concepts).^43,44 Subjective approaches more closely represent the human visual system (HVS) and thus are arguably more reliable for grading images. However, there is a degree of variability between observer perceptions, and the overall grading process can be highly time consuming. Objective approaches minimize bias and analysis time, but typically measure individual aspects of image quality, which variably correlate to the HVS. There are efforts to effectively model the HVS with an objective approach,^44–46 but optimal solutions have not yet been determined. As such, most sequence development and optimization studies report one or more metrics commonly used by physicists, including SNR, contrast-to-noise ratio (CNR), and modulation transfer function.⁴⁷ The correlation between subjective and objective approaches has also been evaluated.⁴⁸ To consolidate the strengths of both approaches, we include both subjective and objective metrics relevant to radiotherapy treatment planning in our assessment. This includes the metric known as conspicuity, which is a parameter directly related to the visibility of an object in a complex background. Although such a variable would be directly applicable to radiotherapy segmentation, it is not commonly reported, nor is it available on most image analysis platforms. Thus we develop a script to automatically calculate this value from an image and its associated segmentations. This metric, along with SNR, CNR, and pairwise distance, are incorporated to capture multiple aspects of the HVS. By calculating the metrics separately and combining them in a rubric, specific strengths and deficiencies of each sequence can be observed. The resultant analysis platform described here is easily customizable and can be generalized for the optimization of any anatomic-based imaging sequence, adding further value to this study.

2.

Methods

2.9.

Rubric for Overall Sequence Scoring

For each metric that was analyzed, a score was determined for each sequence-structure pair. Each sequence-structure pair received a score between 1 and 4, where 4 corresponded to the pair with the best performance. The value of 4 is equivalent to the number of sequences analyzed. Sequence-structure pairs could only receive a higher score than other pairs if the difference in performance was statistically significant ($P<0.05$) compared with all other sequence-structure pairs with a lower score. Sequence-structure pairs that received the same score were rescaled to the average rank between them. For example, if a scoring distribution was scored as {4, 2, 2, 1}, it was rescaled to {4, 2.5, 2.5, 1}, where $2.5=(3+2)/2$. For clarity, these rescaled scores will be regarded as metric scores.

The metric scores within each analysis category (SNR, CNR, conspicuity, etc.) were summed for each sequence-structure pair. For structure-agnostic metrics, the score was added to each structure within the sequence. The summed metric scores for a particular structure within a sequence was then renormalized to a score between 1 and 4 (4 corresponding to highest summed score) according to its rank relative to the same structure among the other sequences. For clarity, these will be regarded as normalized category scores. This normalization was performed so that a category with more metrics (such as SNR and CNR measurements) would be weighed the same in the overall analysis as a category with fewer metrics (such as conspicuity).

The normalized category scores for each sequence-structure pair were then summed and normalized to determine the total score and the normalized total score. These scores were used to compare the overall image quality for each structure among the sequences. Finally, the total score for each structure within a sequence was summed across structures and normalized to determine the combined total score and combined normalized score. These scores were used to compare the overall image quality across structures among the sequences. Refer to Fig. 1 for a graphical depiction of the scoring.

Fig. 1

Flowchart of scoring procedure for the analysis platform. Images are acquired and screened by MR physicists. Images from acceptable sequences are segmented by physicians. Metrics across multiple categories are calculated for segmented structures. Metric scores for each sequence-structure pair from each category are summed and normalized to their respective normalized category scores. Normalized category scores are further summed across structures and normalized to calculate the total score for each sequence-structure pair. Finally, the total score per structure within each sequence are summed and normalized to calculate the final combined total score. During each “summed and normalized” step, weights can be applied if the user wishes to weigh an individual metric, individual analysis category, or individual structure higher for their specific application. CNR, contrast-to-noise ratio; MR, magnetic resonance; SNR, signal-to-noise ratio.

3.

Results

3.1.

Image Acquisition and MR Physicist Initial Screening

A nonsuppressed T2w sequence and five candidate SPAIR sequences were successfully acquired for each of the five patients. The six sequences for a representative patient are shown in Fig. 2. Paired nonsuppressed and paired SPAIR images (SPAIR 4) in two regions of the head and neck are illustrated in Fig. 3 (with visible segmentations) and Fig. S2 in the Supplementary Material (without visible segmentations). The borders of the primary tumor and metastatic lymph nodes are clearer on the SPAIR image than on the nonsuppressed image. As a result, the segmentations initially drawn on the nonsuppressed image clearly overestimate and/or underestimate the extent of the primary tumor and metastatic lymph nodes when viewed on the SPAIR image.

