Photon-Counting Detector CT for Musculoskeletal Imaging: A Clinical Perspective

Photon-counting detector (PCD) CT has emerged as a novel imaging modality that represents a fundamental shift in the way that CT systems detect x-rays. After pre-clinical and clinical investigations showed benefits of PCD CT for a range of imaging tasks, the U.S. FDA in 2021 approved the first commercial PCD CT system for clinical use. The technologic features of PCD CT are particularly well suited for musculo-skeletal imaging applications. Advantages of PCD CT compared with conventional energy-integrating detector (EID) CT include smaller detector pixels and excellent geometric dose efficiency that enable imaging of large joints and central skeletal anatomy at ultrahigh spatial resolution; advanced multienergy spectral postprocessing that allows quantification of gout deposits and generation of virtual noncalcium images for visualization of bone edema; improved metal artifact reduction for imaging of orthopedic implants; and higher CNR and suppression of electronic noise. Given substantially improved cortical and trabecular detail, PCD CT images more clearly depict skeletal abnormalities, including fractures, lytic lesions, and mineralized tumor matrix. The purpose of this article is to review, by use of clinical examples comparing EID CT and PCD CT, the technical features of PCD CT and their associated impact on musculoskeletal imaging applications.

Ultra-high-resolution imaging of the shoulder and pelvis using photon-counting-detector CT: a feasibility study in patients

OBJECTIVE

To evaluate ultra-high-resolution (UHR) imaging of large joints using an investigational photon-counting detector (PCD) CT.

MATERIALS AND METHODS

Patients undergoing clinical shoulder or pelvis energy-integrating-detector (EID) CT exam were scanned using the UHR mode of the PCD-CT system. Axial EID-CT images (1-mm sections) and PCD-CT images (0.6-mm sections) were reconstructed using Br62/Br64 and Br76 kernels, respectively. Two musculoskeletal radiologists rated visualization of anatomic structures using a 5-point Likert scale. Wilcoxon rank-sum test was used for statistical analysis of reader scores, and paired t-test was used for comparing bone CT numbers and image noise from PCD-CT and EID-CT.

RESULTS

Thirty-two patients (17 shoulders and 15 pelvis) were prospectively recruited for this feasibility study. Mean age for shoulder exams was 67.3 ± 15.5 years (11 females) and 47.2 ± 15.8 years (11 females) for pelvis exams. The mean volume CT dose index was lower on PCD-CT compared to that on EID-CT (shoulders: 18 mGy vs. 34 mGy, pelvis: 11.6 mGy vs. 16.7 mGy). PCD-CT was rated significantly better than EID-CT (p < 0.001) for anatomic-structure visualization. Trabecular delineation in shoulders (mean score = 4.24 ± 0.73) and femoroacetabular joint visualization in the pelvis (mean score = 3.67 ± 1.03) received the highest scores. PCD-CT demonstrated significant increase in bone CT number (p < 0.001) relative to EID-CT; no significant difference in image noise was found between PCD-CT and EID-CT.

CONCLUSION

The evaluated PCD-CT system provided improved visualization of osseous structures in the shoulders and pelvises at a 31-47% lower radiation dose compared to EID-CT.

KEY POINTS

• A full field-of-view PCD-CT with 0.151 mm × 0.176 mm detector pixel size (isocenter) facilitates bilateral, high-resolution imaging of shoulders and pelvis. • The evaluated investigational PCD-CT system was rated superior by two musculoskeletal radiologists for anatomic structure visualization in shoulders and pelvises despite a 31-47% lower radiation dose compared to EID-CT. • PCD-CT demonstrated significantly higher bone CT number compared to EID-CT, while no significant difference in image noise was observed between PCD-CT and EID-CT despite a 31-47% dose reduction on PCD-CT.

