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Purpose:
To investigate the utility of a free-breathing ultrashort echo time (UTE) sequence for the evaluation of small pulmonary nodules in oncology patients by using a hybrid positron emission tomography (PET)/magnetic resonance (MR) imaging system and to compare the nodule detection rate between UTE and a conventional three-dimensional gradient...
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... a 4-month period (August 2014 through November 2014), we pro- spectively enrolled eight patients with known pulmonary nodules who were scheduled to undergo PET/computed tomography (CT) for clinical oncologic evaluation. Mean patient age 6 standard deviation was 58.6 years 6 13.6 (range, 45–80 years). Most patients were male (five of eight), and men were significantly older than women according to results of the Student t test to assume unequal variance (65.4 years 6 11.7 [range, 56–80 years] vs 47.3 years 6 2.1 [range, 45–49 years], respectively; P = .03). The primary malignancy for which the patient was undergoing clinical PET/CT included melanoma ( n = 5), breast carcinoma ( n = 2), and papillary thyroid carcinoma ( n = 1). To maximize the number of small pulmonary nodules available for evaluation, an effort was made to en- roll patients with at least two solid pulmonary nodules (ie, not “ground-glass” nodules) smaller than 2 cm in diameter. Patients were excluded on the basis of their history or as a result of preceding clinical PET/CT scanning if they had (a) contraindication to MR imaging (eg, pacemaker, metallic foreign bodies, claustrophobia), (b) more than small-sized pleural effusion, or (c) resolution of previously identified nodules at preceding clinical PET/CT. Clinical and demographic patient information is summarized in Table 1. All PET/CT examinations were performed by using a Biograph 16 Hi-Rez PET/CT scanner (Siemens Medical So- lutions, Erlangen, Germany) with an integrated PET and 16-section multidetector CT scanner or a Discovery VCT PET/CT scanner (GE Healthcare, Waukesha, Wis) with an integrated PET and 64-section multidetector CT scanner. All patients fasted with hydration for at least 6 hours prior to PET/CT examinations, and blood glucose levels were measured just before fluorodeoxyglucose (FDG) injection and were found to be less than 150 mg/dL (8.325 mmol/L) in all cases. FDG was inject- ed intravenously (8.7 mCi 6 1.7), and clinical PET imaging began on average 1 hour 19 minutes 6 23 minutes after radiotracer injection. CT examinations were performed after the injection of 150 mL of iohexol (3 mL/sec, Omnip- aque 350; GE Healthcare). Whole-body images were reconstructed with contig- uous 5-mm section thickness. Dedicated images with 2.5-mm section thickness that covered the lungs were acquired during inspiratory breath hold. These thin-section lung images were used for reference nodule identification and measurement. PET images were obtained with seven to 10 bed positions per patient, with an acquisition time of 3–4 minutes per station, from the skull vertex through the midthigh, without respiratory gating. After clinical PET/CT, patients underwent combined PET and MR imaging in a hybrid 3.0-T Signa time-of-flight PET/MR imaging system (GE Healthcare). No additional radiotracer was administered for the PET/MR imaging; the residual activity from the FDG administered for clinical PET/CT scanning was used for the research PET/ MR imaging. The time interval from FDG injection to the start of research PET imaging was 2 hours 20 minutes 6 24 minutes. After patient positioning and placement of head-to-thighs radio- frequency surface coil arrays, MR and PET data were acquired concurrently without intravenous contrast material. The MR imaging protocol consisted of a UTE sequence, with the following parameters: repetition time, 2.3 msec; echo time, 80 m sec; flip angle, 4°; 1.25-mm isotropic resolution; adaptive respiratory gating with a 40% accep- tance window; and imaging time, 4 minutes 30 seconds (14). A 3D dual-echo GRE sequence with a two-point Dixon method for water-fat separation (LAVA- Flex; GE Healthcare) was performed at end-inspiration with the following parameters: repetition time, 5.6 msec; first echo time, 1.3 msec; second echo time, 2.6 msec; flip angle, 12°; matrix size, 344 3 256; frequency field of view, 42 cm with 80% phase field of view of 2 3 2 Auto-calibrating Reconstruction for Cartesian sampling (GE Healthcare) acceleration; and imaging time, 22 seconds. PET of the lungs was performed with a 12-minute acquisition. No intravenous contrast material was administered