Fig. 2

(a) Nonsuppressed, T2-weighted sequence and (b)–(f) five SPAIR sequences acquired on a representative patient with HNC using a Unity magnetic resonance linear accelerator.

Fig. 3

Representative (a), (c) nonsuppressed, T2-weighted and (b), (d) SPAIR T2-weighted images in a patient with HNC. These images are from the SPAIR 4 sequence iterations. The top and bottom rows show two different slices from the patient and illustrate the differences in the clarity of the primary tumor (segmented in blue) and the metastatic lymph nodes (segmented in yellow). The visible segmentations were initially drawn on the nonsuppressed, T2-weighted image. (a), (b) The original segmentation underestimated the extent of the primary tumor, which is clearly visible on the SPAIR image (red arrow in b). The clarity of the inferior portions of the submandibular glands (bilateral structures anterior to the metastatic lymph node) can also be appreciated between the nonsuppressed and SPAIR 4 images. In the nonsuppressed image, the submandibular glands are hypointense with little contrast to surrounding fat, but in the SPAIR 4 image, they are hyperintense with exquisite contrast to the suppressed fat signal. (c), (d) The original segmentations overestimated the extent of the primary tumor and the metastatic lymph node, the boundaries of which are better visualized on the SPAIR 4 images (red arrows in d). Refer to Fig. S1 in the Supplementary Material to see the images without the segmentations visible.

MR physicists analyzed SPAIR sequences; their findings are summarized in Table S1 in the Supplementary Material. Among these sequences, SPAIR 4 consistently received the highest qualitative physicist grades (most preferred), whereas SPAIR 2 and 5 consistently received the lowest grades. Moderate-to-severe artifacts, including the herringbone (filtering), Gibbs ringing, zebra (3D phase aliasing), and partial volume artifacts, were consistently observed in SPAIR 2 and 5. One patient image acquired with SPAIR 4 had a bright blood vessels artifact. On average, SPAIR 4 and 2 had the highest percentage of slices that displayed observable amounts of anterior and posterolateral burnout, respectively. Conversely, SPAIR 5 had the lowest percentage of slices that displayed observable amounts of anterior and posterolateral burnout, but this is likely due to the increased number of slices in this sequence. Raw MR physicist feedback is provided in Appendix 2 in the Supplementary Material. The artifacts are illustrated in Figs. S3 and S4 in the Supplementary Material. Because of the consistently low-qualitative grade and persistence of severe artifacts among the patient images acquired with the sequences, SPAIR 2 and 5 were omitted from the primary analysis, which is subsequently described.

4.

Discussion

In this study, we developed several candidate SPAIR T2w sequences, reported here for the first time, so that fat-suppressed images could be incorporated into the treatment planning pipeline for patients with HNC treated on a Unity MR-Linac. We also developed a comprehensive image quality analysis platform to objectively score the sequences using a combination of quantitative and qualitative metrics. Using these metrics, the SPAIR sequence with the best combination of SNR, CNR, conspicuity, segmentation consistency, and qualitative segmentor assessment results for four structures—the GTV, metastatic lymph nodes, parotid glands, and pterygoid muscles—was identified. Both the optimized SPAIR sequence and the analysis platform can be used for clinical and research applications in radiation oncology.