Clinical applications of photon counting detector CT

The X-ray detector is a fundamental component of a CT system that determines the image quality and dose efficiency. Until the approval of the first clinical photon-counting-detector (PCD) system in 2021, all clinical CT scanners used scintillating detectors, which do not capture information about individual photons in the two-step detection process. In contrast, PCDs use a one-step process whereby X-ray energy is converted directly into an electrical signal. This preserves information about individual photons such that the numbers of X-ray in different energy ranges can be counted. Primary advantages of PCDs include the absence of electronic noise, improved radiation dose efficiency, increased iodine signal and the ability to use lower doses of iodinated contrast material, and better spatial resolution. PCDs with more than one energy threshold can sort the detected photons into two or more energy bins, making energy-resolved information available for all acquisitions. This allows for material classification or quantitation tasks to be performed in conjunction with high spatial resolution, and in the case of dual-source CT, high pitch, or high temporal resolution acquisitions. Some of the most promising applications of PCD-CT involve imaging of anatomy where exquisite spatial resolution adds clinical value. These include imaging of the inner ear, bones, small blood vessels, heart, and lung. This review describes the clinical benefits observed to date and future directions for this technical advance in CT imaging. KEY POINTS: • Beneficial characteristics of photon-counting detectors include the absence of electronic noise, increased iodine signal-to-noise ratio, improved spatial resolution, and full-time multi-energy imaging. • Promising applications of PCD-CT involve imaging of anatomy where exquisite spatial resolution adds clinical value and applications requiring multi-energy data simultaneous with high spatial and/or temporal resolution. • Future applications of PCD-CT technology may include extremely high spatial resolution tasks, such as the detection of breast micro-calcifications, and quantitative imaging of native tissue types and novel contrast agents.

Seeing More with Less: Clinical Benefits of Photon-counting Detector CT

Photon-counting detector (PCD) CT is an emerging technology that has led to continued innovation and progress in diagnostic imaging after it was approved by the U.S. Food and Drug Administration for clinical use in September 2021. Conventional energy-integrating detector (EID) CT measures the total energy of x-rays by converting photons to visible light and subsequently using photodiodes to convert visible light to digital signals. In comparison, PCD CT directly records x-ray photons as electric signals, without intermediate conversion to visible light. The benefits of PCD CT systems include improved spatial resolution due to smaller detector pixels, higher iodine image contrast, increased geometric dose efficiency to allow high-resolution imaging, reduced radiation dose for all body parts, multienergy imaging capabilities, and reduced artifacts. To recognize these benefits, diagnostic applications of PCD CT in musculoskeletal, thoracic, neuroradiologic, cardiovascular, and abdominal imaging must be optimized and adapted for specific diagnostic tasks. The diagnostic benefits and clinical applications resulting from PCD CT in early studies have allowed improved visualization of key anatomic structures and radiologist confidence for some diagnostic tasks, which will continue as PCD CT evolves and clinical use and applications grow. ^©RSNA, 2023 Quiz questions for this article are available in the supplemental material. See the invited commentary by Ananthakrishnan in this issue.

Effects of Aspirin Therapy on Ultrasound-Guided Renal Allograft Biopsy Bleeding Complications

PURPOSE

To determine if patient aspirin exposure and timing affect bleeding risk after renal allograft biopsy.

MATERIALS AND METHODS

Review of 6,700 renal allograft biopsies (in 2,362 unique patients) was performed. Median patient age was 53.0 years [interquartile range 43.0, 62.0]; 56.2% of patients were male. Of biopsies, 4,706 (70.2%) were performed in patients with no aspirin exposure within 10 days of biopsy; 664 (9.9%), were performed within 8-10 days of aspirin exposure; 855 (12.8%), within 4-7 days; and 475 (7.1%), within 0-3 days. Follow-up to 3 months after the procedure was completed in all patients. Biopsies were categorized as protocol or indication; 19.7% were indication biopsies. Bleeding complications were graded based on SIR criteria. Logistic regression models examined the association between aspirin use and bleeding events.

RESULTS

Rate [95% confidence interval] of major bleeding complications was 0.24% [0.14, 0.39], and rate of any bleeding complication was 0.66% [0.46, 0.90]. Bleeding events were significantly associated with patients undergoing indication biopsies compared with protocol biopsies (odds ratio [OR] 2.27, P = .012). Patient factors associated with major bleeding complications in multivariate models included estimated glomerular filtration rate (OR 0.61, P = .016) and platelet count (OR 0.64, P = .033). Aspirin use was not significantly associated with increased risk of bleeding complication except for use of 325 mg of aspirin within 3 days of biopsy (any complication OR 3.87 [1.12, 13.4], P = .032; major complication OR 6.30 [1.27, 31.3], P = .024).

CONCLUSIONS

Renal allograft biopsy bleeding complications are very rare, particularly for protocol biopsies. Use of 325 mg of aspirin within 3 days of renal allograft biopsy was associated with increased bleeding complications.