for MR imaging. Image quality was good in all cases, and no patients were excluded because of technically insufficient image quality. Imaging parameters for each technique are summarized in Table 2. CT was considered the reference standard for determination of nodule size, location, and appearance. Nodules were categorized into five subdivisions according to the short-axis dimension by an experienced reviewer (N.S.B., with 7 years of experience) as follows: smaller than 2 mm to smaller than 4 mm, at least 4 mm to smaller than 6 mm, at least 6 mm to smaller than 8 mm, at least 8 mm to smaller than 10 mm, and at least 10 mm. Fissural or pleural nodules and nodules measuring up to 2 mm at CT were not included in the analysis. For each MR imaging technique, assessment of nodule detection and measurement of nodule diameter were repeated by two experienced readers (N.S.B. and T.A.H., who had 10 years of experience) who compared findings with the CT reference standard; mean nodule diameter between readers was used for diameter subgroup classification. In limited cases where there was disagreement between raters regarding nodule detection (three nodules for both UTE and dual-echo GRE imaging), nodules were classified as not detected. In-phase dual-echo GRE series have been reported to be more sensitive for nodule detection compared with “water only” series, and we therefore based our assessment of nodule detection on in-phase images (Fig 1) (2). Nodule location and FDG avidity were determined by an experienced reviewer (N.S.B.). Nodules were characterized as “central” if they were within 2 cm of hilar structures or “peripheral” if they were within 2 cm of the chest wall or mediastinum. All other nodules were considered “midlung.” The “central” definition superseded “peripheral” in cases of group overlap. Nodules were further characterized as “upper lung” if they were located superior to the origin of the upper lobe bronchi and as “lower lung” if they were located inferior to the origin of the basal segmental bronchi. Nodules were defined as subpleural if any margin of the nodule was located within 5 mm of a parietal pleural surface. Nodule FDG avidity was assessed with both clinical PET/CT and PET/ MR. FDG avidity was defined as any discernable FDG uptake above background lung activity. Baseline characteristics are given as means 6 standard deviations for contin- uous variables and frequencies for categorical variables. Comparison of group means was performed with nonparamet- ric Mann-Whitney U tests. The x 2 test and Fisher exact test were used to evaluate differences in frequency of unpaired categorical variables. The McNemar test was used to evaluate paired differences in nodule detection rate between MR imaging techniques. Interrater reliability of nodule detection was determined by means of the unadjusted Cohen k statis- tic. Bland-Altman plots were generated to visually depict interrater variance and limits of agreement. All statistical analyses were performed by using Stata version 13.0 software (StataCorp, Col- lege Station, Tex). The mean number of pulmonary nodules identified per patient with clinical CT was 10.25 6 11.9 (range, 2–36 nodules; Table 1). The mean nodule diameter at CT was 6.2 mm 6 2.7 (range, 3–17 mm), and most nodules measured up to 1 cm (79 of 82 nodules, 96%). Most nodules were enlarged in size or new compared with findings of prior clinical PET/CT studies (57 of 82 nodules, 70%), but only a minority showed FDG uptake above background on clinical PET/CT images (13 of 82 nodules, 16%). Table 3 presents nodule distribution and characteristics. The overall nodule detection rate was 73% (60 of 82 nodules) for UTE sequences and 30% (25 of 82 nodules) for dual-echo GRE sequences ( P , .001) (Fig 2). The nodule detection rate according to UTE technique was low for nodules larger than 2 mm but smaller than 4 mm in diameter (two of 12 nodules, 17%) but was significantly higher for nodules at least 4 mm in diameter (58 of 70 nodules, 83%; P , .001). Similarly, the nodule detection rate with dual-echo GRE imaging was extremely low for nodules larger than 2 mm but smaller than 4 mm in diameter (one of 12 nodules, 8%) but was higher for nodules at least 4 mm in diameter (24 of 70 nodules, 34%), although this did not reach statistical significance ( P = .07). The nodule detection rate was significantly higher for UTE imaging compared with dual-echo GRE imaging for nodules at least 4 mm in diameter (58 of 70 nodules [83%] vs 24 of 70 nodules [34%], respectively; P , .001) and for size categories of at least 4 mm to smaller than 6 mm (71% vs ...