After the five initial candidate SPAIR sequences were acquired in five patients, MR physicists performed an initial screening on the images to identify those with persistent image quality deficiencies. SPAIR 2 and 5 were consistently graded the lowest and produced numerous artifacts. Unlike the other SPAIR sequences, SPAIR 2 and 5 both used “through-plane” instead of “strong” free induction decay reduction. This parameter controls the crushing gradient that is responsible for attenuating residual magnetization during an echo train. Typically, this attenuation is achieved using in-plane gradients (such as in the “strong” option), but a research option for this parameter is “through-plane,” which uses the transverse gradient to attenuate the magnetization and reduces TE (also used in flow compensation). Insufficient magnetization attenuation can lead to stimulated echoes in an echo train and resultant “ringing-like” artifacts (separate from Gibbs), which appear as alternating lines of hyperintense and hypointense signals in an image. Examples of this artifact, which is also called a herringbone or filtering artifact, and other artifacts are shown in Fig. S6 in the Supplementary Material. Thus this free induction decay (FID) reduction parameter as an acceleration method is cautioned against for these types of sequences. No artifacts were identified in any of the other sequences (except for the “bright blood vessels” artifact in one of the SPAIR 4 patient images); this result was encouraging for the use of these SPAIR sequences in radiation oncology applications. In each SPAIR sequence, there was some degree of burnout in areas near tissue–air or tissue–bone interfaces away from the image isocenter, but this was expected due to ${\mathrm{B}}_0$ inhomogeneity in these areas and did not impact segmentation. Because of these image quality deficiencies, SPAIR 2 and 5 were omitted from the subsequent primary analysis, which focused on the nonsuppressed sequence and SPAIR 1, 3, and 4.

The included metrics of the image analysis platform were carefully selected according to their applicability in HNC radiotherapy. For example, segmentation precision and qualitative comments were included in this analysis, although they may not be as necessary as other metrics for diagnostic imaging purposes. Metrics for patient motion were not analyzed in this study because of the use of immobilization devices in HNC radiotherapy. For the optimization of sequences for thoracic or abdominal imaging, these metrics would need to be considered in selecting the best sequence to use. The major benefit of this image analysis platform is that it can be easily customized through the addition, removal, or weighting of analysis metrics, depending on the application, and it can be generalized to any sequence (MRI or another modality) for any number of structures. For example, if users desired to give the results of the segmentor assessment twice as much weight as the other analysis categories, they could simply double the normalized category score of this analysis category when calculating the total scores and updated total scores. Alternatively, if they wanted the outcomes of the GTV and lymph node target structure analyses to be given twice as much weight as the outcomes of the parotid gland and pterygoid muscle analyses segmentations, they could simply double the scores of the former when calculating the combined total score. It is up to the individual user or institution to decide on how these weights should be implemented. A similar multicategory grading system to the one in this study was described by Dimaridis et al.,⁵² in which several studies in the field of optoacoustic imaging were reviewed and graded based on author-defined criteria of the study quality. The criteria had multiple subgroups and were summed and normalized in a similar manner as this study. The authors stated that this scoring system was developed to be “applicable to works with a broad range of purposes, as well as to emphasize the importance of evaluation with human subjects.” However, the criteria were limited to qualitative assessments due to the nature of the analysis. Miéville et al.⁵³ used multiple objective criteria with a subjective grading from two radiologists to test the effects of an image denoising reconstruction algorithm, though they were not combined in one overall score.

From the comprehensive analysis, the nonsuppressed sequence had lower scores than each SPAIR sequence for the GTV, lymph nodes, and parotid gland structures. Interestingly, the opposite was true for the pterygoid muscle structures, for which the nonsuppressed sequence had scores superior to those for each of the SPAIR sequences. This result was likely due to the fact that muscle appears hypointense in T2w sequences, so its contrast was further reduced after the signal from fat was suppressed. Among the SPAIR sequences, SPAIR 1 and 4 scored higher or equivalent to SPAIR 3 in every category for all structures. For the GTV, parotid glands, and pterygoid muscles, SPAIR 1 and 4 performed very similarly for the various metrics. This was not too surprising considering that the sequence parameters were very similar between the two sequences except in regard to the refocusing angle (SPAIR 1, 40 deg; SPAIR 4, 55 deg), ${\mathrm{TE}}_{\mathrm{equiv}}$ (SPAIR 1, 93 ms; SPAIR 4, 107 ms), and (SPAIR 1, 1600 ms; SPAIR 4, 1400 ms). These sequences also had larger oversampling factors and smaller water-fat shifts than the remaining SPAIR sequences. It should be noted that SPAIR 4 was consistently the best performing sequence for the lymph node structures, which could perhaps be explained by slight differences in T2 weighting. Furthermore, the acquisition time for SPAIR 4 was nearly 1 min less than that for SPAIR 1. Users may want to test both sequences to determine which fits their personal preferences, but we officially recommend that SPAIR 4 be used for HNC radiotherapy treatment planning. Because extended FOV scans can be helpful for identifying lower neck lymph nodes in the head and neck region, we attempted to create an extended FOV sequence (SPAIR 5) with an equivalent acquisition time as the other SPAIR iterations. However, the acceleration techniques required for this acceleration resulted in severe imaging artifacts. Thus we recommend extending the FOV of SPAIR 4, rather than using SPAIR 5, if lower neck nodes need to be imaged. Adding the extra 50 slices to SPAIR 4 to match the FOV of SPAIR 5 would result in an increased acquisition time of 2:30.