Photon-counting Detector CT with Deep Learning Noise Reduction to Detect Multiple Myeloma

Background Photon-counting detector (PCD) CT and deep learning noise reduction may improve spatial resolution at lower radiation doses compared with energy-integrating detector (EID) CT. Purpose To demonstrate the diagnostic impact of improved spatial resolution in whole-body low-dose CT scans for viewing multiple myeloma by using PCD CT with deep learning denoising compared with conventional EID CT. Materials and Methods Between April and July 2021, adult participants who underwent a whole-body EID CT scan were prospectively enrolled and scanned with a PCD CT system in ultra-high-resolution mode at matched radiation dose (8 mSv for an average adult) at an academic medical center. EID CT and PCD CT images were reconstructed with Br44 and Br64 kernels at 2-mm section thickness. PCD CT images were also reconstructed with Br44 and Br76 kernels at 0.6-mm section thickness. The thinner PCD CT images were denoised by using a convolutional neural network. Image quality was objectively quantified in two phantoms and a randomly selected subset of participants (10 participants; median age, 63.5 years; five men). Two radiologists scored PCD CT images relative to EID CT by using a five-point Likert scale to detect findings reflecting multiple myeloma. The scoring for the matched reconstruction series was blinded to scanner type. Reader-averaged scores were tested with the null hypothesis of equivalent visualization between EID and PCD. Results Twenty-seven participants (median age, 68 years; IQR, 61-72 years; 16 men) were included. The blinded assessment of 2-mm images demonstrated improvement in viewing lytic lesions, intramedullary lesions, fatty metamorphosis, and pathologic fractures for PCD CT versus EID CT (P < .05 for all comparisons). The 0.6-mm PCD CT images with convolutional neural network denoising also demonstrated improvement in viewing all four pathologic abnormalities and detected one or more lytic lesions in 21 of 27 participants compared with the 2-mm EID CT images (P < .001). Conclusion Ultra-high-resolution photon-counting detector CT improved the visibility of multiple myeloma lesions relative to energy-integrating detector CT. © RSNA, 2022 Online supplemental material is available for this article.

Musculoskeletal CT Imaging: State-of-the-Art Advancements and Future Directions

CT is one of the most widely used modalities for musculoskeletal imaging. Recent advancements in the field include the introduction of four-dimensional CT, which captures a CT image during motion; cone-beam CT, which uses flat-panel detectors to capture the lower extremities in weight-bearing mode; and dual-energy CT, which operates at two different x-ray potentials to improve the contrast resolution to facilitate the assessment of tissue material compositions such as tophaceous gout deposits and bone marrow edema. Most recently, photon-counting CT (PCCT) has been introduced. PCCT is a technique that uses photon-counting detectors to produce an image with higher spatial and contrast resolution than conventional multidetector CT systems. In addition, postprocessing techniques such as three-dimensional printing and cinematic rendering have used CT data to improve the generation of both physical and digital anatomic models. Last, advancements in the application of artificial intelligence to CT imaging have enabled the automatic evaluation of musculoskeletal pathologies. In this review, the authors discuss the current state of the above CT technologies, their respective advantages and disadvantages, and their projected future directions for various musculoskeletal applications.

Role of imaging in multiple myeloma

With rapid advancements in the diagnosis and treatment of multiple myeloma (MM), imaging has become instrumental in detection of intramedullary and extramedullary disease, providing prognostic information, and assessing therapeutic efficacy. Whole-body low dose computed tomography (WBLDCT) has emerged as the study of choice to detect osteolytic bone disease. Positron emission tomography/computed tomography (PET/CT) combines functional and morphologic information to identify MM disease activity and assess treatment response. Magnetic resonance imaging (MRI) has excellent soft-tissue contrast and is the modality of choice for bone marrow evaluation. This review focuses on the imaging modalities available for MM patient management, highlighting advantages, disadvantages, and applications of each.

Benefits of iterative metal artifact reduction and dual-energy CT towards mitigating artifact in the setting of total shoulder prostheses

OBJECTIVE

To determine the utility of iterative metal artifact reduction and 130 keV dual-energy virtual monoenergetic images to improve bone and soft tissue visualization in CT scans affected by metal artifacts.