Citations
... Limitations of pulmonary MRI include motion artifacts, a small signal-to-noise ratio due to low proton density of the lung, and poor detection of ground-glass opacities (3,5,6). Though still underused, MRI offers advantages for the diagnosis of various conditions affecting the pulmonary parenchyma by providing information on both soft tissue morphology and functional properties (4,7): MRI is feasible and valuable in immunocompromised patients with pneumonia and suspected fungal infection (5,8), free-breathing ultrashort echo time sequences have been reported to be highly sensitive in detecting pulmonary nodules (9)(10)(11)(12) and promising results have been reported for diffusion-weighted imaging (DWI) including prediction of lung cancer invasiveness, determination of tumor type, and discrimination of malignant and benign nodules (13)(14)(15)(16)(17). For example, Koo et al. investigated multiparametric contrastenhanced lung MRI, including T1-, T2-, and DWI, and identified decisive parameters for characterization of pulmonary nodules and prediction of malignancy (18). ...
... In contrast, the T2w images are first converted into parametric maps, from which the values of the features within the VOI are retrieved in the next step. The parametric maps are generated by dissembling the original image into a grid of voxels (while voxel size can be modified in the software script to adjust [1][2][3][4][5][6][7][8][9][10][11][12]***** *, one patient underwent a CT-guided biopsy that was negative for malignant cells. No pathogen was identified by microbiological analysis of the biopsy sample or the BAL. ...
Background
Differentiating inflammatory from malignant lung lesions continues to be challenging in clinical routine, frequently requiring invasive methods like biopsy. Therefore, we aimed to investigate if inflammatory and malignant pulmonary lesions could be distinguished noninvasively using radiomics of apparent diffusion coefficient (ADC) maps and radiomic feature maps calculated from T2-weighted (T2w) 3 Tesla (3T) magnetic resonance imaging (MRI) of the lung.
Methods
Fifty-four patients with an unclear pulmonary lesion on computed tomography (CT) were prospectively included and examined by 3T MRI with T2w and diffusion-weighted sequences (b values of 50 and 800). ADC maps were calculated automatically. All patients underwent biopsy or bronchoalveolar lavage (BAL). Sixteen patients were excluded (e.g., motion artifacts), leaving 19 patients each with malignant and inflammatory pulmonary lesions. Target lesions were defined by biopsy or as the largest lesion (BAL-based pathogen detection), and two readers placed volumes of interest (VOIs) around the lesions on T2w images and ADC maps. One hundred and seven features were conventionally extracted from the ADC maps using PyRadiomics. T2w images were converted to 107 parametric feature maps per patient using a PyRadiomics-based, pretested software tool developed by our group. VOIs were copied from T2w images to T2 maps for feature quantification. Features were tested for significant differences using the Mann-Whitney U-test. Diagnostic performance was assessed using receiver operating characteristic (ROC) analysis and interreader agreement by intraclass correlation coefficients (ICCs).
Results
Fifty-eight features derived from ADC maps differed significantly between malignant and inflammatory pulmonary lesions, with areas under the curve (AUCs) >0.90 for 5 and >0.80 for 27 features, compared with 67 features from T2 maps (5 features with AUCs >0.80). ICCs were excellent throughout.
Conclusions
ADC and T2 maps differentiate inflammatory and malignant pulmonary lesions with outstanding (ADC) and excellent (T2w derived feature maps) diagnostic performance. MRI could thus guide the further diagnostic workup and a timely initiation of the appropriate therapy.
... The fourth part of the book is on clinical applications which include the use of data acquisitions, contrast mechanisms for visualization of abnormalities, and quantitation for determining their extent as well as showing and characterizing disease, particularly where it is not recognizable using qualitative image assessment. Changes in short-and ultrashort-T2 tissues can be evaluated with UTE type sequences, which may have critical applications in the musculoskeletal (e.g., osteoarthritis (OA) [57], osteoporosis (OP) [43], tendinopathy [58], hemophilia arthropathy (HA) [59], rotator cuff injury [60], temporomandibular disorders (TMD) [61], and spine degeneration [62]), as well as in the nervous (e.g., multiple sclerosis (MS) [63]), respiratory (e.g., lung diseases [64]), and gastrointestinal systems (e.g., liver iron overload [65]). MR-based attenuation correction and the silent feature of ZTE-type sequences are also discussed in this section. ...