Additional criteria, other than the absence of severe artifacts, should be met before MR images are used for radiation oncology applications. High geometric accuracy is arguably the most important criterion to ensure accurate target coverage during treatment delivery. System-based geometric distortion on the Unity MR-Linac has been extensively investigated and is reported to be $\sim 1$ to 2 mm for a 350-mm diameter spherical volume, which still results in treatment plan accuracy within recommended tolerances in phantoms and patients.^19,54,55 We observed similar results in the nonsuppressed and SPAIR 1 and 4 sequences using an Elekta geometric distortion phantom (data provided in Table S7 and Fig. S10 in the Supplementary Material). Furthermore, there were no significant differences in the distortion measurements between the nonsuppressed sequence (currently used clinically) and either SPAIR sequence. Geometric distortion also manifests from susceptibility artifacts, which can occur in the head and neck region due to dental implants, bone, and air that cause local changes in the magnetic field. Imaging at a low magnetic field and using spin echo rather than gradient echo sequences are the most effective methods for mitigating this artifact. Because all of the sequences in this study are fast spin echo and acquired at the same magnetic field, they all possess similar levels of susceptibility artifacts. Additionally, high-resolution images are needed for the accurate delineation of structures. Each of the SPAIR sequences had an isotropic reconstructed voxel size of 1 mm or less (except for SPAIR 5, which had a larger through-plane field of view and resultant voxel size). Finally, the target structures and OARs need to be visible in the images for accurate and consistent segmentation. The SNR, CNR, conspicuity, pairwise distance, and segmentor analyses in our study all demonstrated that representative structures were more detectible with the SPAIR sequences than with the nonsuppressed T2w sequence.

Several studies have evaluated one or more fat suppression methods for head and neck diagnostic imaging.^56–59 A few studies further compared various fat-suppressed techniques for the head and neck region. Gaddikeri et al.³³ compared the Dixon and STIR techniques in T2w images as well as the Dixon and SPIR techniques in postcontrast T1w images. The Dixon method outperformed both the STIR and the SPIR techniques in terms of signal intensity and reader-graded image quality. In a series of publications, Ma et al.^34,60 demonstrated that, in the head and neck region, a triple-echo Dixon technique suppressed the fat signal to a higher degree and more uniformly than did the CHESS and alternative Dixon techniques. These results were corroborated by Wendl et al.⁶¹ Kawai et al.⁶² compared coronal STIR and axial SPIR techniques for the detection of metastatic lymph nodes in HNC. The authors stated that both techniques performed comparably, although the STIR sequence was shorter and had fewer susceptibility artifacts than did the SPIR sequence.

Dixon-based sequences are strong candidates for fat suppression. However, there are currently no Dixon sequences available to clinical users of the Unity MR-Linac, and only 2D, T2w, Dixon sequences are available to select research users. 2D sequences are generally unusable for treatment planning purposes for two reasons: the lack of precision when delineating structures and the logistical inability to import 2D images into the treatment planning system. Because we wanted to develop and optimize a sequence that could be broadly disseminated to other Unity users without the need for a research patch, we opted not to pursue the development of a 3D, T2w, Dixon sequence. Thus for the time being, SPAIR is the optimal clinically available fat suppression technique for HNC treatment planning on the Unity MR-Linac. If 3D Dixon sequences are broadly enabled on the Unity, then we plan to compare an optimized 3D Dixon sequence with the optimized SPAIR sequence presented in this paper. It should be noted that, in a study by Huijgen et al.⁶³ that compared Dixon and SPAIR sequences for musculoskeletal tumor imaging, there were minimal differences among the image quality metrics for the sequences, except in terms of fat suppression homogeneity, which was superior in the Dixon sequences. In addition, the Dixon sequences performed noticeably better in areas with large ${\mathrm{B}}_0$ inhomogeneities, which suggests that future investigations of Dixon sequences in HNC treatment planning are warranted. Finally, once compressed sensing is clinically available for the Unity MR-Linac, the iterative reconstruction of undersampled data could accelerate sequences by up to 40%, and it would be worthwhile to use the framework outlined in this paper to reevaluate accelerated SPAIR sequences.