MATERIAL AND METHODS

Thirteen females and 6 males with a history of total shoulder prosthesis who underwent dual-energy shoulder CT were included. Four sets of images were reconstructed for each patient: (1) original polychromatic kV images reconstructed with weighted filtered back projection; (2) polychromatic kV images with iterative metal artifact reduction; (3) 130 keV dual-energy virtual monoenergetic; (4) combined iterative metal artifact reduction and 130 keV dual-energy virtual monoenergetic. Three readers blindly reviewed all image sets and graded the extent of artifact and image quality.

RESULTS

Mean artifact score and median overall image quality score were better in 130 keV dual-energy virtual monoenergetic with iterative metal artifact reduction compared with those in original polychromatic kV images (3.02 vs 4.28, P < 0.001 and 3.00 vs 4.33, P < 0.001, respectively). The median difference in CT numbers between regions affected by artifacts and normal regions was lowest in 130 keV dual-energy virtual monoenergetic with iterative metal artifact reduction compared with that in original polychromatic kV images (72.28 vs 252.08, P < 0.001 for bony regions and 15.09 vs 324.38, P < 0.001 for soft tissue).

CONCLUSION

In patients with metal artifacts due to shoulder replacement surgery, the use of dual-energy monoenergetic images and iterative metal artifact reduction reconstruction significantly improves both subjective and objective indicators of image quality.

A deep learning algorithm for detecting lytic bone lesions of multiple myeloma on CT

BACKGROUND

Whole-body low-dose CT is the recommended initial imaging modality to evaluate bone destruction as a result of multiple myeloma. Accurate interpretation of these scans to detect small lytic bone lesions is time intensive. A functional deep learning) algorithm to detect lytic lesions on CTs could improve the value of these CTs for myeloma imaging. Our objectives were to develop a DL algorithm and determine its performance at detecting lytic lesions of multiple myeloma.

METHODS

Axial slices (2-mm section thickness) from whole-body low-dose CT scans of subjects with biochemically confirmed plasma cell dyscrasias were included in the study. Data were split into train and test sets at the patient level targeting a 90%/10% split. Two musculoskeletal radiologists annotated lytic lesions on the images with bounding boxes. Subsequently, we developed a two-step deep learning model comprising bone segmentation followed by lesion detection. Unet and "You Look Only Once" (YOLO) models were used as bone segmentation and lesion detection algorithms, respectively. Diagnostic performance was determined using the area under the receiver operating characteristic curve (AUROC).

RESULTS

Forty whole-body low-dose CTs from 40 subjects yielded 2193 image slices. A total of 5640 lytic lesions were annotated. The two-step model achieved a sensitivity of 91.6% and a specificity of 84.6%. Lesion detection AUROC was 90.4%.

CONCLUSION

We developed a deep learning model that detects lytic bone lesions of multiple myeloma on whole-body low-dose CTs with high performance. External validation is required prior to widespread adoption in clinical practice.

Evolving Role of Dual-Energy CT in the Clinical Workup of Gout: A Retrospective Study

BACKGROUND. Dual-energy CT (DECT) allows noninvasive detection of monosodium urate (MSU) crystal deposits and has become incorporated into the routine clinical evaluation for gout at many institutions over the past decade. OBJECTIVE. The purpose of this study was to compare two time periods over the past decade in terms of radiologists' interpretations of DECT examinations performed for the evaluation of gout and subsequent clinical actions. METHODS. This retrospective study included 100 consecutive adult patients who underwent DECT to evaluate for gout in each of two periods (one beginning in March 2013 and one beginning in September 2019). Examinations performed in 2013 were conducted using a second-generation DECT scanner (80 kV [tube A] and 140 kV [tube B] with a 0.4-mm tin filter), and those performed in 2019 were conducted using a third-generation DECT scanner (80 kV [tube A] and 150 kV [tube B] with a 0.6-mm tin filter) that provides improved spectral separation. Original DECT reports were classified as positive, negative, or equivocal for MSU crystals indicative of gout. Joint aspirations occurring after the DECT examinations were recorded on the basis of findings from medical record review. A single radiologist performed a post hoc retrospective blinded image review, classifying examinations as positive, negative, or equivocal. RESULTS. In 2013, 44.0% of DECT examinations were interpreted as positive, 23.0% as negative, and 33.0% as equivocal; in 2019, 37.0% were interpreted as positive, 47.0% as negative, and 16.0% as equivocal (p < .001). The frequency of joint aspiration after DECT was 14.0% in 2013 versus 2.0% in 2019 (p = .002), and that after DECT examinations with negative interpretations was 17.4% in 2013 versus 2.1% in 2019 (p = .02). In post hoc assessment by a single radiologist, the distribution of interpretations in 2013 was positive in 49.0%, negative in 22.0%, and equivocal in 29.0%, and in 2019 it was positive in 39.0%, negative in 50.0%, and equivocal in 11.0% (p < .001). CONCLUSION. When DECT examinations performed for gout in 2013 and 2019 were compared, the frequency of equivocal interpretations was significantly lower in 2019, possibly in relation to interval technologic improvements. Negative examinations were less frequently followed by joint aspirations in 2019, possibly reflecting increasing clinical acceptance of the DECT results. CLINICAL IMPACT. The findings indicate an evolving role for DECT in the evaluation of gout after an institution's routine adoption of the technology for this purpose.