The structure of the book is introduced. It includes four sections: signal acquisition, contrast mechanisms, quantitation, and clinical applications. Basic concepts within these categories are discussed and particular features of the imaging of short- and ultrashort-T2 tissues are outlined. The historical background and multidisciplinary nature of work in this field are emphasized.
... Pulmonary thin-section MRI with a UTE sequence can successfully detect lung nodules and can be used to distinguish nodule types [53]. A high sensitivity was shown, especially for the detection of small pulmonary nodules in a range of 4-8 mm [54]. UTE MRI was also employed in a lung cancer screening study and performed comparable to standard-or low-dose CT [55]. ...
... Morphological imaging [44,45] Fracture risk assessment [40,42,43] Osteoporosis and porosity evaluation [41] Lung Short TE imaging Imaging of minus pathologies Cystic fibrosis, [50][51][52] Lung cancer and lung nodule characterization [53][54][55] Pulmonary hypertension [2] Post-COVID [57,58] Brain Combination of inversion preparation and difference imaging ...
Background With the availability of MRI sequences with ultrashort echo times (UTE sequences), a signal can be gained from tissue, which was formerly only indirectly accessible. While already extensively employed in various research settings, the widespread transition of UTE imaging to clinical practice is just starting.
Methods Based on a systematic literature search as well as knowledge gained through annual participation in conferences dedicated to advances in MRI, this review aims to give a brief overview of technical considerations and challenges of UTE imaging and summarizes the major areas of application of UTE imaging.
Results UTE is already employed in clinical practice for structural lung imaging as well as the characterization of tissue composition and its alterations in selected musculoskeletal, cardiovascular, or neurodegenerative diseases. In specific contexts it can replace CT examinations with ionizing radiation and is especially attractive for pediatric patients and longitudinal monitoring of disease progression and treatment.
Conclusion UTE imaging provides an interesting and very valuable tool for various clinical purposes and promises a multitude of new insights into tissue properties. While some challenges remain, ongoing adoption in the clinical routine can be expected, as UTE approaches provide a new contrast and capture a signal in tissue formerly invisible on MR imaging.
Key Points:
... The recent development of the ultrashort echo time (UTE) MRI sequence enables lung MRI to be used in clinical practice (4)(5)(6)(7)(8)(9)(10)(11)(12)(13), but lung MRI is disadvantaged by a long scan time due to inefficient k-space coverage (4,6,14). To overcome this limitation, we applied threedimensional (3D) stack-of-spirals (spiral) acquisition to improve readout efficiency (15)(16)(17). ...
... For solid nodules, previous studies have reported detection rates of 60% to 90% for 5 to 8 mm diameter lesions and detection rates close to 100% for ≥8 mm lesions (22)(23)(24). Burris et al. reported a pulmonary nodule detection rate of 17% for nodules >2 mm but <4 mm in diameter and 83% for nodules ≥4 mm in diameter (83%) (11). Our results largely mirror these findings. ...
Background
Chest computed tomography (CT) is routinely performed to evaluate intrathoracic metastasis in patients with breast cancer, but radiation exposure and its potential carcinogenic risks are major drawbacks. Furthermore, pulmonary imaging by magnetic resonance imaging (MRI) is limited by low proton density, rapid signal decay, and sensitivity to respiratory and cardiac motions in lung tissue. Recently, a respiratory gating spiral three-dimensional (3D) ultrashort echo time (UTE) volume interpolated breath-hold examination (VIBE) sequence for lung MRI provides high spatial-resolution images with reasonable scan times. Our objective was to investigate the feasibility of chest spiral 3D UTE VIBE MRI to detect intrathoracic metastasis in breast cancer patients.
Methods
This retrospective study of a prospectively collected database was conducted between February and July 2019 after institutional review board approval. All participants provided informed consent for MRI scans. Ninety-three female patients with breast cancer were retrospectively enrolled and underwent preoperative breast MRI, including a chest spiral 3D UTE VIBE sequence. Two chest radiologists evaluated image qualities of intrapulmonary vessels and bronchial wall visibilities, the presence of pulmonary nodules, significant lymph nodes (LNs), and other lung abnormalities on spiral 3D UTE magnetic resonance (MR) images and compared them using chest CT as a reference standard.
