McGee, Kiaran P.

Abstract Additive manufacturing (AM), commonly referred to as 3D printing, has been broadly used in the field of medical imaging and radiotherapy. In medical imaging applications, patient‐specific anatomic models have been used to aid in preoperative planning, patient communication, imaging technique assessment and optimization, and medical education. Within the field of radiation therapy, 3D printing finds significance in fabricating patient‐specific immobilization devices, custom bolus devices, anthropomorphic phantoms, brachytherapy devices, and electron therapy devices. Consistent with standard medical physics practices there is a need to establish quality assurance and quality control (QA/QC) protocols, tolerances, and testing frequencies. The diversity of 3D printing technologies and products poses challenges for medical adoption and thus requires guidelines in workflow standardization and establishment of testing procedures. This report is centered around providing comprehensive vendor‐neutral implementation guidelines and recommendations for QC and QA processes. The scope of this report encompasses diverse requirements within medical imaging and radiation therapy settings, serving both clinical and research purposes. Brief overview of 3D printing technology, common materials, and potential clinical applications is provided. QA team, infrastructure, measurement techniques and challenges are discussed. QA processes for each step in 3D printing are discussed in great detail and baseline and action limits are recommended. This report also touched on regulatory considerations. Key recommendations are summarized at the end of the report.

ABSTRACT Purpose Magnetic resonance elastography (MRE) of the heart has predominantly utilized breath‐held acquisitions with limited anatomic coverage. This work investigates the feasibility of 3D, free‐breathing cardiac MRE. Methods A 3D hybrid radial and EPI acquisition is utilized and combined with retrospective binning of k ‐space via physiologic monitoring, iterative reconstruction, MRE processing and inversions, and post‐processing specialized for cardiac imaging. Results Feasibility of free‐breathing stiffness mapping of the left ventricular myocardium was demonstrated in 11 participants. Myocardial stiffness estimates were obtained throughout the left ventricle for seven temporal states of the cardiac cycle. Stiffness estimates were presented in participant‐specific images, 17‐segment bullseye plots, and stiffness plots throughout the cardiac cycle. Results indicate increased stiffness in systolic states, with localized stiffness heterogeneity observed between participants, illustrating a key benefit of 3D cardiac MRE acquisition. Mean stiffness estimates obtained through 11 subjects were 4.44 ± 0.68 kPa (begin‐systole), 5.74 ± 0.94, 5.42 ± 0.87 (end‐systole), 3.84 ± 0.71 (begin‐diastole), 3.51 ± 0.74, 3.54 ± 0.52, and 3.55 ± 0.59 kPa (end‐diastole). Statistical testing indicated significant differences in stiffness across cardiac states with one‐way, repeated‐measures ANOVA ( p < 0.001) and Friedman's test ( p < 0.001). Experiments in scanning three participants with and without applied motion indicated that stiffness changes were more robustly detected in systole than in diastole, although patterns were subject‐specific. Comparison of free‐breathing stiffness measurements against reference breath‐held measurements yielded similar systolic stiffness estimates. Conclusions This study establishes the feasibility of free‐breathing, 3D cardiac MRE in healthy volunteers. With the current framework, stiffness estimation appears more robust in systole, and the reliability of diastolic estimates is limited and subject‐specific.

AbstractPurposeTo study if adaptive image receive (AIR) receiver coil elements can be configured into a 2D array with high (>45% by diameter) element‐to‐element overlap, allowing improved SNR at depth (0.7–1.5× element diameter) versus conventional (20%) overlap.MethodsAn anterior array composed of twenty 10‐cm diameter elements with 45% overlap arranged into a 4 × 5 grid and a similar 3 × 7 twenty‐one‐element posterior array were constructed. SNR and g‐factor were measured in a pelvic phantom using the new high‐density (HD) arrays (41 total elements) and compared to vendor AIR‐based arrays (30 total elements) with conventional overlap. T2‐weighted fast‐spin‐echo (T2SE) images acquired using both arrays were compared in 20 subjects. SNR was estimated in vivo. Results were compared blindly by three uroradiologists using a five‐point scale. Images using the HD arrays were also compared to a set of images acquired over a range of acceleration factors (R = 2.0, 2.5, 3.0) with the conventional arrays.ResultsSNR within the phantom was on average 15% higher for R = 1.0, 1.5, and 2.0 using the HD arrays. Across the 20 subjects SNR within the prostate was 11% higher and assessed radiologically as significantly higher (p < 0.001) for the HD versus conventional arrays. At all acceleration factors the new HD arrays outperformed the conventional arrays (p ≤ 0.01), allowing increased R for similar SNR.ConclusionAIR elements can be configured into 2D arrays with high (45%) element‐to‐element overlap, consistently providing increased SNR at depth versus arrays with conventional (20%) overlap. The SNR improvement allows increased acceleration in T2SE prostate MRI.