Quantifying Uncertainty in Deep Learning of Radiologic Images

In recent years, deep learning (DL) has shown impressive performance in radiologic image analysis. However, for a DL model to be useful in a real-world setting, its confidence in a prediction must also be known. Each DL model's output has an estimated probability, and these estimated probabilities are not always reliable. Uncertainty represents the trustworthiness (validity) of estimated probabilities. The higher the uncertainty, the lower the validity. Uncertainty quantification (UQ) methods determine the uncertainty level of each prediction. Predictions made without UQ methods are generally not trustworthy. By implementing UQ in medical DL models, users can be alerted when a model does not have enough information to make a confident decision. Consequently, a medical expert could reevaluate the uncertain cases, which would eventually lead to gaining more trust when using a model. This review focuses on recent trends using UQ methods in DL radiologic image analysis within a conceptual framework. Also discussed in this review are potential applications, challenges, and future directions of UQ in DL radiologic image analysis.

Sonographic evaluation of the lateral femoral cutaneous nerve in meralgia paresthetica

OBJECTIVE

Identify sonographic features of the lateral femoral cutaneous nerve (LFCN) in meralgia paresthetica (MP) and report therapeutic outcomes in sonographically confirmed cases.

MATERIALS AND METHODS

Retrospective review of 50 patients with clinically suspected MP and 20 controls. Ultrasounds were reviewed for characteristics of the LFCN and compared between groups. When available, MRIs were reviewed. In cases of sonographically pathologic LFCN, subsequent therapeutic interventions were recorded.

RESULTS

Thirty-five of the suspected MP cases (70%) had ultrasound findings suggestive of MP, 10 (20%) were negative, and in 5 (10%) the LFCN was not seen. Sonographic findings in positive cases included nerve enlargement in all cases (mean cross-sectional area 9 mm² (standard deviation (SD) ± 5.59) versus 4 mm² (SD ± 2.31) and 3 mm² (SD ± 2.31) in negative cases and normal controls, respectively; p < 0.01), nerve hypoechogenicity (30 of 35 cases, 86%), and focal lesion (7 of 35 cases, 20%). Sixteen ultrasounds positive for MP had MRIs with only 4 (25%) reporting a concordant LFCN abnormality (enlargement or T2 hyperintensity). Twenty-five of the 35 (71%) patients with positive sonographic findings for MP had a US-guided LFCN block (local anesthetic ± corticosteroid), with 24 of 25 (96%) patients reporting immediate symptomatic improvement. Eighteen of 35 (51%) underwent LFCN neurectomy or neurolysis, all of whom experienced symptomatic improvement.

CONCLUSION

Ultrasound is a useful modality for LFCN assessment in clinically suspected MP and is more sensitive for abnormalities than MRI. Nearly all patients who received perineural analgesia and/or neurectomy or neurolysis had symptomatic improvement.

CT-derived sarcopenia should not preclude surgical stabilization of traumatic rib fractures

BACKGROUND

Rib fractures are associated with considerable morbidity and mortality. Surgical stabilization of rib fractures (SSRF) can be performed to mitigate complications. Sarcopenia is in general known to be associated with poor clinical outcomes. We investigated if sarcopenia impacted number of days of mechanical ventilation, intensive care unit (ICU) stay, and total hospital stay in patients who underwent SSRF.

METHODS

A retrospective single institutional review was performed including patients who underwent SSRF (2009-2017). Skeletal muscle index (SMI) was semiautomatically calculated at the L3 spinal level on computed tomography (CT) images and normalized by patient height. Sarcopenia was defined as SMI < 55 cm²/m² in males and < 39 cm²/m² in females. Demographics, operative details, and postoperative outcomes were reviewed. Univariate and multivariate analyses were performed.