Results
Intrapulmonary vessels and bronchial walls were visible up to sub-subsegmental and sub-subsegmental levels, respectively, on spiral 3D UTE MR images, and better than fair quality was obtained for artifact/noise and overall image quality for 95.7% and 98.9% of the patients, respectively. The overall detection rate for pulmonary nodules was 62.8% (59/94). Furthermore, 59 of the 81 solid nodules detected by CT were detected by spiral 3D UTE MRI (72.8%), and 31 of the 33 solid nodules (≥5 mm in diameter) detected by CT were identified by spiral 3D UTE MRI (93.9%). Significant LNs in the axillary area were similarly detected by spiral 3D UTE MRI and chest CT.
Conclusions
Preoperative breast MRI with a chest spiral 3D UTE sequence could be used to evaluate breast cancer and axillary LNs and intrathoracic metastasis simultaneously and offers a potential alternative to chest CT for breast cancer patients without additional radiation exposure.
... Despite its popularity for attenuation correction in PET, a number of studies have reported significant underestimation in PET SUV values in the brain, ranging between 4 and 17% when compared to CTAC, especially in the cortical regions [29,36,[59][60][61], and misclassification of voxels belongs to the ventricles, which were classified as air [62], and bone, which was classified as tissue [59,61,63,64]. In the lung, UTE performs well in terms of tissue detectability [65,66], but the sequence has not been extensively applied in the body due its long acquisition time [67]. It has also been demonstrated that the change in the magnetic field during the UTE sequence induces eddy currents that lead to degradation of the reconstructed images and misclassification to tissue boundaries [68]. ...
Despite being thirteen years since the installation of the first PET-MR system, the scanners constitute a very small proportion of the total hybrid PET systems installed. This is in stark contrast to the rapid expansion of the PET-CT scanner, which quickly established its importance in patient diagnosis within a similar timeframe. One of the main hurdles is the development of an accurate, reproducible and easy-to-use method for attenuation correction. Quantitative discrepancies in PET images between the manufacturer-provided MR methods and the more established CT- or transmission-based attenuation correction methods have led the scientific community in a continuous effort to develop a robust and accurate alternative. These can be divided into four broad categories: (i) MR-based, (ii) emission-based, (iii) atlas-based and the (iv) machine learning-based attenuation correction, which is rapidly gaining momentum. The first is based on segmenting the MR images in various tissues and allocating a predefined attenuation coefficient for each tissue. Emission-based attenuation correction methods aim in utilising the PET emission data by simultaneously reconstructing the radioactivity distribution and the attenuation image. Atlas-based attenuation correction methods aim to predict a CT or transmission image given an MR image of a new patient, by using databases containing CT or transmission images from the general population. Finally, in machine learning methods, a model that could predict the required image given the acquired MR or non-attenuation-corrected PET image is developed by exploiting the underlying features of the images. Deep learning methods are the dominant approach in this category. Compared to the more traditional machine learning, which uses structured data for building a model, deep learning makes direct use of the acquired images to identify underlying features. This up-to-date review goes through the literature of attenuation correction approaches in PET-MR after categorising them. The various approaches in each category are described and discussed. After exploring each category separately, a general overview is given of the current status and potential future approaches along with a comparison of the four outlined categories.
... Magnetic resonance imaging (MRI) is a non-ionizing modality for lung imaging. Advancements in MRI techniques have enabled the visualization of pathological pulmonary lesions and thus can be used as a complementary diagnostic tool for pulmonary disease (11)(12)(13)(14). Short-tau inversion recovery (STIR) T2-weighted turbo-spin echo (TSE) imaging was reported as a better strategy for pulmonary nodules detection (14,15). ...
... The previous studies were mainly focused on the pulmonary nodules/masses detection, while the assessment of nodules' morphological features by MRI is scarce (18, 24,26,27). UTE refers to a TE shorter than 200μs, which has delivered image quality close to that of CT and has been used for diagnosis of pulmonary diseases (11,22,24,(27)(28)(29)(30). The UTE morphological features agreed well with that of CT scanning (24,27). ...
Objective: MRI as a non-ionizing modality can be a complementary tool for nodules assessment. This study is aimed to evaluate the potential of the combined conventional and modified anatomical MRI sequences for differential diagnosis of invasive adenocarcinoma (IAC) and tuberculoma.