Compared with diagnostic systems, the imaging quality of the MR-Linac is slightly compromised to allow for the delivery of megavoltage beams. For example, the gradient coils are split at isocenter to allow for the entry of a beam up to 22 cm wide, but this makes it susceptible to eddy currents and gradient nonlinearities. Eddy currents can lead to unwanted time-varying gradients as well as ${\mathrm{B}}_0$ inhomogeneities and were shown to be the main source of distortion around isocenter rather than gradient nonlinearities.^64,65 Although these effects are more pronounced in sequences with fast-switching gradients, such as EPI and diffusion scans, turbo spin echo sequences are still susceptible to shading artifacts. And although eddy currents can affect alternative fat suppression techniques such as CHESS and SPIR sequences, the adiabatic inversion pulse used in SPAIR sequences is highly insensitive to local ${\mathrm{B}}_1$ inhomogeneities and frequency shifts induced by eddy currents. This is also beneficial to mitigating any ${\mathrm{B}}_1$ inhomogeneities produced by the radiolucent quadrature body coil used to transmit RF power (the anterior and posterior array surface coils are receive-only and do not contribute to ${\mathrm{B}}_1$ inhomogeneities). This quadrature body coil was designed to maintain reproducibility and minimize dosimetry modulation of treatment plans.^66,67 However, image quality with this coil is impacted because it cannot be positioned as close to the anatomy as four-channel flex coils commonly used in head and neck MR simulations, nor can it produce the same amount of SNR as 64-channel head and neck volume coils that are often used in diagnostic settings. Furthermore, the maximum gradient strength and slew rate of the MR-Linac gradient coils is lower than what is seen in typical diagnostic and simulation MR scanners, resulting in slower gradient encoding.⁶⁵ Although this does have a diminishing effect on SNR, T2-weighted sequences are not otherwise significantly impacted because they do not utilize short TE values. Finally, even though the gantry hardware can rotate around the MR-Linac bore and operate during image acquisition, it was demonstrated that it minimally impacts image quality.^19,65

A limitation of this study was that only a few patients were included in the analysis, although for initial sequence development and optimization studies, this is not too unusual because the time required to manually segment multiple structures per image for multiple images per patient limits the number of patients that can realistically be included in one study. This limitation was mediated by our inclusion of multiple independent segmentors. Further multiinstitutional validation of these sequences within the MR-Linac Consortium would add power to these results. Furthermore, investigating the usefulness of these sequences for autosegmentation purposes would greatly increase the number of available segmentations. A perceived limitation to this study could be that the scores on the rubric were not substantially different between the SPAIR sequences. This is due to the requirement that sequences with higher rubric scores must have significantly greater ($p<0.05$) metric values than all sequences with lower rubric scores. In the statistical analysis, the $p$-values were corrected for multiple comparisons, which further results in more stringent criteria. A final limitation to the study was that the front-end treatment planning effects (segmentations), but not the downstream dosimetric implications, were investigated. But because there was no ground-truth segmentation for the investigated structures, only relative differences in downstream doses from differing segmentations would have been inferred. A study designed to investigate the clinical implications of using or excluding an optimized fat-suppressed sequence during the treatment planning process would be of great interest.

5.

Conclusions

In this study, we optimized a 3D, T2w SPAIR sequence for HNC treatment planning on the Unity 1.5 T MR-Linac. This sequence and the other candidate sequences are available for download for use on other Unity devices. We also developed a robust image quality analysis platform that can be customized and generalized to any type of image optimization. This is the first study to report data on fat suppression methods in the head and neck using the Unity MR-Linac and to provide a working sequence for fat suppression. Furthermore, this is the first study to report a SPAIR T2w image quality assessment for primary and metastatic structures in HNC. We believe that HNC treatment planning and subsequent treatment outcomes can be improved through the use of this sequence.