RESULTS

Of 238 patients, 88 (36.9%) had sarcopenia. There was no significant difference in number of days of mechanical ventilation (2.8 ± 4.9 versus 3.1 ± 4.3, p = 0.304), ICU stay (5.9 ± 6.5 versus 4.9 ± 5.7 days, p = 0.146), or total hospital stay (13.3 ± 7.2 versus 12.9 ± 8.2 days, p = 0.183) between sarcopenic and nonsarcopenic patients. Sarcopenic patients demonstrated increased modified frailty index scores (1.5 ± 1.1 versus 0.9 ± 0.9, p < 0.001) compared to nonsarcopenic patients.

CONCLUSIONS

For patients who underwent SSRF for rib fractures, sarcopenia did not increase the number of days of mechanical ventilation, ICU stay, or total hospital stay. Sarcopenia should not preclude the utilization of SSRF in these patients.

BRAF inhibitor-induced panniculitis in patients treated for stage IV metastatic melanoma: a case series

BRAF and MEK inhibitor combination therapy is the standard treatment for patients with BRAF V600E mutant metastatic melanoma. Neutrophilic panniculitis is a known rare complication of BRAF inhibitor therapy and can act as a potential mimic of melanoma metastases on ¹⁸F-FDG PET/CT. In this case series, we present three cases of BRAF inhibitor-induced panniculitis in patients being treated for BRAF-mutant metastatic melanoma and emphasize the use of ultrasound to differentiate between panniculitis lesions, which are typically ill-defined echogenic masses and subcutaneous soft tissue melanoma metastases, which present as hypoechoic vascular masses.

Staging of breast cancer in the breast and regional lymph nodes using contrast-enhanced photon-counting detector CT: accuracy and potential impact on patient management

OBJECTIVES

To describe the feasibility and evaluate the performance of multiphasic photon-counting detector (PCD) CT for detecting breast cancer and nodal metastases with correlative dynamic breast MRI and digital mammography as the reference standard.

METHODS

Adult females with biopsy-proven breast cancer undergoing staging breast MRI were prospectively recruited to undergo a multiphasic PCD-CT using a 3-phase protocol: a non-contrast ultra-high-resolution (UHR) scan and 2 intravenous contrast-enhanced scans with 50 and 180 s delay. Three breast radiologists compared CT characteristics of the index malignancy, regional lymphadenopathy, and extramammary findings to MRI.

RESULTS

Thirteen patients underwent both an MRI and PCD-CT (mean age: 53 years, range: 36-75 years). Eleven of thirteen cases demonstrated suspicious mass or non-mass enhancement on PCD-CT when compared to MRI. All cases with metastatic lymphadenopathy (3/3 cases) demonstrated early avid enhancement similar to the index malignancy. All cases with multifocal or multicentric disease on MRI were also identified on PCD-CT (3/3 cases), including a 4 mm suspicious satellite lesion. Four of five patients with residual suspicious post-biopsy calcifications on mammograms were detected on the UHR PCD-CT scan. Owing to increased field-of-view at PCD-CT, a 5 mm thoracic vertebral metastasis was identified at PCD-CT and not with the breast MRI.

CONCLUSIONS

A 3-phase PCD-CT scan protocol shows initial promising results in characterizing breast cancer and regional lymphadenopathy similar to MRI and detects microcalcifications in 80% of cases.

ADVANCES IN KNOWLEDGE

UHR and spectral capabilities of PCD-CT may allow for comprehensive characterization of breast cancer and may represent an alternative to breast MRI in select cases.

Photon-counting Detector CT with Deep Learning Noise Reduction to Detect Multiple Myeloma