Materials and methods:Sixty-seven patients (median 54 years, range 18-82 years) with 82 noncalcified nodules (mean 19.56±6.85 mm, range 7-30 mm) underwent CT and MRI (T1WI-starVIBE, T1WI-VIBE, T2WI-TSE-fBLADE). Two radiologists independently assessed nodule dimensions and morphologic features (margin, morphology, lobulation, spiculation, cavity, air bronchogram, pleural indentation). Comparison of categorical variables was performed using Chi-square test. The inter-method agreement of morphologic features assessment by CT and MRI sequences were compared using Kappa test. Multivariate logistic regression analyses were applied to identify independent predictors to IAC. ROC analysis was performed to investigate the differential diagnosis capability.
Results: Thirty-eight IACs and 44 tuberculomas were identified. Readers 1 and 2 underestimated the nodules mean diameter with T1WI-starVIBE (T1WI-VIBE, T2WI-TSE-fBLADE) by 0.86±1.71 mm (1.19±2.06 mm, 0.15±1.96 mm) and 0.99±1.75 mm (1.27±2.04 mm, 0.19±1.91 mm). The inter-method agreements between MRI and CT were “fair” to “excellent” in the evaluation of morphological features except for spiculation (0.318≦Kappa≦0.895). Compared with the tuberculoma group, the IAC group was significant with unclear margin (T1WI-starVIBE, T1WI-VIBE), irregular morphology (CT, MRI), lobulation (CT, MRI), spiculation (T1WI-starVIBE, T2WI-TSE-fBLADE) and air bronchogram (CT, T1WI-starVIBE and T1WI-VIBE) (P﹤0.05). The AUC values for the logistic model by the combination of CT and MRI were 0.867/0.877 (reader 1/2: sensitivity 73.68%/76.32%, specificity 86.36%/86.36%) and were significantly higher than that by T1WI-starVIBE (P=0.002) and T1WI-TSE-fBLADE (P=0.027) (reader 1), as well as higher than that by CT (P=0.045) and T1WI-starVIBE (P=0.003) (reader 2).
Conclusion: The combined conventional and modified anatomical MRI sequences has diagnostic potential in distinguishing pulmonary IAC from tuberculoma.
... In a study of pulmonary nodules in a general oncology patient population, FDG-PET/MRI detected only 70.3% of all pulmonary nodules detected by FDG-PET/CT, though with higher detection rates for FDG-avid nodules (96%) and nodules > 5 mm in size (89%) [78]. Improved MRI techniques, such as UTE sequences, have somewhat addressed this limitation of PET/MRI by improving its sensitivity for small [4-8 mm] pulmonary nodules [79]. At this time, a dedicated chest CT is required in addition to whole-body PET/MRI for staging. ...
Positron emission tomography (PET) in the era of personalized medicine has a unique role in the management of oncological patients and offers several advantages over standard anatomical imaging. However, the role of molecular imaging in lower GI malignancies has historically been limited due to suboptimal anatomical evaluation on the accompanying CT, as well as significant physiological ¹⁸F-flurodeoxyglucose (FDG) uptake in the bowel. In the last decade, technological advancements have made whole-body FDG-PET/MRI a feasible alternative to PET/CT and MRI for lower GI malignancies. PET/MRI combines the advantages of molecular imaging with excellent soft tissue contrast resolution. Hence, it constitutes a unique opportunity to improve the imaging of these cancers. FDG-PET/MRI has a potential role in initial diagnosis, assessment of local treatment response, and evaluation for metastatic disease. In this article, we review the recent literature on FDG-PET/MRI for colorectal and anal cancers; provide an example whole-body FDG-PET/MRI protocol; highlight potential interpretive pitfalls; and provide recommendations on particular clinical scenarios in which FDG-PET/MRI is likely to be most beneficial for these cancer types.
Graphical abstract
... Similar results have been published for PET MRI, using a free breathing technique. Nodules less than 4 mm in size had a low rate of detectability, whereas larger nodules greater than 4 mm in size were readily detected [27,28]. ...
Functional imaging is playing an increasingly important role in pediatric radiology. Hybrid imaging techniques utilizing PET/CT (positron emission tomography/computed tomography), PET/MRI (positron emission tomography/magnetic resonance imaging), or SPECT/CT (single photon emission computed tomography/computed tomography) are now available in nearly every clinical practice. There are an increasing number of indications for the use of functional imaging, including oncologic and infectious indications, and it is essential to select and design the hybrid imaging protocol in order to optimize both the functional and anatomic components of the examination. Optimizing the protocol includes strategies for dose reduction, judicious use of contrast media and diagnostic quality imaging as appropriate, and for the greatest reduction in exposure to ionizing radiation, utilizing PET/MRI, whenever available. This review will provide an overview of hybrid imaging protocol considerations with a focus on oncologic and infectious indications.