Ever since its introduction as a diagnostic imaging tool the potential of magnetic resonance imaging (MRI) in radiation therapy (RT) treatment simulation and planning has been recognized. Recent technical advances have addressed many of the impediments to use of this technology and as a result have resulted in rapid and growing adoption of MRI in RT. The purpose of this article is to provide a broad review of the multiple uses of MR in the RT treatment simulation and planning process, identify several of the most used clinical scenarios in which MR is integral to the simulation and planning process, highlight existing limitations and provide multiple unmet needs thereby highlighting opportunities for the diagnostic MR imaging community to contribute and collaborate with our oncology colleagues.Evidence Level5Technical EfficacyStage 5

AbstractObjectiveTo evaluate the magnetic resonance (MR) image artifact and image distortion associated with the two transcutaneous bone conduction implants currently available in the United States.Study DesignCadaveric study.MethodsTwo cadaveric head specimens (1 male, 1 female) were unilaterally implanted according to manufacturer guidelines and underwent MR imaging (General Electric and Siemens 1.5 T scanners) under the following device conditions: (1) no device, (2) Cochlear Osia with magnet and headwrap, (3) Cochlear Osia without magnet, and (4) MED‐EL Bonebridge with magnet. Maximum metal mitigation techniques were employed in all conditions, and identical sequences were obtained. Blinded image scoring (diagnostic vs nondiagnostic image) was performed by experienced neuroradiologists according to anatomical subsites.ResultsAll device conditions produced artifact and image distortion. The Osia with magnet produced diagnostic T1‐ and T2‐weighted images of the ipsilateral temporal bone, however, non‐echo planar imaging diffusion‐weighted imaging (DWI) was nondiagnostic. The Osia without magnet scanned on the Siemens MR imaging demonstrated the least amount of artifact and was the only condition that allowed for diagnostic imaging of the ipsilateral temporal bone on DWI. The Bonebridge produced a large area of artifact and distortion with the involvement of the ipsilateral and contralateral temporal bones.ConclusionIn summary, of the three device conditions (Osia with magnet, Osia without magnet, and Bonebridge), Osia without magnet offered the least amount of artifact and distortion and was the only condition in which diagnostic DWI was available for the middle ear and mastoid regions on the Siemens MR imaging scanner.

Purpose: To evaluate the performance of a new, highly flexible radiofrequency (RF) coil system for imaging patients undergoing MR simulation. Methods: Volumetric phantom and in vivo images were acquired with a commercially available and prototype RF coil set. Phantom evaluation was performed using a silicone-filled humanoid phantom of the head and shoulders. In vivo assessment was performed in five healthy and six patient subjects. Phantom data included T1-weighted volumetric imaging, while in vivo acquisitions included both T1- and T2-weighted volumetric imaging. Signal to noise ratio (SNR) and uniformity metrics were calculated in the phantom data, while SNR values were calculated in vivo. Statistical significance was tested by means of a non-parametric analysis of variance test. Results: At a threshold of p = 0.05, differences in measured SNR distributions within the entire phantom volume were statistically different in two of the three paired coil set comparisons. Differences in per slice average SNR between the two coil sets were all statistically significant, as well as differences in per slice image uniformity. For patients, SNRs within the entire imaging volume were statistically significantly different in four of the nine comparisons and seven of the nine comparisons performed on the per slice average SNR values. For healthy subjects, SNRs within the entire imaging volume were statistically significantly different in seven of the nine comparisons and eight of the nine comparisons when per slice average SNR was tested. Conclusions: Phantom and in vivo results demonstrate that image quality obtained from the novel flexible RF coil set was similar or improved over the conventional coil system. The results also demonstrate that image quality is impacted by the specific coil configurations used for imaging and should be matched appropriately to the anatomic site imaged to ensure optimal and reproducible image quality.

BackgroundLocalized regions of left–right image intensity asymmetry (LRIA) were incidentally observed on T2‐weighted (T2‐w) and T1‐weighted (T1‐w) diagnostic magnetic resonance imaging (MRI) images. Suspicion of herpes encephalitis resulted in unnecessary follow‐up imaging. A nonbiological imaging artifact that can lead to diagnostic uncertainty was identified.PurposeTo investigate whether systematic LRIA exist for a range of scanner models and to determine if LRIA can introduce diagnostic uncertainty.Study TypeA retrospective study using the Alzheimer's Disease Neuroimaging Initiative (ADNI) data base.SubjectsOne thousand seven hundred fifty‐three (median age: 72, males/females: 878/875) unique participants with longitudinal data were included.Field Strength3T.SequencesT1‐w three‐dimensional inversion‐recovery spoiled gradient‐echo (IR‐SPGR) or magnetization‐prepared rapid gradient‐echo (MP‐RAGE) and T2‐w fluid‐attenuated inversion recovery (FLAIR) long tau fast spin echo inversion recovery (LT‐FSE‐IR). Only General Electric, Philips, and Siemens' product sequences were used.AssessmentLRIA was calculated as the left–right percent difference with respect to the mean intensity from automated anatomical atlas segmented regions. Three neuroradiologists with 37 (**), 32 (**), and 3 (**) years of experience rated the clinical impact of 30 T2‐w three‐dimensional FLAIR exams with LRIA to determine the diagnostic uncertainty. Statistical comparisons between retrospective intensity normalized T1m and original T1‐w images were made.Statistical TestsFor each image type, a linear mixed effects model was fit using LRIA scores from all scanners, regions, and participants as the outcome and age and sex as predictors. Statistical significance was defined as having a P‐value <0.05.ResultsLRIA scores were significantly different from zero on most scanners. All clinicians were uncertain or recommended definite diagnostic follow‐up in 62.5% of cases with LRIA >10%. Individuals with acute brain pathology or focal neurologic deficits are not enrolled in ADNI; therefore, focal signal abnormalities were considered false positives.Data ConclusionLRIA is system specific, systematic, creates diagnostic uncertainty, and impacts IR‐SPGR, MP‐RAGE, and LT‐FSE‐IR product sequences.Level of Evidence: 2Technical Efficacy Stage: 3