Background Photon-counting detector (PCD) CT and deep learning noise reduction may improve spatial resolution at lower radiation doses compared with energy-integrating detector (EID) CT. Purpose To demonstrate the diagnostic impact of improved spatial resolution in whole-body low-dose CT scans for viewing multiple myeloma by using PCD CT with deep learning denoising compared with conventional EID CT. Materials and Methods Between April and July 2021, adult participants who underwent a whole-body EID CT scan were prospectively enrolled and scanned with a PCD CT system in ultra-high-resolution mode at matched radiation dose (8 mSv for an average adult) at an academic medical center. EID CT and PCD CT images were reconstructed with Br44 and Br64 kernels at 2-mm section thickness. PCD CT images were also reconstructed with Br44 and Br76 kernels at 0.6-mm section thickness. The thinner PCD CT images were denoised by using a convolutional neural network. Image quality was objectively quantified in two phantoms and a randomly selected subset of participants (10 participants; median age, 63.5 years; five men). Two radiologists scored PCD CT images relative to EID CT by using a five-point Likert scale to detect findings reflecting multiple myeloma. The scoring for the matched reconstruction series was blinded to scanner type. Reader-averaged scores were tested with the null hypothesis of equivalent visualization between EID and PCD. Results Twenty-seven participants (median age, 68 years; IQR, 61-72 years; 16 men) were included. The blinded assessment of 2-mm images demonstrated improvement in viewing lytic lesions, intramedullary lesions, fatty metamorphosis, and pathologic fractures for PCD CT versus EID CT (P < .05 for all comparisons). The 0.6-mm PCD CT images with convolutional neural network denoising also demonstrated improvement in viewing all four pathologic abnormalities and detected one or more lytic lesions in 21 of 27 participants compared with the 2-mm EID CT images (P < .001). Conclusion Ultra-high-resolution photon-counting detector CT improved the visibility of multiple myeloma lesions relative to energy-integrating detector CT. © RSNA, 2022 Online supplemental material is available for this article.

Preoperative sonographic ulnar nerve mapping in the postoperative elbow

OBJECTIVE

To describe the technique of sonographic ulnar nerve mapping in the postoperative elbow for surgical planning.

MATERIALS AND METHODS

A retrospective review of a surgical databank identified 24 patients, all aged 18 years and older with a history of orthopedic elbow surgery, who were referred for preoperative sonographic mapping of the ulnar nerve prior to subsequent surgery. All cases were reviewed for patient demographics, clinical presentation, prior surgical interventions, and ultrasound technique. Charts were reviewed for intraoperative and postoperative outcomes, including nerve injury.

RESULTS

The cohort included 12 males and 12 females with a mean age of 51 years (range 22-68 years) and a mean BMI of 29 (range 20-48). Preoperative sonographic ulnar nerve mapping occurred following various elbow surgeries including ulnar nerve transposition to assess nerve location prior to subsequent elbow surgery. Of the 24 patients with preoperative sonographic ulnar nerve mapping, subsequent surgery was performed arthroscopically in 14 and open in 10 cases. In 11 of the 24 cases, there was specific mention of a modified approach to joint access which was guided by the ulnar nerve map. There were no perioperative ulnar nerve-related complications, such as nerve transection.

CONCLUSION

Preoperative mapping can facilitate planning of surgical access and ulnar nerve dissection. Sonographic mapping of the ulnar nerve reduces the potential uncertainty of nerve palpation in a complex postoperative elbow following ulnar nerve transposition. This technique may mitigate the risk of ulnar nerve injury.

Whole-body low-dose computed tomography in patients with newly diagnosed multiple myeloma predicts cytogenetic risk: a deep learning radiogenomics study

OBJECTIVE

To develop a whole-body low-dose CT (WBLDCT) deep learning model and determine its accuracy in predicting the presence of cytogenetic abnormalities in multiple myeloma (MM).

MATERIALS AND METHODS

WBLDCTs of MM patients performed within a year of diagnosis were included. Cytogenetic assessments of clonal plasma cells via fluorescent in situ hybridization (FISH) were used to risk-stratify patients as high-risk (HR) or standard-risk (SR). Presence of any of del(17p), t(14;16), t(4;14), and t(14;20) on FISH was defined as HR. The dataset was evenly divided into five groups (folds) at the individual patient level for model training. Mean and standard deviation (SD) of the area under the receiver operating curve (AUROC) across the folds were recorded.

RESULTS

One hundred fifty-one patients with MM were included in the study. The model performed best for t(4;14), mean (SD) AUROC of 0.874 (0.073). The lowest AUROC was observed for trisomies: AUROC of 0.717 (0.058). Two- and 5-year survival rates for HR cytogenetics were 87% and 71%, respectively, compared to 91% and 79% for SR cytogenetics. Survival predictions by the WBLDCT deep learning model revealed 2- and 5-year survival rates for patients with HR cytogenetics as 87% and 71%, respectively, compared to 92% and 81% for SR cytogenetics.