... We applied XD-MBDL to reconstruction of the end-inspiratory phase of free breathing, retrospectively gated 3D pulmonary UTE acquisitions. End-inspiratory frame images are commonly used in CT for pulmonary nodule detection 28 and are also required for MRI ventilation mapping, 29 hence, our focus on this frame. Furthermore, the end-inspiratory frame is often the most undersampled respiratory phase and is thus challenging to reconstruct. ...
Purpose
To investigate motion compensated, self‐supervised, model based deep learning (MBDL) as a method to reconstruct free breathing, 3D pulmonary UTE acquisitions.
Theory and Methods
A self‐supervised eXtra dimension MBDL architecture (XD‐MBDL) was developed that combined respiratory states to reconstruct a single high‐quality 3D image. Non‐rigid motion fields were incorporated into this architecture by estimating motion fields from a lower resolution motion resolved (XD‐GRASP) reconstruction. Motion compensated XD‐MBDL was evaluated on lung UTE datasets with and without contrast and compared to constrained reconstructions and variants of self‐supervised MBDL that do not account for dynamic respiratory states or leverage motion correction.
Results
Images reconstructed using XD‐MBDL demonstrate improved image quality as measured by apparent SNR (aSNR), contrast to noise ratio (CNR), and visual assessment relative to self‐supervised MBDL approaches that do not account for dynamic respiratory states, XD‐GRASP and a recently proposed motion compensated iterative reconstruction strategy (iMoCo). Additionally, XD‐MBDL reduced reconstruction time relative to both XD‐GRASP and iMoCo.
Conclusion
A method was developed to allow self‐supervised MBDL to combine multiple respiratory states to reconstruct a single image. This method was combined with graphics processing unit (GPU)‐based image registration to further improve reconstruction quality. This approach showed promising results reconstructing a user‐selected respiratory phase from free breathing 3D pulmonary UTE acquisitions.
... MRI has also been used to quantify lung cancer (132,133). Conventional 1 H MRI is useful in differentiating inflammation from fibrosis (134,135). Alveolitis presents as high signal intensity on T2-weighted sequences and early enhancement on contrast-enhanced magnetic resonance (MR) sequences, whereas fibrotic lesions present as low signal with late AMERICAN THORACIC SOCIETY DOCUMENTS enhancement (136). ...
Multiple thoracic imaging modalities have been developed to link structure to function in the diagnosis and monitoring of lung disease. Volumetric computed tomography (CT) renders three-dimensional maps of lung structures and may be combined with positron emission tomography (PET) to obtain dynamic physiological data. Magnetic resonance imaging (MRI) using ultrashort-echo time (UTE) sequences has improved signal detection from lung parenchyma; contrast agents are used to deduce airway function, ventilation-perfusion-diffusion, and mechanics. Proton MRI can measure regional ventilation-perfusion ratio. Quantitative imaging (QI)-derived endpoints have been developed to identify structure-function phenotypes, including air-blood-tissue volume partition, bronchovascular remodeling, emphysema, fibrosis, and textural patterns indicating architectural alteration. Coregistered landmarks on paired images obtained at different lung volumes are used to infer airway caliber, air trapping, gas and blood transport, compliance, and deformation. This document summarizes fundamental "good practice" stereological principles in QI study design and analysis; evaluates technical capabilities and limitations of common imaging modalities; and assesses major QI endpoints regarding underlying assumptions and limitations, ability to detect and stratify heterogeneous, overlapping pathophysiology, and monitor disease progression and therapeutic response, correlated with and complementary to, functional indices. The goal is to promote unbiased quantification and interpretation of in vivo imaging data, compare metrics obtained using different QI modalities to ensure accurate and reproducible metric derivation, and avoid misrepresentation of inferred physiological processes. The role of imaging-based computational modeling in advancing these goals is emphasized. Fundamental principles outlined herein are critical for all forms of QI irrespective of acquisition modality or disease entity.