CONCLUSION

A deep learning model trained on WBLDCT scans predicted the presence of cytogenetic abnormalities used for risk stratification in MM. Assessment of the model's performance revealed good to excellent classification of the various cytogenetic abnormalities.

Multimodality evaluation of transfascial muscle and other soft tissue herniations of the extremities

This review illustrates the multimodality assessment of transfascial muscle and other soft tissue herniations of the extremities. Transfascial herniations of the extremities can develop from congenital or acquired disruptions of the deep fascia, resulting in herniation of the underlying muscle, nerve, or soft tissue tumor into the subcutaneous tissues. While most patients present with a painless subcutaneous nodule that may change in size with muscle activation, some may experience focal or diffuse extremity symptoms such as pain and paresthesias. Although the diagnosis may be clinically suspected, radiologic evaluation is useful for definitive diagnosis and characterization. Ultrasound is the preferred modality for initial workup through a focused and dynamic examination. Magnetic resonance imaging can be utilized for equivocal, complicated, and preoperative cases. Computed tomography is less useful in the evaluation of transfascial herniations in the extremities due to similarities in the attenuation between muscle and fascia, which can decrease the conspicuity of small defects.

Deep-learning for automated detection of MSU deposits on DECT: evaluating impact on efficiency and reader confidence

Introduction

Dual-energy CT (DECT) is a non-invasive way to determine the presence of monosodium urate (MSU) crystals in the workup of gout. Color-coding distinguishes MSU from calcium following material decomposition and post-processing. Manually identifying these foci (most commonly labeled green) is tedious, and an automated detection system could streamline the process. This study aims to evaluate the impact of a deep-learning (DL) algorithm developed for detecting green pixelations on DECT on reader time, accuracy, and confidence.

Methods

We collected a sample of positive and negative DECTs, reviewed twice-once with and once without the DL tool-with a 2-week washout period. An attending musculoskeletal radiologist and a fellow separately reviewed the cases, simulating clinical workflow. Metrics such as time taken, confidence in diagnosis, and the tool's helpfulness were recorded and statistically analyzed.

Results

We included thirty DECTs from different patients. The DL tool significantly reduced the reading time for the trainee radiologist (p = 0.02), but not for the attending radiologist (p = 0.15). Diagnostic confidence remained unchanged for both (p = 0.45). However, the DL model identified tiny MSU deposits that led to a change in diagnosis in two cases for the in-training radiologist and one case for the attending radiologist. In 3/3 of these cases, the diagnosis was correct when using DL.

Conclusions

The implementation of the developed DL model slightly reduced reading time for our less experienced reader and led to improved diagnostic accuracy. There was no statistically significant difference in diagnostic confidence when studies were interpreted without and with the DL model.

Development of a deep learning model for the automated detection of green pixels indicative of gout on dual energy CT scan

Background

Dual-energy CT (DECT) is a non-invasive way to determine the presence of monosodium urate (MSU) crystals in the workup of gout. Color-coding distinguishes MSU from calcium following material decomposition and post-processing. Most software labels MSU as green and calcium as blue. There are limitations in the current image processing methods of segmenting green-encoded pixels. Additionally, identifying green foci is tedious, and automated detection would improve workflow. This study aimed to determine the optimal deep learning (DL) algorithm for segmenting green-encoded pixels of MSU crystals on DECTs.

Methods

DECT images of positive and negative gout cases were retrospectively collected. The dataset was split into train (N = 28) and held-out test (N = 30) sets. To perform cross-validation, the train set was split into seven folds. The images were presented to two musculoskeletal radiologists, who independently identified green-encoded voxels. Two 3D Unet-based DL models, Segresnet and SwinUNETR, were trained, and the Dice similarity coefficient (DSC), sensitivity, and specificity were reported as the segmentation metrics.

Results

Segresnet showed superior performance, achieving a DSC of 0.9999 for the background pixels, 0.7868 for the green pixels, and an average DSC of 0.8934 for both types of pixels, respectively. According to the post-processed results, the Segresnet reached voxel-level sensitivity and specificity of 98.72 % and 99.98 %, respectively.

Conclusion

In this study, we compared two DL-based segmentation approaches for detecting MSU deposits in a DECT dataset. The Segresnet resulted in superior performance metrics. The developed algorithm provides a potential fast, consistent, highly sensitive and specific computer-aided diagnosis tool. Ultimately, such an algorithm could be used by radiologists to streamline DECT workflow and improve accuracy in the detection of gout.